Spatiotemporal Dynamics and Environmental Predictors of Confirmed Uncomplicated Malaria in Bayelsa State, Nigeria (2017-2024)

Spatiotemporal Dynamics and Environmental Predictors of Confirmed Uncomplicated Malaria in Bayelsa State, Nigeria (2017-2024) | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Spatiotemporal Dynamics and Environmental Predictors of Confirmed Uncomplicated Malaria in Bayelsa State, Nigeria (2017-2024) James Olaoye Oyeleye, Olalekan John Taiwo, Okpachi Abbah, Ganiyat Eshikhena, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8165329/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 15 You are reading this latest preprint version Abstract Background Bayelsa State, Nigeria with a current prevalence rate of 17% based on the 2021 Nigeria Malaria Indicator Survey. Previous studies on malaria incidence in Bayelsa State, Nigeria, have the absence of longitudinal studies, low survey coverage, limited integration of environmental factors into analyses of local government area (LGA)-level malaria patterns, and few or no comparisons between upland and riverine settings. This study quantifies temporal trends and spatial heterogeneity in confirmed uncomplicated malaria across eight ( 8 ) LGAs, compares malaria burdens between upland and riverine LGAs, and identifies and ranks environmental and infrastructural predictors of LGA-level malaria via complementary statistical approaches. Methods Data on confirmed uncomplicated malaria cases from 2017–2024 for all LGAs in Bayelsa were downloaded from the DHIS2 database. Administrative data and the number of healthcare facilities were downloaded from Grid 3.org, whereas environmental data were downloaded from the Google Earth Engine website. Descriptive statistics, univariate Moran’s I, ANOVA, correlation, and exploratory and ordinary least squares regression were the analytical methods used. Results The incidence of confirmed uncomplicated malaria in Bayelsa State rose from 10,745 cases in 2017 to 65,149 cases in 2024, a 6.06-fold increase corresponding to a compound annual growth rate of 29.4%. Yenagoa consistently accounted for the largest share, ranging from 28.3% in 2018 to 49.2% in 2019 and 39.5% in 2024. The incidence was generally greater in upland LGAs than in riverine LGAs, with significantly greater dispersion in upland settings (F(1,62) = 4.904, p = 0.030). The global Moran’s I coefficients were weakly negative across years, suggesting spatial dispersion rather than clustering. Regression analysis revealed Visible Infrared Imaging Radiometer Suite (VIIRS) (t = 25.86), elevation (t = 18.89), and NDVI (|t|=12.88) as the strongest predictors, supported by built-up land (r = 0.954, p < 0.001), roads (r = 0.912, p = 0.001), cropland (r = 0.711, p = 0.024), and healthcare facilities (r = 0.808, p = 0.008). Conclusions The findings show that settlement expansion and environmental conditions strongly shape malaria dynamics, often outweighing broad climate and vegetation measures in thermally optimal, wet areas. Priorities for reducing the malaria burden include peri-urban environmental management, improved housing, and strengthened unbiased surveillance. Cluster environment epidemiology endemic diseases malaria incidence Nigeria Plasmodium falciparum spatial analysis Figures Figure 1 1.0 Introduction Malaria remains a leading cause of morbidity in West Africa, yet its intensity varies sharply over space and time, as climate, hydrology, settlement form, housing, and mobility shape receptivity and exposure ( 1 ). Across tropical Africa, transmission peaks in warm, humid landscapes where efficient vectors such as Anopheles gambiae sensu stricto and Anopheles coluzzii exploit small, sunlit freshwater habitats, whereas brackish tidal systems favor more localized Anopheles melas ( 2 ). Temperatures near 25–27°C maximize transmission potential, but local water management and land use often dominate at subnational scales ( 3 , 4 ). Urbanization has a dual effect: improved housing can suppress risk, yet rapid peri-urban growth, roadworks, and construction create prolific larval habitats close to households ( 5 , 6 ). Proxies of settlement intensity and connectivity, including nighttime lights and built-up indices, consistently align with where people live, move, and access care ( 7 – 9 ). Against this backdrop, Nigeria continues to shoulder a large share of the global Plasmodium falciparum burden, with subnational heterogeneity central to control ( 10 , 11 ). Bayelsa State, straddling upland freshwater and riverine/mangrove ecologies in the Niger Delta, is an archetypal setting for examining how the environment and urban growth structure of malaria are detected through routine surveillance. Given Bayelsa’s mix of freshwater and mangrove ecologies, remote sensing and geospatial epidemiology play critical roles in the identification of spatial heterogeneity and environmental predictors of malaria. Vegetation indices (e.g., the normalized difference vegetation index (NDVI)) often have inverse relationships with malaria, where a dense canopy reduces the number of sunlit larval habitats, whereas hydrologic indicators (precipitation, soil moisture, flooded area) and river proximity capture habitat availability and persistence, frequently with temporal lags ( 4 , 12 ). Land use also matters, as peri-urban/urban agriculture, drainage ditches, and construction sites consistently produce productive larval habitats near households ( 13 – 15 ). In deltaic settings, slight increases in inland elevation can signal fresher waters away from saline influences, favoring Anopheles. gambiae s.l. ( 2 , 16 ). Anthropogenic structure and connectivity further organize risk and the visibility of cases ( 5 , 17 ). Urbanization has a dual effect since improved housing can suppress transmission, yet rapid peri-urban growth and poor drainage create focal hotspots ( 5 , 6 ). Nighttime lights and built-up indices are validated proxies for settlement density and economic activity, which are correlated with where people live and move ( 7 , 8 ). Road networks both generate larval habitats (e.g., puddling, borrowing pits) and facilitate parasite flow via human mobility ( 9 , 18 ). Access to care also shapes the measured incidence, as areas with more reachable facilities detect a larger share of infections, thereby inflating routine counts relative to underserved areas ( 11 , 19 ). Despite these advances, critical gaps remain for Bayelsa State as peer-reviewed studies providing longitudinal, state-specific spatiotemporal analyses of confirmed uncomplicated malaria are scarce; environmental drivers have been underexamined; and rigorous comparisons between upland and riverine local government areas (LGAs) are largely absent. Moreover, spatiotemporal analyses leveraging routine DHIS2 data are scarce, and rigorous comparisons between upland and riverine LGAs have seldom been attempted. Moreover, few studies leverage DHIS2 facility data for Bayelsa while explicitly integrating environmental predictors and connectivity metrics, despite growing evidence that such integration strengthens the interpretation of routine surveillance ( 4 , 19 ). Addressing these gaps, this study examines how confirmed uncomplicated malaria varied across space and time in Bayelsa State from 2017–2024 and which environmental predictors best explain the observed pattern. We integrate health facility reports with remotely sensed and infrastructural covariates that capture settlement intensity (nighttime lights, built-up extent), greenness (NDVI), hydroclimatic and geomorphic context (precipitation, soil moisture, rivers, elevation), agriculture (cropland), and connectivity (roads). The specific objectives are to (a) quantify temporal trends and spatial heterogeneity in confirmed uncomplicated malaria across LGAs; (b) compare malaria burdens between upland and riverine groups; and (c) identify and rank environmental and infrastructural predictors of LGA-level malaria via complementary statistical approaches. This research has both scientific and policy relevance. Specifically, it links routine DHIS2 surveillance to a multisource environmental evidence base, demonstrating how urban expansion, mobility corridors, and agro-hydrological context together structure malaria risk and visibility at the LGA scale ( 4 , 7 , 11 ). Programmatically, identifying when and where confirmed malaria is rising and which environmental indicators influence those increases directly supports subnational stratification, focal vector control, housing and drainage improvements, and strategic surveillance ( 5 , 6 ). By testing upland–riverine contrasts, this study also addresses operational questions about prioritizing freshwater inland settlements versus brackish riverine zones. In Bayelsa, the juxtaposition of fast-growing upland towns and low-lying, tidal LGAs provides a natural laboratory for integrating environmental predictors with routine data. We leverage validated proxies for settlement density (VIIRS nighttime lights), extent of built-up areas, vegetation (NDVI), and access (roads, healthcare travel time) alongside hydrogeomorphic variables (rivers, elevation, precipitation, and soil moisture) to generate a coherent explanatory framework ( 7 , 8 , 12 ). The resulting spatiotemporal narrative and predictor ranking aim to move Bayelsa’s malaria analytics from descriptive counts toward environmentally informed, geographically targeted decision-making. 2.0 Methodology 2.1 Data Sources Bayelsa State is one of the states in Nigeria and one of the states in the Niger Delta area of the country. Administratively, it has eight LGAs and 105 local wards (Fig. 1 ). The LGAs are divided into two categories: four upland and four riverine. The dominant vegetation comprises mangroves and freshwater swamp forests. Data on the annual incidence of confirmed uncomplicated malaria from 2017–2024 were obtained from Nigeria’s National Malaria Elimination Program through the District Health Information System 2 (DHIS2) platform ( 20 ). The DHIS2 serves as Nigeria's official national health management information system, capturing comprehensive malaria surveillance data from health facilities across all 36 states and the Federal Capital Territory. The platform provides standardized, real-time reporting of malaria case management, prevention activities, and epidemiological indicators, making it a highly credible and authoritative source for malaria data used by the Nigerian Ministry of Health, WHO, and international research organizations. Data on the potential environmental predictors of confirmed uncomplicated malaria were downloaded from the Google Earth Engine (GEE). The GEE is a cloud-based platform for planetary-scale geospatial analysis that enables Google's massive computational capabilities to address a variety of high-impact societal issues, including deforestation, drought, disaster, disease, food security, water management, climate monitoring and environmental protection ( 21 ) Data obtained from the GEE include the land surface temperature (LST), enhanced vegetation index (EVI), normalized difference vegetation index (NDVI), soil adjusted vegetation index (SAVI), normalized difference built-up index (NDBI), normalized difference water index (NDWI), shuttle radar topographic mapping (SRTM) data, soil moisture, soil pH, monthly precipitation, and VIIRS nighttime lights. Data on built-up areas, water coverage, and cropland coverage were extracted from the Environmental Systems Research Institute (ESRI) Living Atlas database ( 22 ). These land use/land cover data were processed from Sentinel-2 and are available at a 10-meter resolution worldwide ( 23 ). Administrative boundaries (states and LGAs) were obtained from Grid3.org (Fig. 1 ). All raster datasets were projected to a common coordinate system (WGS84 UTM32N). Zonal statistics were used to extract the average of each of the potential parameters for each LGA. The extracted values were combined with the administrative map of the LGAs in Bayelsa State for geospatial analysis and statistical modelling. 2.3 Data analysis Descriptive analysis was conducted on the confirmed uncomplicated malaria data to understand the spatiotemporal distribution of malaria incidents across LGAs and over time in Bayelsa State. In addition, we calculated the percentage change and the compound annual growth rate of confirmed uncomplicated malaria incidents. For each LGA, we extracted the yearly count of confirmed uncomplicated malaria cases for the years 2017–2024 from the DHIS2 routine surveillance data. We treated each LGA year as one observation and computed growth rates per LGA over the full interval from 2017–2024. Compound annual growth requires the number of years between the start and end points of the observations. For the 2017 (baseline) to 2024 (endpoint) window, the growth interval is n = 2024 − 2017 = 7. For each LGA, we therefore calculated the compound annual growth rate (CAGR) of confirmed uncomplicated malaria as follows: ​where V i,2017 ​ and V i,2024 ​ are the baseline and endpoint annual case counts for LGA, respectively. CAGR provides the constant annualized rate that links the beginning and ending values under geometric compounding. This is the same “geometric endpoint” growth measure widely used in official statistics and international monitoring ( 24 ). Because it is geometric, CAGR smooths year-to-year volatility and summarizes long-term changes with a single annualized figure. We analyzed routine LGA-level counts of confirmed uncomplicated malaria cases from 2017–2024, classified each LGA as upland or riverine based on proximity to the coastline, and used one-way ANOVA in SPSS to compare group means. The dataset comprises 8 LGA-year observations (4 upland; 4 riverine). We report group descriptives with 95% confidence intervals (CIs), the ANOVA F test, and effect sizes (etasquared, epsilonsquared, and omegasquared) with CIs. Interpretation follows standard guidance for ANOVA and effect sizes ( 25 ). Annual global Moran’s I was computed via GeoDa software to assess the spatial autocorrelation of confirmed uncomplicated malaria across Bayelsa’s eight LGAs. Moran’s I measure the similarity of values among neighboring areal units relative to the overall mean; its slope equals the regression slope in the Moran scatterplot ( 26 – 28 ). GeoDa software typically evaluates significance via permutation testing (e.g., 999 randomizations) to produce a pseudo p value and z score ( 29 , 30 ). With N = 8 LGAs, the expected value of Moran’s I under the null hypothesis of spatial randomness is E[I] = − 1/(N − 1) = − 1/7 ≈ − 0.1429 ( 26 , 27 ). The values near this benchmark are consistent with spatial randomness. The number of confirmed uncomplicated malaria incidents in each LGA was the dependent variable, while the various environmentally related variables constituted the explanatory variables. Pearson correlation analysis was used to investigate the associations between the number of confirmed uncomplicated malaria incidents and potential environmental predictors. The average malaria incidence by LGA was correlated with environmental, infrastructural, and access covariates: VIIRS nightlight radiance, soil moisture, top soil pH (0–20 cm), SAVI, NDWI, NDVI, NDBI, EVI, land surface temperature (LST), elevation, average daily precipitation, total road length, river length, number of healthcare facilities, area of built-up land, and area of cropland. Pearson r values and 1-tailed p-values were calculated (n = 8). Furthermore, to identify the predictors of confirmed uncomplicated malaria, exploratory and ordinary least squares (OLS) regressions were conducted. The ArcGIS Pro’s exploratory regression tool was used to screen combinations of candidate predictors for LGA--level malaria incidence in Bayelsa State and retained models that (a) explained a large share of variance (adjusted R 2 ), (b) were parsimonious (lowest Akaike information criterion (AICc), and (c) met OLS diagnostics—residual normality (Jarque–Bera, JB), homoscedasticity/stationarity (Koenker–Breusch–Pagan, K(BP)), acceptable multicollinearity (maximum VIF 0.05). These diagnostics follow established guidance for spatial regression and model selection ( 31 , 32 ). In addition, we used the ordinary least squares (OLS) model to fit ArcGIS Pro with malaria counts as the dependent variable and four covariates identified through exploratory regression: VIIRS night-time lights (VIIRS_NIGH), mean NDVI (Mean_NDVI), mean elevation (Elevation), and total river length (Rivers). ArcGIS Pro reports both classical and heteroskedasticity-robust (White) standard errors; when the Koenker–Breusch–Pagan test is significant, interpretation should rely on robust p values ( 33 ). Multicollinearity was assessed via variance inverse factors (VIFs). The exploratory and OLS regressions were conducted via ArcGIS Pro 3.4 ( 34 ). 3.0 Results 3.1 Spatiotemporal Distribution and Patterns of Confirmed Uncomplicated Malaria in Bayelsa State from 2017–2024 The spatial distribution and concentration of confirmed uncomplicated malaria show that, cumulatively, from 2017–2024, Yenagoa LGA (Bayelsa’s State capital) contributed 78,808 of 201,994 cases (39.02%), followed by Sagbama (24,914; 12.16%), southern Ijaw (22,215; 11.00%), Nembe (18,305; 9.06%), Kolokuma/Opokuma (17,920; 8.87%), Ekeremor (14,887; 7.37%), Ogbia (12,779; 6.33%), and Brass (12,526; 6.20%). Annually, Yenagoa’s share ranged from 28.25% (2018) to 49.2% (2019) and stood at 39.5% in 2024, demonstrating a persistent concentration of reported burden in that LGA throughout the period (Table 1 ). Yenagoa LGA had the highest confirmed uncomplicated malaria incidence in 2024, with 25,735 cases (39.5% of the state total), followed by Sagbama, with 8,327 cases (12.8%), and southern Ijaw, with 7,379 cases (11.3%). Mid-range counts were observed in Nembe (5,905; 9.1%) and Ogbia (5,844; 9.0%), while Kolokuma/Opokuma (4,582; 7.0%) and Ekeremor (4,426; 6.8%) counts were lower, and Brass (2,951; 4.5%) reported the lowest counts. All the LGAs reached their highest values in 2024. Relative to 2017, the rank positions in 2024 were stable at the top (Yenagoa first, Sagbama second, Southern Ijaw third), with upwards movement for Nembe (eighth to fourth) and Ogbia (seventh to fifth), and a decline for Brass (fourth to eighth) and Ekeremor (fifth to seventh). Table 1 Confirmed incidence of uncomplicated malaria across local government areas of Bayelsa State from 2017–2024. LGA 2017 2018 2019 2020 2021 2022 2023 2024 Total Brass 1159 1595 1201 1264 1851 1221 1284 2951 12526 Ekeremor 972 1567 1550 965 1274 1782 2351 4426 14887 Kolokuma/Opokuma 506 1384 2188 1357 2043 2269 3591 4582 17920 Nembe 256 1247 1392 1641 1441 2734 3689 5905 18305 Ogbia 505 858 671 371 853 1030 2647 5844 12779 Sagbama 1879 2475 1956 2040 1590 2014 4273 8327 24554 Southern Ijaw 1541 2138 1637 1195 1888 3021 3416 7379 22215 Yenagoa 3927 4434 10272 7341 4979 7729 14391 25735 78808 Total 10745 15698 20867 16174 15919 21800 35642 65149 202,354 Source: DHIS2 The temporal trend in confirmed uncomplicated malaria incidence at the state level revealed that the number of cases rose from 10,745 in 2017 to 65,149 in 2024. This represents a 6.06-fold increase, corresponding to a compound annual growth rate (CAGR) of 29.4% (Table 2 ). The annual totals and year-to-year (YoY) changes were as follows: 2017, 10,745; 2018, 15,698 (+ 46.1%); 2019, 20,867 (+ 32.9%); 2020, 16,174 (− 22.5%); 2021, 16,279 (+ 0.6%); 2022, 21,800 (+ 33.9%); 2023, 35,642 (+ 63.5%); and 2024, 65,149 (+ 82.8%) (Table 2 ). The period was characterised by an increase in the incidence of confirmed uncomplicated malaria from 2018–2019, a clear reduction in 2020, a minimal net change in 2021, recovery in 2022, and a step change beginning in 2023 that accelerated in 2024. The total of 2024 was 3.85 times greater than the average annual total from 2017–2022 (16,927). Table 2 Year-over-Year Changes (%) in Confirmed Uncomplicated Malaria Across LGAs in Bayelsa State LGA Year-over-Year Change (%) Avg Annual Change 2017-18 2018-19 2019-20 2020-21 2021-22 2022-23 2023-24 Brass 37.60% -24.70% 5.20% 46.40% -34.00% 5.20% 129.80% 23.70% Ekeremor 61.20% -1.10% -37.70% 32.00% 39.90% 31.90% 88.30% 30.60% Kolokuma/Opokuma 173.50% 58.10% -38.00% 50.60% 11.10% 58.30% 27.60% 48.70% Nembe 387.10% 11.60% 17.90% -12.20% 89.70% 34.90% 60.10% 84.20% Ogbia 69.90% -21.80% -44.70% 129.90% 20.80% 157.00% 120.80% 61.70% Sagbama 31.70% -21.00% 4.30% -22.10% 26.70% 112.20% 94.90% 32.40% Southern Ijaw 38.70% -23.40% -27.00% 58.00% 60.00% 13.10% 116.00% 33.60% Yenagoa 12.90% 131.70% -28.50% -32.20% 55.20% 86.20% 78.80% 43.40% Total 46.10% 32.90% -22.50% -1.60% 36.90% 63.50% 82.80% 34.00% At the LGA level, the data revealed that confirmed uncomplicated malaria increased by 2206.64% in Nembe, 1057.23% in Ogbia, 805.53% in Kolokuma/Opokuma, 555.33% in Yenagoa, 378.84% in southern Ijaw, 355.35% in Ekeremor, 343.16% in Sagbama, and 154.62% in Brass LGAs from 2017–2024. The corresponding approximate CAGRs were as follows: Nembe, 56.5%; Ogbia, 41.9%; Kolokuma/Opokuma, 37.0%; Yenagoa, 30.8%; southern Ijaw, 25.1%; Ekeremor, 24.2%; Sagbama, 23.7%; and Brass, 14.3% (Table 3 ). The 2024 surge relative to each LGA’s 2017–2022 median was highest in Ogbia (7.67 times), followed by a cluster of approximately fourfold in Sagbama (4.20 times), southern Ijaw (4.19 times), Yenagoa (4.18 times), and Nembe (4.17 times). Ekeremor (3.14 times) and Kolokuma/Opokuma (2.67 times) were moderate, and Brass (2.37 times) presented the smallest relative increase. Table 3 Annual Changes (%) in Confirmed Uncomplicated Malaria Across LGAs in Bayelsa State LGA 2017 (Start) 2024 (End) Growth Ratio CAGR (%) Classification Brass 1,159 2,951 2.55 14.30% Moderate Growth Ekeremor 972 4,426 4.55 24.20% High Growth Kolokuma/Opokuma 506 4,582 9.06 37.00% Very High Growth Nembe 256 5,905 23.07 56.60% Very High Growth Ogbia 505 5,844 11.57 41.90% Very High Growth Sagbama 1,879 8,327 4.43 23.70% High Growth Southern Ijaw 1,541 7,379 4.79 25.10% Very High Growth Yenagoa 3,927 25,735 6.55 30.80% Very High Growth Total 10,745 65,149 6.06 29.40% Very High Growth Note: Growth Classification Legend: Very High Growth: CAGR > 25%; High Growth: 15% < CAGR ≤ 25%; Moderate Growth: 5% < CAGR ≤ 15%; Low/Decline: CAGR ≤ 5% The year-specific patterns revealed that the 2019 state-level rise was strongly influenced by Yenagoa, which accounted for 49.2% of the cases that year (10,272 of 20,867). The 2020 decline was broad-based (Table 4 ), with the steepest LGA-level declines from 2019–2020 in Ogbia (− 44.7%; 671–371), Kolokuma/Opokuma (− 37.9%; 2,188–1,357), Ekeremor (− 37.7%; 1,550–965), Yenagoa (− 28.5%; 10,272–7,341), and southern Ijaw (− 27.0%; 1,637–1,195). The subsequent increase was pronounced in 2022–2023 in Ogbia (+ 157%; 1,030 to 2,647), Sagbama (+ 112%; 2,014 to 4,273), Yenagoa (+ 86%; 7,729 to 14,391), Kolokuma/Opokuma (+ 58%; 2,269 to 3,591), Ekeremor (+ 32%; 1,782 to 2,351), Nembe (+ 35%; 2,734 to 3,689), southern Ijaw (+ 13%; 3,021 to 3,416), and Brass (+ 5%; 1,221 to 1,284). It intensified again from 2023–2024: Brass (+ 130%; 1,284–2,951), Ogbia (+ 121%; 2,647–5,844), southern Ijaw (+ 116%; 3,416–7,379), Sagbama (+ 95%; 4,273–8,327), Ekeremor (+ 88%; 2,351–4,426), Yenagoa (+ 79%; 14,391–25,735), Nembe (+ 60%; 3,689–5,905), and Kolokuma/Opokuma (+ 28%; 3,591–4,582). Table 4 Percentage change in confirmed uncomplicated malaria LGA 2017–2018 2018–2019 2019–2020 2020–2021 2021–2022 2022–2023 2023–2024 Brass 37.62 -24.70 5.25 46.44 -34.04 5.16 129.83 Ekeremor 61.21 -1.08 -37.74 32.02 39.87 31.93 88.26 Kolokuma/Opokuma 173.52 58.09 -37.98 50.55 11.06 58.26 27.60 Nembe 387.11 11.63 17.89 -12.19 89.73 34.93 60.07 Ogbia 69.90 -21.79 -44.71 129.92 20.75 156.99 120.78 Sagbama 31.72 -20.97 4.29 -22.06 26.67 112.16 94.87 Southern Ijaw 38.74 -23.43 -27.00 57.99 60.01 13.08 116.01 Yenagoa 12.91 131.66 -28.53 -32.18 55.23 86.19 78.83 3.2 Comparative Analysis of Confirmed Uncomplicated Malaria between Upland and Riverine LGAs in Bayelsa State Bayelsa State lies within the Niger Delta, where malaria transmission is perennial but ecologically heterogeneous. Inland/upland LGAs are dominated by freshwater systems, while riverine/mangrove zones experience tidal influence and brackish conditions. Because dominant vectors such as Anopheles gambiae sensu stricto and Anopheles. coluzzii prefer sunlit, shallow freshwater, whereas brackish habitats are more suitable for more localized Anopheles. melas , differences in landscape settings may translate into measurable differences in malaria burden ( 2 , 16 ). We assessed whether confirmed uncomplicated malaria incidence rates differ between upland and riverine LGAs from 2017–2024. Across the eight LGAs, from 2017–2024, malaria counts were higher in upland LGAs than in riverine LGAs and showed markedly greater dispersion in upland settings. Upland LGAs (n = 4) reported a mean of 4,189 cases per LGA per year (SD = 5,064; SE = 895), with a 95% confidence interval (CI) for the mean of 2,363-6,015 and a range of 371 − 25,735. Riverine LGAs (n = 4) averaged 2,123 cases (SD = 1,490; SE = 263), 95% CI 1,586–2,660, ranging from 256–7,379 (Table 5 ). The mean difference was 2,067 cases, and the ratio of group means was approximately 1.97 (upland ≈ twice riverine). Relative variability was substantially greater in upland LGAs (coefficient of variation ≈ 121%) than in riverine LGAs (≈ 70%), reflecting heterogeneous burdens and occasional high-count years in upland areas. Overall, across all observations, the mean was 3,156 cases (SD = 3,847; 95% CI 2,195–4,117; range 256–25,735). Upland LGAs report higher rates of confirmed uncomplicated malaria than Riverine LGAs do in Bayelsa State (2017–2024). Table 5 Descriptive Comparison of Malaria Incidence between Upland and Riverine LGAs in Bayelsa State N Mean Std. Deviation Std. Error 95% Confidence Interval for Mean Minimum Maximum Lower Bound Upper Bound Upland 4 4189.41 5064.530 895.291 2363.45 6015.36 371 25735 Riverine 4 2122.91 1489.519 263.312 1585.88 2659.94 256 7379 Total 8 3156.16 3846.745 480.843 2195.27 4117.04 256 25735 A one-way ANOVA was used to test whether the mean number of confirmed uncomplicated malaria cases differed between upland and riverine LGAs (2017–2024). The analysis revealed a statistically significant group effect (F (1, 62) = 4.904, p = 0.030) (Table 6 ). Thus, the upland and riverine LGAs do not have the same average malaria counts. Given the descriptive results reported earlier (upland mean ≈ 4,189 vs riverine mean ≈ 2,123) (Table 5 ), the direction of the effect indicates higher counts in upland LGAs. Table 6 ANOVA of Confirmed Uncomplicated Malaria between Inland and Riverine LGAs in Bayelsa State Sum of Squares Df Mean Square F Sig. Between Groups 68326756.000 1 68326756.000 4.904 .030 Within Groups 863912188.438 62 13934067.555 Total 932238944.438 63 According to the ANOVA results (Table 6 ), the between-group mean square (68,326,756) is approximately 4.9 times greater than the within-group mean square (13,934,067.56), producing the observed F ratio (4.904). In terms of practical importance, effect size estimates computed for this comparison indicate that the group factor accounts for approximately 6–7% of the total variance (η² ≈ 0.073; ω² ≈ 0.057), representing a small to moderate effect by conventional benchmarks. 3.3 Pattern of Confirmed Uncomplicated Malaria among LGAs in Bayelsa from 2017–2024 All Moran’s I coefficients are negative, indicating that the global pattern of confirmed uncomplicated malaria is one of spatial dispersion (i.e., a tendency for dissimilar values to be neighbours, a “checkerboard” pattern) rather than clustering ( 26 , 27 ). The coefficients are small in absolute value (|I| < 0.20), and crucially, they hover around the null expectation for N = 8 ( ≈ − 0.1429). The Moran’s I was very close to the null value in 2017 (− 0.141), 2020 (− 0.149), and 2021 (− 0.131), while the Moran’s I was more negative than the null value in 2022. The most dispersed year was 2022 (− 0.168), while the year 2023 (− 0.069) indicates the weakest negative autocorrelation. There is mild interannual fluctuation with a trough (mostly negative) in 2022, followed by a rebound toward zero in 2023 and a slight decrease in 2024 (Table 7 ). No monotonic trend is evident. Given the small number of LGAs (N = 8) and the proximity of most estimates to the null expectation ( ≈ − 0.143), the global pattern is most consistent with spatial randomness in many years ( 30 ) Table 7 Moran’s I statistics for confirmed uncomplicated malaria incidence from 2017–2024 among LGAs in Bayelsa State, Nigeria. Year 2017 2018 2019 2020 2021 2022 2023 2024 Moran’s I -0.141 -0.122 -0.100 -0.149 -0.131 -0.168 -0.069 -0.099 Nigeria remains a high burden setting where malaria risk is heterogeneous over short distances due to environmental, infrastructural, and intervention coverage gradients ( 33 ). Such heterogeneity can generate local clusters even when the statewide (global) pattern appears randomly. Across 2017–2024, confirmed uncomplicated malaria in Bayelsa State exhibited weak negative global spatial autocorrelation, with coefficients clustered around the null expectation for the eight LGAs. The statewide pattern is largely consistent with spatial randomness, punctuated by year-to-year fluctuations. 3.4 Association between Malaria Incidence and Environmental Predictors Malaria transmission in southern Nigeria is perennial and intense, driven by efficient Anopheles vectors and a warm, humid deltaic environment ( 33 ). While climate sets broad suitability, within-state heterogeneity often reflects how people live, move, and access care ( 5 , 10 ). We examined LGA-level correlations between average malaria incidence and environmental/infrastructural covariates in Bayelsa to understand which landscape features align with the observed patterns. Bayelsa’s Niger Delta ecology ensures perennial malaria receptivity, with Anopheles gambiae s.l. and Anopheles funestus sustained by warm temperatures and high humidity ( 2 , 34 ). Within-state heterogeneity is therefore expected to reflect human environmental modification, population aggregation, mobility, and differential access to testing and care ( 5 , 10 ). The built environment indicators, such as the area of built-up land (r = 0.954, p < 0.001), exhibited the strongest positive correlations with confirmed uncomplicated malaria. Nightlights (r = 0.706, p = 0.025) supported this pattern, which is consistent with these variables acting as complementary proxies for settlement density and urban/peri-urban expansion ( 5 , 35 ). There was also a strong positive association between the incidence of confirmed uncomplicated malaria and the length of roads in each LGA (r = 0.912, p = 0.001, r²≈0.83). Roads both concentrate people and create larval habitats through borrow pits, drainage failures, and puddling along verges; they also facilitate parasite flow via human movement ( 9 ). The number of confirmed uncomplicated malaria incidents was also positively correlated with the number of healthcare facilities (r = 0.808, p = 0.008). This likely reflects detection/reporting intensity, such that LGAs with more facilities test and report more, inflating the observed incidence relative to areas with poorer access ( 33 ). This may also reflect the rational allocation of facilities to higher burden LGAs. The number of confirmed uncomplicated malaria incidents was also positively correlated with the area of cropland (r = 0.711, p = 0.024). Agricultural landscapes, especially low-lying and poorly drained fields, irrigation ditches, ponds, and wheel ruts, frequently generate sunlit, shallow water bodies favored by major vectors ( 13 , 14 ). The vegetation indices (SAVI, r = 0.108; NDVI, r = 0.162; EVI, r = 0.142) and NDWI (r = 0.129) showed little explanatory power. LST (r = 0.108) and precipitation (r = − 0.250) were also weak. Elevation (r = 0.399) and soil pH (r = 0.586, p = 0.063) tended to increase but were not significant. River length was weakly negative (r = − 0.284). In Bayelsa’s consistently warm, rainy environment, climatic gradients between LGAs are modest and remain near the thermal optimum for Plasmodium falciparum transmission, limiting their explanatory value at this spatial scale ( 3 , 36 ). Large rivers and tidal channels are typically poor larval habitats compared with small, sunlit, stagnant pools ( 2 ). 3.5 Environmental Predictors of Confirmed Uncomplicated Malaria in Bayelsa State The exploratory regression analysis yielded three potential models for explaining variations in confirmed uncomplicated malaria among LGAs in Bayelsa State. These candidate models had very high fits (AdjR 2 ≥ 0.97) and passed key diagnostics (Table 6 ). AICc differences and diagnostics, however, identify Model 1 as the strongest, with Model 2 being a plausible alternative and Model 3 receiving comparatively less support. Model 1 (best-supported) identified increased VIIRS Nighttime Light (+), reduced NDVI (-), increased Elevation (+) and increased length of streams/rivers (+) as the most likely predictors of confirmed uncomplicated malaria among LGAs in Bayelsa State (Table 8 ). Table 8 Highest adjusted R-square results from the exploratory regression analysis AdjR2 AICc JB K(BP) VIF SA Model 0.98 204.71 0.75 0.16 6.19 0.32 +VIIRS_Night*** -Mean_NDVI_*** +Elevation_*** +Rivers*** 0.98 207.42 0.98 0.37 5.73 0.87 -Soil Moisture*** +Elevation_** +Avg_Precip** +Built-up*** 0.97 208.87 0.81 0.35 2.71 0.37 +VIIRS_Night*** +Mean_Lst_2*** +Rivers** +Cropland*** The VIIRS nighttime lights (positive) proxy settlement density, electrification, and economic activity. Brighter, denser LGAs typically exhibit more peri-urban habitats (blocked drains, borrow pits, water containers) that support Anopheles gambiae s.l. , sustaining focal transmission ( 5 , 6 , 37 , 38 ). The negative NDVI suggests that greener and more vegetated LGAs are less prone to malaria at this scale, which is consistent with the preference of dominant West African vectors for small, sunlit, shallow freshwater pools rather than shaded habitats. Increased greenness often corresponds to canopy covers or wetlands that are less productive for these vectors ( 2 , 14 ). The positive elevation aligns with Bayelsa’s ecology. The slightly higher inland/upland LGAs provide fresher water bodies, whereas low-lying, saline/brackish tidal zones are less suitable for Anopheles. gambiae s.s./Anopheles. coluzzii ; coastal Anopheles. melas is more saline-tolerant but have a narrower distribution ( 2 , 16 ). Additionally, the positive relationship between the length of rivers/streams likely captures floodplain edges, borrow pits, and human settlements along waterways, where overbank flooding and poor drainage create discrete larval habitats and increase exposure ( 14 ). This model combines the lowest AICc with a very strong fit and acceptable diagnostics, indicating the best trade-off between explanatory power and parsimony (Table 8 ). Its predictors also match established transmission mechanisms: anthropogenic settlement intensity coupled with freshwater availability. The second model is ecologically coherent and statistically sound, but its higher AICc provides less support than Model 1 does. Using Akaike weights, Model 1 receives ~ 72% of the support, and Model 2 receives ~ 19%. Model 3 diagnostics are strong, and multicollinearity is minimal, but the higher AICc and slightly lower fit make this model less competitive (Table 8 ). Nighttime lights and built-up areas consistently appear in the best models, indicating that settlement density, nighttime lights, and rapid peri-urban growth structure malaria risk, likely via the creation of artificial breeding sites and increased human‒vector contact ( 5 , 6 , 39 ). Hydroclimate factors, such as soil moisture, precipitation, rivers, and cropland, confirm that water availability and management affect habitat persistence, especially in floodplain/upland mosaics typical of Bayelsa ( 14 , 36 ). The nonsignificant Moran’s I on residuals (SA p ≥ 0.32) indicates no remaining unmodelled spatial structure, while the Koenker test p > 0.1 suggests that relationships are spatially stable at the LGA scale and that the VIF values are within accepted limits, indicating manageable collinearity ( 32 ). The variables identified by the exploratory regression were further analyzed OLS regression in ArcGIS Pro software to identify the contributions of the identified explanatory factors to malaria incidence in Bayelsa State. All four predictors in Model 1 are highly significant under both classical and robust inference (all p < 0.001), indicating stable associations even if the residual variance is nonconstant. The variance inverse factors (VIF) values are acceptable: 1.19 (VIIRS), 3.44 (NDVI), 6.19 (elevation), and 2.73 (river) (Table 9 ). Although elevation shows moderate collinearity, it remains below the common thresholds (≤ 7.5) recommended for ArcGIS OLS ( 32 ) Additionally, because covariates are at different scales, the relative importance is best gauged by t-statistics. With respect to robust t values, the variable influence ranks were as follows: VIIRS (25.86) > Elevation (18.89) > NDVI (|12.88|) > Rivers (11.23) (Table 9 ). Table 9 Summary of OLS Results Variable Coefficienta StdError t-Statistic Probabilityb Robust_SE Robust_t Robust_Prb VIFc Intercept -5713.31 1055.711 -5.41181 0.000033* 445.1671 -12.8341 0.000000* -------- VIIRS Nighttime Light 10880.47 608.2618 17.8878 0.000000* 420.7523 25.85955 0.000000* 1.194224 NDVI -48331.8 5441.864 -8.88148 0.000000* 3752.763 -12.879 0.000000* 3.440061 Elevation 817.857 59.59614 13.72332 0.000000* 43.30437 18.88625 0.000000* 6.189371 Length of Stream/Rivers 3.383418 0.375365 9.013671 0.000000* 0.301398 11.22575 0.000000* 2.729606 The VIIRS nighttime light data show a strong positive association with confirmed uncomplicated malaria across the LGAs in Bayelsa State from 2017–2024 (β = 10,880.47; robust p < 0.001; VIF 1.19). Nighttime light radiance is a validated proxy for settlement density, electrification, and economic activity ( 39 ). Brighter LGAs are typically more urban/peri-urban, where rapid expansion, poor drainage, construction of burrow pits, and container storage create numerous sunlit freshwater habitats near households. These settings sustain Anopheles gambiae . and maintain focal transmission despite some urbanization-related protection ( 2 ). The NDVI (β = −48,331.81; robust p < 0.001; VIF 3.44) also showed a strong negative association with confirmed uncomplicated malaria. A higher NDVI indicates denser/greener vegetation, which often corresponds to shaded, forested, or permanently inundated environments that are less favorable for the sunlit, shallow pools preferred by dominant West African vectors ( 2 ) Studies repeatedly show that small, open, human-made or modified water bodies in more sparsely vegetated peri-urban/agricultural mosaics are productive larval habitats ( 12 , 14 ). Elevation was also positively associated with confirmed uncomplicated malaria (β = 817.86; robust p < 0.001; VIF 6.19). Within Bayelsa’s low relief delta, slightly greater inland/upland areas are less brackish and provide fresher breeding waters favoured by Anopheles. gambiae s.s./Anopheles. coluzzii , whereas the lowest coastal/mangrove zones are more saline and better suited to Anopheles. melas , which is geographically restricted ( 2 , 16 ). This gradient is consistent with greater receptivity away from tidal marshes. The length of streams/rivers was positively associated with confirmed uncomplicated malaria (β = 3.38; robust p < 0.001; VIF = 2.73). River networks and their floodplains generate abundant fringe habitats, such as burrow pits and post flood residual pools, while also concentrating settlements and human activity along banks. These conditions increase both larval habitat availability and human–vector contact ( 4 , 40 ). 4.0 Discussion In this study, we analyzed the spatiotemporal dynamics of confirmed uncomplicated malaria, its pattern and predictors among LGAs in Bayelsa State, Nigeria, and found that malaria incidents are highly heterogeneous among LGAs and that there has been a high incidence since 2023. The spatial pattern of malaria incidence was dispersed, and no clustering was observed based on the results from Moran's I. Although malaria incidents were significantly correlated with built environment indicators such as the area of built-up land, nighttime light intensity, length of roads, number of healthcare facilities, and area of cropland, only increased VIIRS nighttime light, reduced NDVI, increased elevation and increased length of rivers/streams were predictors of confirmed uncomplicated malaria in the state. Using DHIS2 routine data, we observed three consistent patterns in Bayelsa State’s confirmed uncomplicated malaria burden. There is a sharp inflexion beginning in 2023 and accelerating through 2024. In routine surveillance, abrupt rises can reflect true increases in transmission, step changes in access to and uptake of testing, improvements in reporting completeness, or combinations thereof; disentangling these drivers requires triangulation with testing volumes, test positivity, stockout logs, and timeliness/completeness indicators ( 41 , 42 ). The dip in 2020, followed by a rebound in 2021–2022, is temporally consistent with broad COVID–19–related disruptions to malaria services across sub-Saharan Africa and subsequent restoration of care seeking and diagnostics ( 43 ), reinforcing the need for programmatic indicators when interpreting DHIS2 counts. The analysis of year-over-year malaria case trends reveals alarming patterns that may demand immediate public health attention. The data show an escalating malaria burden that has reached crisis proportions by 2024, as indicated by the dramatic surge experienced across all LGAs from 2023–2024, with some LGAs more than doubling their caseloads. Yenagoa, the state capital, saw cases jump by 78.8% from 14,391 to 25,735, Ogba experienced an even more severe 120.8% increase from 2,647 to 5,844 cases. Brass recorded the highest proportional increase at 129.8%, with cases rising from 1,284 to 2,951, and Sagbama showed a substantial 94.9% jump from 4,273 to 8,327 cases. The data also reveals volatility in malaria reporting patterns. Yenagoa demonstrated the most erratic trajectory, swinging dramatically from a 131.8% increase between 2018 and 2019 to a sharp 28.5% decline the following year. Similarly, Nembe experienced explosive early growth, with a staggering 387.1% increase from 2017–2018, highlighting the unpredictable nature of malaria transmission dynamics in these LGAs. While 2024 was the peak in every LGA, the magnitude of change varied markedly: Nembe showed the largest long-term increase relative to 2017 (≈ 23.1×), and Ogbia recorded the sharpest surge relative to its 2017–2022 median (≈ 7.67×). Brass and Kolokuma/Opokuma rose less steeply, which could reflect genuinely lower burdens or under detection scenarios with very different operational implications ( 33 ). Together, these findings argue for maintaining high-intensity coverage and surveillance in Yenagoa, Sagbama, and southern Ijaw while conducting focused assessments in Ogbia and Nembe to understand the rapid recent increases. Several challenging trends emerged from longitudinal analysis. The year 2020 marked a period of disruption across multiple LGAs, with mixed results that likely reflect the impact of COVID-19 on health reporting systems and healthcare access. However, the 2022–2024 period shows a consistent pattern of accelerating growth across all LGAs, with average annual increases ranging from 28% to over 50% for most LGAs. This sustained upwards trajectory suggests systemic challenges in malaria control efforts. The total system impact reflects a sobering picture of public health deterioration. Across all eight LGAs, malaria cases increased from 10,745 in 2017 to 65,149 in 2024, representing a more than six-fold increase over seven years. The particularly sharp increases observed in the final two years of the study period raise critical questions about whether this trend reflects improved surveillance systems that ultimately capture the true disease burden or represent a genuine escalation in malaria transmission that requires urgent intervention from state and federal health authorities. The inter-LGA differences align strongly with anthropogenic and agroecological correlates. The extent of built-up land is strongly and positively associated with confirmed malaria (r = 0.954, p < 0.001), and it covaries with the NDBI (r = 0.967, p < 0.001), total road length (r = 0.982, p < 0.001), number of healthcare facilities (r = 0.815, p = 0.007), and cropland area (r = 0.709, p = 0.025). VIIRS nighttime light (NTL), a validated proxy for settlement density and economic activity, shows an equally strong association with malaria (r = 0.964, p < 0.001) ( 8 , 39 , 44 ). These tightly linked indicators capture a common “settlement intensity/connectivity” dimension that plausibly raises malaria risk through several mechanisms. Urban and peri-urban expansion can create abundant, sunlit, shallow freshwater habitats such as blocked drains, construction of burrow pits, tire ruts, and water storage containers close to households. These habitats are highly productive for the dominant West African vectors Anopheles gambiae s.s. and Anopheles. coluzzii , sustaining focal transmission even as some features of (e.g., improved housing) may reduce risk on average ( 5 , 6 , 37 , 45 ). The strong agreement between the mapped built-up area and the NDBI further supports the validity of these remotely sensed settlement proxies ( 44 ). NTL’s correlation with roads and services is also programmatically meaningful: electrified, economically active corridors tend to be well connected by roads, which generate larval habitats by puddling and drainage failures and facilitate parasite movement through human mobility ( 9 , 18 , 37 ). Artificial light at night may additionally shift mosquito and human activity patterns in ways that increase evening biting opportunities ( 46 ). The number of healthcare facilities is a second, complementary pathway. Confirmed uncomplicated malaria incidents were positively associated with the number of healthcare facilities (r = 0.808, p = 0.008). The number of healthcare facilities covaries with road length (r = 0.831, p = 0.005) and built-up area (r = 0.815, p = 0.007), reflecting siting along populated, accessible corridors ( 11 , 47 ). Where facilities are numerous, a larger share of infections is tested and reported, inflating the measured incidence relative to areas with poorer access, which is a well-documented surveillance effect in programmatic malaria data ( 19 , 41 , 48 ). Accordingly, some of the strong positive associations with built environment proxies and NTL likely reflect both ecological risk and enhanced case detection. Agriculture and elevation add another layer. Cropland area is positively correlated with malaria (r = 0.711, p = 0.024) and is more extensive at higher elevations in Bayelsa, suggesting a concentration away from tidally influenced, brackish mangrove zones. Upland croplands and farmadjacent drainage features provide the clear, sunlit freshwater microhabitats preferred by Anopheles. gambiae s.s./Anopheles. coluzzii ; in contrast, brackish habitats of coastal marshes are less suitable for these vectors, with the salt-tolerant An. melas has a more restricted coastal distribution ( 2 , 16 ). Urban/peri-urban agriculture is repeatedly associated with higher vector densities and local transmission in African cities, echoing the patterns we observe here ( 14 , 15 , 45 ). Roads link these agroecological patches, enabling both habitat creation (e.g., borrow pits) and parasite flow via mobility ( 9 , 18 ). The Bayelsa data, therefore, suggest that a constellation of settlement-related indicators (built-up extent, VIIR), connectivity (roads), health-service availability (number of healthcare facilities), and freshwater- favorable land use (cropland, upland locations) shape where malaria is most frequently detected. These are correlations rather than causal estimates, but they are congruent with established mechanisms linking urban/peri-urban growth, mobility, and agriculture to vector ecology and transmission dynamics ( 5 , 6 , 37 ). Programmatically, these factors argue for (i) sustained high-intensity coverage in Yenagoa and other bright, highly connected upland LGAs; (ii) targeted environmental management of “few, fixed, and findable” habitats in construction zones, roadside corridors, and farm edges; and (iii) routine triangulation of DHIS2 counts with testing volume, test positivity, supply chain, and reporting completeness to separate true epidemiologic shifts from surveillance artefacts ( 41 , 42 ). 5.0 Conclusion From 2017 to 2024, Bayelsa State experienced a sharp increase in confirmed uncomplicated malaria cases beginning in 2023, with a persistent concentration of burden in Yenagoa and heterogeneous surges elsewhere (notably Nembe and Ogbia LGAs). Spatial variation in the DHIS2-reported burden is most strongly aligned with a latent “settlement connectivity” axis, captured by built-up extent, NTL, and road length, reinforced by the number of healthcare facilities and upland cropland, all of which jointly increase both the ecological receptivity and the visibility of cases ( 2 , 5 , 6 , 9 , 11 , 14 , 15 , 18 , 37 , 39 , 41 , 42 , 45 , 47 ). In a thermally optimal, uniformly wet setting, these human landscape features overshadow coarse climate and vegetation metrics. The findings point toward peri-urban environmental management and housing improvements, alongside strengthened, unbiased surveillance, as priorities for reducing the malaria burden in Bayelsa State. The immediate priorities are to sustain high coverage in the most affected upland, highly connected LGAs; to address peri-urban and roadside larval habitats and settlement drainage; and to verify the 2023–2024 step change through systematic triangulation with testing and reporting indicators to determine the relative contributions of epidemiologic versus operational drivers and refine subnational prioritization in line with national and WHO guidance ( 49 – 51 ). As Bayelsa advances subnational stratification, these findings support geographically targeted vector control and surveillance that explicitly account for the coupled effects of urbanization, mobility, and agroecology on malaria transmission and detection. Abbreviations ANOVA Analysis of variance AICc Akaike information criterion CAGR Compound annual growth rate CI Confidence interval DHIS 2 District Health Information System 2 ESRI Environmental Systems Research Institute EVI Enhanced Vegetation Index GEE Google Earth Engine JB Jarque–Bera K(B)P Koenker–Breusch–Pagan LST Land surface temperature NDBI Normalized difference built-up index NDVI Normalized difference vegetation index NDWI Normalized difference water index NMEP National Malaria Elimination Program NTL Nighttime light OLS Ordinary least squares Ph Potential of hydrogen SA Spatial autocorrelation SAVI Soil Adjusted Vegetation Index SPSS Statistical Package for the Social Sciences SRTM Shuttle Radar Topographical Mapping VIF Variance Inverse Factor VIIRS Visible Infrared Imaging Radiometer Suite Declarations Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Availability of data and materials The malaria case data used in this study were obtained from the District Health Information System 2 (DHIS2) and were granted access by the National Malaria Elimination Program (NMEP), Nigeria. All other data sets used in this study are openly available. The environmental predictor variables were obtained from the Google Earth Engine (GEE). Administrative boundaries were sourced from Grid3.org. Competing interests The authors declare that they have no competing interests. Funding This study received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. Authors’ contributions JOO , OJT and OA drafted the original manuscript, as well as reviewed and edited the manuscript. GE, DA, IE were involved in project administration and reviewing the manuscript. LO, AA, ST, TB, GW, curated data. JOO and OJT conducted a formal analysis. CK critically review the manuscript and provide feedback with leadership of project design, supervision of project delivery, and supervisory authorship of our manuscript. All the authors have read and approved the final manuscript. Acknowledgements Not applicable References Kar NP, Kumar A, Singh OP, Carlton JM, Nanda N. A review of malaria transmission dynamics in forest ecosystems. Parasit Vectors. 2014;7(1):265. Sinka ME, Bangs MJ, Manguin S, Coetzee M, Mbogo CM, Hemingway J, et al. The dominant Anopheles vectors of human malaria in Africa, Europe and the Middle East: occurrence data, distribution maps and bionomic précis. Parasit Vectors. 2010;3(1):117. Mordecai EA, Paaijmans KP, Johnson LR, Balzer C, Ben-Horin T, de Moor E, et al. Optimal temperature for malaria transmission is dramatically lower than previously predicted. Ecol Lett. 2013;16(1):22–30. Machault V, Vignolles C, Borchi F, Vounatsou P, Pages F, Briolant S, et al. The use of remotely sensed environmental data in the study of malaria. Geospat Health. 2011;5(2):151. Hay SI, Guerra CA, Tatem AJ, Atkinson PM, Snow RW. Urbanization, malaria transmission and disease burden in Africa. Nat Rev Microbiol. 2005;3(1):81–90. Tusting LS, Bisanzio D, Alabaster G, Cameron E, Cibulskis R, Davies M, et al. Mapping changes in housing in sub-Saharan Africa from 2000 to 2015. Nature. 2019;568(7752):391–4. Li X, Elvidge C, Zhou Y, Cao C, Warner T. Remote sensing of night-time light. Int J Remote Sens. 2017;38(21):5855–9. Small C, Elvidge CD, Balk D, Montgomery M. Spatial scaling of stable night lights. Remote Sens Environ. 2011;115(2):269–80. Wesolowski A, Eagle N, Tatem AJ, Smith DL, Noor AM, Snow RW, et al. Quantifying the Impact of Human Mobility on Malaria. Science (1979). 2012;338(6104):267–70. Bhatt S, Weiss DJ, Cameron E, Bisanzio D, Mappin B, Dalrymple U, et al. The effect of malaria control on Plasmodium falciparum in Africa between 2000 and 2015. Nature. 2015;526(7572):207–11. Weiss DJ, Nelson A, Vargas-Ruiz CA, Gligoric K, Bavadekar S, Gabrilovich E, et al. Global maps of travel time to healthcare facilities. Nat Med. 2020;26(12):1835–8. Ceccato P, Connor SJ, Jeanne I, Thomson MC. Application of Geographical Information Systems and Remote Sensing technologies for assessing and monitoring malaria risk. Parassitologia. 2005; 47:81–96. Ijumba JN, Lindsay SW. Impact of irrigation on malaria in Africa: paddies paradox. Med Vet Entomol. 2001;15(1):1–11. Munga S, Minakawa N, Zhou G, Mushinzimana E, Barrack OOJ, Githeko AK, et al. Association Between Land Cover and Habitat Productivity of Malaria Vectors in Western Kenya Highlands. Am J Trop Med Hyg. 2006;74(1):69–75. Klinkenberg E, McCall P, Wilson MD, Amerasinghe FP, Donnelly MJ. Impact of urban agriculture on malaria vectors in Accra, Ghana. Malar J. 2008;7(1):151. Coetzee M, Hunt RH, Wilkerson R, Torre A Della, Coulibaly MB, Besansky NJ. Anopheles coluzzii and Anopheles amharicus, new members of the Anopheles gambiae complex. Zootaxa. 2013;3619(3). Aliyu A, Amadu L. Urbanization, cities, and health: The challenges to Nigeria – A review. Ann Afr Med. 2017;16(4):149. Ruktanonchai NW, Smith DL, De Leenheer P. Parasite sources and sinks in a patched Ross–Macdonald malaria model with human and mosquito movement: Implications for control. Math Biosci. 2016; 279:90–101. Stresman G, Cameron A, Drakeley C. Freedom from Infection: Confirming Interruption of Malaria Transmission. Trends Parasitol. 2017;33(5):345–52. Yusuf O, Okoronkwo C, Ademu C, Silal SP. Investigating the relationship between climatic factors and malaria transmission dynamics in Southwest states of Nigeria. 2024. Gorelick N, Hancher M, Dixon M, Ilyushchenko S, Thau D, Moore R. Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sens Environ. 2017; 202:18–27. Esri. https://livingatlas.arcgis.com/landcoverexplorer/. ArcGIS Living Atlas of the World. Karra K, Kontgis C, Statman-Weil Z, Mazzariello JC, Mathis M, Brumby SP. Global land use/land cover with Sentinel 2 and deep learning. In: 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS. IEEE; 2021. p. 4704–7. Liu M, Zhang Y. Impact of climate change on dengue fever: a bibliometric analysis. Geospat Health. 2025;20(1). Ellis PD. The essential guide to effect sizes: statistical power, meta-analysis, and the interpretation of research results. Cambridge University Press; 2010. 173 p. Moran PAP. Notes on Continuous Stochastic Phenomena. Biometrika. 1950;37(1/2):17. Getis A. Cliff, A.D. and Ord, J.K. 1973: Spatial autocorrelation. London: Pion. Prog Hum Geogr. 1995 Jun 1;19(2):245–9. Anselin L, Syabri I, Kho Y. GeoDa: An Introduction to Spatial Data Analysis. In: Handbook of Applied Spatial Analysis. Berlin, Heidelberg: Springer Berlin Heidelberg; 2010. p. 73–89. Anselin L. Local Indicators of Spatial Association—LISA. Geogr Anal. 1995;27(2):93–115. Waller Lance A, Gotway Carol A. Applied Spatial Statistics for Public Health Data. John Wiley & Sons; 2004. Anselin Luc. Spatial econometrics: methods and models. Kluwer Academic Publishers; 1988. 284 p. Dormann CF, Elith J, Bacher S, Buchmann C, Carl G, Carré G, et al. Collinearity: a review of methods to deal with it and a simulation study evaluating their performance. Ecography. 2013;36(1):27–46. WHO. Malaria surveillance, monitoring and evaluation: a reference manual. In Malaria surveillance, monitoring and evaluation: a reference manual. 2018. Ciski M, Rzasa K. Multiscale Geographically Weighted Regression in the Investigation of Local COVID-19 Anomalies Based on Population Age Structure in Poland. Int J Environ Res Public Health. 2023;20(10):5875. WHO. WHO Malaria Policy Advisory Group (MPAG) Meeting Report. 2023. https://www.who.int/publications/i/item/9789240074385. Accessed 20 July 2025. Elvidge CD, Baugh KE, Kihn EA, Kroehl HW, Davis ER. Mapping city lights with nighttime data from the DMSP Operational Linescan System. Photogramm Eng Remote Sensing. 1997;63(6):727–34. Githeko AK, Lindsay SW, Confalonieri UE, Patz JA. Climate change and vector-borne diseases: a regional analysis. Bulletin of the World Health Organization Special Theme– Environment and Health. 2000;78(9):1136–47. Keiser J, Jürg Utzinger, Marcia Caldas De Castro, Thomas A. Smith, Marcel Tanner, Burton H. Singer. Urbanization in Sub-Saharan Africa and Implication for Malaria Control. In: The Intolerable Burden of Malaria II: What’s New, What’s Needed: Supplement to Volume 71 (2) of the American Journal of Tropical Medicine and Hygiene. 2nd ed. American Society of Tropical Medicine and Hygiene, Northbrook (IL); 2004. Elvidge CD, Zhizhin M, Ghosh T, Hsu FC, Taneja J. Annual Time Series of Global VIIRS Nighttime Lights Derived from Monthly Averages: 2012 to 2019. Remote Sens (Basel). 2021;13(5):922. Elvidge CD, Baugh KE, Zhizhin M, Hsu FC. Why VIIRS data are superior to DMSP for mapping nighttime lights. In: Proceedings of the Asia-Pacific Advanced Network. 2013. p. 62. Minakawa N, Munga S, Atieli F, Mushinzimana E, Zhou G, Githeko AK, et al. Spatial Distribution of Anopheline Larval Habitats in Western Kenyan Highlands: Effects of Land Cover Types and Topography. Am J Trop Med Hyg. 2005;73(1):157–65. WHO. WHO malaria policy advisory group (MPAG) meeting report. World Health Organization; 2024. https://www.who.int/publications/i/item/9789240095298. Accessed 14 July 2025. Sherrard-Smith E, Hogan AB, Hamlet A, Watson OJ, Whittaker C, Winskill P, et al. The potential public health consequences of COVID-19 on malaria in Africa. Nat Med. 2020;26(9):1411–6. Zha Y, Gao J, Ni S. Use of normalized difference built-up index in automatically mapping urban areas from TM imagery. Int J Remote Sens. 2003;24(3):583–94. Sattler MA, Mtasiwa D, Kiama M, Premji Z, Tanner M, Killeen GF, et al. Habitat characterization and spatial distribution of Anopheles sp. mosquito larvae in Dar es Salaam (Tanzania) during an extended dry period. Malar J. 2005;4(1):4. Rund S, O’Donnell A, Gentile J, Reece S. Daily Rhythms in Mosquitoes and Their Consequences for Malaria Transmission. Insects. 2016;7(2):14. Noor AM, Zurovac D, Hay SI, Ochola SA, Snow RW. Defining equity in physical access to clinical services using geographical information systems as part of malaria planning and monitoring in Kenya. Tropical Medicine & International Health. 2003;8(10):917–26. Stresman G, Bousema T, Cook J. Malaria Hotspots: Is There Epidemiological Evidence for Fine-Scale Spatial Targeting of Interventions? Trends Parasitol. 2019;35(10):822–34. NMEP, NPC, ICF. Nigeria Malaria Indicator Survey 2021 Final Report. Abuja; 2022. 150 p. WHO. Global technical strategy for malaria, 2016-2030. Global Malaria Program, World Health Organization; 2021. 29 p. WHO. Report on the first and second meetings of the Technical Advisory Group on Malaria Elimination and Certification. World Health Organization, 2023. 2023. Additional Declarations No competing interests reported. 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09:23:02","extension":"html","order_by":6,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":184432,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8165329/v1/3afb1c756e5fc968233cd1e1.html"},{"id":97423177,"identity":"e90d1d1b-ed53-43ae-a995-4ef53c54b5fd","added_by":"auto","created_at":"2025-12-04 08:51:57","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":583803,"visible":true,"origin":"","legend":"\u003cp\u003eAdministrative Map of Bayelsa State, Nigeria\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8165329/v1/217db445ee1fb443a750d5bb.png"},{"id":97677330,"identity":"e4c41e88-cc86-40c1-a7bd-1dc72f5110be","added_by":"auto","created_at":"2025-12-08 09:52:59","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1903699,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8165329/v1/b2d80ca8-eeb6-4611-9549-d9873ad5d68e.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Spatiotemporal Dynamics and Environmental Predictors of Confirmed Uncomplicated Malaria in Bayelsa State, Nigeria (2017-2024)","fulltext":[{"header":"1.0 Introduction","content":"\u003cp\u003eMalaria remains a leading cause of morbidity in West Africa, yet its intensity varies sharply over space and time, as climate, hydrology, settlement form, housing, and mobility shape receptivity and exposure (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Across tropical Africa, transmission peaks in warm, humid landscapes where efficient vectors such as \u003cem\u003eAnopheles gambiae sensu stricto\u003c/em\u003e and \u003cem\u003eAnopheles coluzzii\u003c/em\u003e exploit small, sunlit freshwater habitats, whereas brackish tidal systems favor more localized \u003cem\u003eAnopheles melas\u003c/em\u003e (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Temperatures near 25\u0026ndash;27\u0026deg;C maximize transmission potential, but local water management and land use often dominate at subnational scales (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Urbanization has a dual effect: improved housing can suppress risk, yet rapid peri-urban growth, roadworks, and construction create prolific larval habitats close to households (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Proxies of settlement intensity and connectivity, including nighttime lights and built-up indices, consistently align with where people live, move, and access care (\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). Against this backdrop, Nigeria continues to shoulder a large share of the global \u003cem\u003ePlasmodium falciparum\u003c/em\u003e burden, with subnational heterogeneity central to control (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). Bayelsa State, straddling upland freshwater and riverine/mangrove ecologies in the Niger Delta, is an archetypal setting for examining how the environment and urban growth structure of malaria are detected through routine surveillance.\u003c/p\u003e\u003cp\u003eGiven Bayelsa\u0026rsquo;s mix of freshwater and mangrove ecologies, remote sensing and geospatial epidemiology play critical roles in the identification of spatial heterogeneity and environmental predictors of malaria. Vegetation indices (e.g., the normalized difference vegetation index (NDVI)) often have inverse relationships with malaria, where a dense canopy reduces the number of sunlit larval habitats, whereas hydrologic indicators (precipitation, soil moisture, flooded area) and river proximity capture habitat availability and persistence, frequently with temporal lags (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). Land use also matters, as peri-urban/urban agriculture, drainage ditches, and construction sites consistently produce productive larval habitats near households (\u003cspan additionalcitationids=\"CR14\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). In deltaic settings, slight increases in inland elevation can signal fresher waters away from saline influences, favoring \u003cem\u003eAnopheles. gambiae s.l.\u003c/em\u003e (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). Anthropogenic structure and connectivity further organize risk and the visibility of cases (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). Urbanization has a dual effect since improved housing can suppress transmission, yet rapid peri-urban growth and poor drainage create focal hotspots (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Nighttime lights and built-up indices are validated proxies for settlement density and economic activity, which are correlated with where people live and move (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Road networks both generate larval habitats (e.g., puddling, borrowing pits) and facilitate parasite flow via human mobility (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). Access to care also shapes the measured incidence, as areas with more reachable facilities detect a larger share of infections, thereby inflating routine counts relative to underserved areas (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eDespite these advances, critical gaps remain for Bayelsa State as peer-reviewed studies providing longitudinal, state-specific spatiotemporal analyses of confirmed uncomplicated malaria are scarce; environmental drivers have been underexamined; and rigorous comparisons between upland and riverine local government areas (LGAs) are largely absent. Moreover, spatiotemporal analyses leveraging routine DHIS2 data are scarce, and rigorous comparisons between upland and riverine LGAs have seldom been attempted. Moreover, few studies leverage DHIS2 facility data for Bayelsa while explicitly integrating environmental predictors and connectivity metrics, despite growing evidence that such integration strengthens the interpretation of routine surveillance (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). Addressing these gaps, this study examines how confirmed uncomplicated malaria varied across space and time in Bayelsa State from 2017\u0026ndash;2024 and which environmental predictors best explain the observed pattern. We integrate health facility reports with remotely sensed and infrastructural covariates that capture settlement intensity (nighttime lights, built-up extent), greenness (NDVI), hydroclimatic and geomorphic context (precipitation, soil moisture, rivers, elevation), agriculture (cropland), and connectivity (roads). The specific objectives are to (a) quantify temporal trends and spatial heterogeneity in confirmed uncomplicated malaria across LGAs; (b) compare malaria burdens between upland and riverine groups; and (c) identify and rank environmental and infrastructural predictors of LGA-level malaria via complementary statistical approaches.\u003c/p\u003e\u003cp\u003eThis research has both scientific and policy relevance. Specifically, it links routine DHIS2 surveillance to a multisource environmental evidence base, demonstrating how urban expansion, mobility corridors, and agro-hydrological context together structure malaria risk and visibility at the LGA scale (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). Programmatically, identifying when and where confirmed malaria is rising and which environmental indicators influence those increases directly supports subnational stratification, focal vector control, housing and drainage improvements, and strategic surveillance (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). By testing upland\u0026ndash;riverine contrasts, this study also addresses operational questions about prioritizing freshwater inland settlements versus brackish riverine zones.\u003c/p\u003e\u003cp\u003eIn Bayelsa, the juxtaposition of fast-growing upland towns and low-lying, tidal LGAs provides a natural laboratory for integrating environmental predictors with routine data. We leverage validated proxies for settlement density (VIIRS nighttime lights), extent of built-up areas, vegetation (NDVI), and access (roads, healthcare travel time) alongside hydrogeomorphic variables (rivers, elevation, precipitation, and soil moisture) to generate a coherent explanatory framework (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). The resulting spatiotemporal narrative and predictor ranking aim to move Bayelsa\u0026rsquo;s malaria analytics from descriptive counts toward environmentally informed, geographically targeted decision-making.\u003c/p\u003e"},{"header":"2.0 Methodology","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Data Sources\u003c/h2\u003e\u003cp\u003eBayelsa State is one of the states in Nigeria and one of the states in the Niger Delta area of the country. Administratively, it has eight LGAs and 105 local wards (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The LGAs are divided into two categories: four upland and four riverine. The dominant vegetation comprises mangroves and freshwater swamp forests. Data on the annual incidence of confirmed uncomplicated malaria from 2017\u0026ndash;2024 were obtained from Nigeria\u0026rsquo;s National Malaria Elimination Program through the District Health Information System 2 (DHIS2) platform (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). The DHIS2 serves as Nigeria's official national health management information system, capturing comprehensive malaria surveillance data from health facilities across all 36 states and the Federal Capital Territory. The platform provides standardized, real-time reporting of malaria case management, prevention activities, and epidemiological indicators, making it a highly credible and authoritative source for malaria data used by the Nigerian Ministry of Health, WHO, and international research organizations.\u003c/p\u003e\u003cp\u003eData on the potential environmental predictors of confirmed uncomplicated malaria were downloaded from the Google Earth Engine (GEE). The GEE is a cloud-based platform for planetary-scale geospatial analysis that enables Google's massive computational capabilities to address a variety of high-impact societal issues, including deforestation, drought, disaster, disease, food security, water management, climate monitoring and environmental protection (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e) Data obtained from the GEE include the land surface temperature (LST), enhanced vegetation index (EVI), normalized difference vegetation index (NDVI), soil adjusted vegetation index (SAVI), normalized difference built-up index (NDBI), normalized difference water index (NDWI), shuttle radar topographic mapping (SRTM) data, soil moisture, soil pH, monthly precipitation, and VIIRS nighttime lights.\u003c/p\u003e\u003cp\u003eData on built-up areas, water coverage, and cropland coverage were extracted from the Environmental Systems Research Institute (ESRI) Living Atlas database (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). These land use/land cover data were processed from Sentinel-2 and are available at a 10-meter resolution worldwide (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). Administrative boundaries (states and LGAs) were obtained from Grid3.org (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). All raster datasets were projected to a common coordinate system (WGS84 UTM32N). Zonal statistics were used to extract the average of each of the potential parameters for each LGA. The extracted values were combined with the administrative map of the LGAs in Bayelsa State for geospatial analysis and statistical modelling.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Data analysis\u003c/h2\u003e\u003cp\u003eDescriptive analysis was conducted on the confirmed uncomplicated malaria data to understand the spatiotemporal distribution of malaria incidents across LGAs and over time in Bayelsa State. In addition, we calculated the percentage change and the compound annual growth rate of confirmed uncomplicated malaria incidents. For each LGA, we extracted the yearly count of confirmed uncomplicated malaria cases for the years 2017\u0026ndash;2024 from the DHIS2 routine surveillance data. We treated each LGA year as one observation and computed growth rates per LGA over the full interval from 2017\u0026ndash;2024. Compound annual growth requires the number of years between the start and end points of the observations. For the 2017 (baseline) to 2024 (endpoint) window, the growth interval is n\u0026thinsp;=\u0026thinsp;2024\u0026thinsp;\u0026minus;\u0026thinsp;2017\u0026thinsp;=\u0026thinsp;7. For each LGA, we therefore calculated the compound annual growth rate (CAGR) of confirmed uncomplicated malaria as follows:\u003c/p\u003e\u003cp\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"184\" height=\"79\"\u003e\u003c/p\u003e\u003cp\u003e​where V\u003csub\u003ei,2017\u003c/sub\u003e​ and V\u003csub\u003ei,2024\u003c/sub\u003e​ are the baseline and endpoint annual case counts for LGA, respectively. CAGR provides the constant annualized rate that links the beginning and ending values under geometric compounding. This is the same \u0026ldquo;geometric endpoint\u0026rdquo; growth measure widely used in official statistics and international monitoring (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). Because it is geometric, CAGR smooths year-to-year volatility and summarizes long-term changes with a single annualized figure.\u003c/p\u003e\u003cp\u003eWe analyzed routine LGA-level counts of confirmed uncomplicated malaria cases from 2017\u0026ndash;2024, classified each LGA as upland or riverine based on proximity to the coastline, and used one-way ANOVA in SPSS to compare group means. The dataset comprises 8 LGA-year observations (4 upland; 4 riverine). We report group descriptives with 95% confidence intervals (CIs), the ANOVA F test, and effect sizes (etasquared, epsilonsquared, and omegasquared) with CIs. Interpretation follows standard guidance for ANOVA and effect sizes (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAnnual global Moran\u0026rsquo;s I was computed via GeoDa software to assess the spatial autocorrelation of confirmed uncomplicated malaria across Bayelsa\u0026rsquo;s eight LGAs. Moran\u0026rsquo;s I measure the similarity of values among neighboring areal units relative to the overall mean; its slope equals the regression slope in the Moran scatterplot (\u003cspan additionalcitationids=\"CR27\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). GeoDa software typically evaluates significance via permutation testing (e.g., 999 randomizations) to produce a pseudo p value and z score (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). With N\u0026thinsp;=\u0026thinsp;8 LGAs, the expected value of Moran\u0026rsquo;s I under the null hypothesis of spatial randomness is E[I]\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;1/(N\u0026thinsp;\u0026minus;\u0026thinsp;1)\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;1/7\u0026thinsp;\u0026asymp;\u0026thinsp;\u0026minus;\u0026thinsp;0.1429 (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). The values near this benchmark are consistent with spatial randomness.\u003c/p\u003e\u003cp\u003eThe number of confirmed uncomplicated malaria incidents in each LGA was the dependent variable, while the various environmentally related variables constituted the explanatory variables. Pearson correlation analysis was used to investigate the associations between the number of confirmed uncomplicated malaria incidents and potential environmental predictors. The average malaria incidence by LGA was correlated with environmental, infrastructural, and access covariates: VIIRS nightlight radiance, soil moisture, top soil pH (0\u0026ndash;20 cm), SAVI, NDWI, NDVI, NDBI, EVI, land surface temperature (LST), elevation, average daily precipitation, total road length, river length, number of healthcare facilities, area of built-up land, and area of cropland. Pearson r values and 1-tailed p-values were calculated (n\u0026thinsp;=\u0026thinsp;8).\u003c/p\u003e\u003cp\u003eFurthermore, to identify the predictors of confirmed uncomplicated malaria, exploratory and ordinary least squares (OLS) regressions were conducted. The ArcGIS Pro\u0026rsquo;s exploratory regression tool was used to screen combinations of candidate predictors for LGA--level malaria incidence in Bayelsa State and retained models that (a) explained a large share of variance (adjusted R\u003csup\u003e2\u003c/sup\u003e), (b) were parsimonious (lowest Akaike information criterion (AICc), and (c) met OLS diagnostics\u0026mdash;residual normality (Jarque\u0026ndash;Bera, JB), homoscedasticity/stationarity (Koenker\u0026ndash;Breusch\u0026ndash;Pagan, K(BP)), acceptable multicollinearity (maximum VIF\u0026thinsp;\u0026lt;\u0026thinsp;7.5), and no residual spatial autocorrelation (Global Moran\u0026rsquo;s I; SA p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). These diagnostics follow established guidance for spatial regression and model selection (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). In addition, we used the ordinary least squares (OLS) model to fit ArcGIS Pro with malaria counts as the dependent variable and four covariates identified through exploratory regression: VIIRS night-time lights (VIIRS_NIGH), mean NDVI (Mean_NDVI), mean elevation (Elevation), and total river length (Rivers). ArcGIS Pro reports both classical and heteroskedasticity-robust (White) standard errors; when the Koenker\u0026ndash;Breusch\u0026ndash;Pagan test is significant, interpretation should rely on robust p values (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). Multicollinearity was assessed via variance inverse factors (VIFs). The exploratory and OLS regressions were conducted via ArcGIS Pro 3.4 (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e"},{"header":"3.0 Results","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Spatiotemporal Distribution and Patterns of Confirmed Uncomplicated Malaria in Bayelsa State from 2017\u0026ndash;2024\u003c/h2\u003e\u003cp\u003eThe spatial distribution and concentration of confirmed uncomplicated malaria show that, cumulatively, from 2017\u0026ndash;2024, Yenagoa LGA (Bayelsa\u0026rsquo;s State capital) contributed 78,808 of 201,994 cases (39.02%), followed by Sagbama (24,914; 12.16%), southern Ijaw (22,215; 11.00%), Nembe (18,305; 9.06%), Kolokuma/Opokuma (17,920; 8.87%), Ekeremor (14,887; 7.37%), Ogbia (12,779; 6.33%), and Brass (12,526; 6.20%). Annually, Yenagoa\u0026rsquo;s share ranged from 28.25% (2018) to 49.2% (2019) and stood at 39.5% in 2024, demonstrating a persistent concentration of reported burden in that LGA throughout the period (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Yenagoa LGA had the highest confirmed uncomplicated malaria incidence in 2024, with 25,735 cases (39.5% of the state total), followed by Sagbama, with 8,327 cases (12.8%), and southern Ijaw, with 7,379 cases (11.3%). Mid-range counts were observed in Nembe (5,905; 9.1%) and Ogbia (5,844; 9.0%), while Kolokuma/Opokuma (4,582; 7.0%) and Ekeremor (4,426; 6.8%) counts were lower, and Brass (2,951; 4.5%) reported the lowest counts. All the LGAs reached their highest values in 2024. Relative to 2017, the rank positions in 2024 were stable at the top (Yenagoa first, Sagbama second, Southern Ijaw third), with upwards movement for Nembe (eighth to fourth) and Ogbia (seventh to fifth), and a decline for Brass (fourth to eighth) and Ekeremor (fifth to seventh).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eConfirmed incidence of uncomplicated malaria across local government areas of Bayelsa State from 2017\u0026ndash;2024.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"10\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLGA\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2017\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2018\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2019\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2020\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2021\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2022\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2023\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003e2024\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBrass\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1159\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1595\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1201\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1264\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1851\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1221\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1284\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e2951\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e12526\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEkeremor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e972\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1567\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1550\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e965\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1274\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1782\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e2351\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e4426\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e14887\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKolokuma/Opokuma\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e506\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1384\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2188\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1357\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2043\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e2269\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e3591\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e4582\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e17920\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNembe\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e256\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1247\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1392\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1641\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1441\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e2734\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e3689\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e5905\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e18305\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOgbia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e505\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e858\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e671\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e371\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e853\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1030\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e2647\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e5844\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e12779\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSagbama\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1879\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2475\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1956\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2040\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1590\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e2014\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e4273\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e8327\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e24554\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSouthern Ijaw\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1541\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2138\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1637\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1195\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1888\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e3021\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e3416\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e7379\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e22215\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYenagoa\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3927\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4434\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e10272\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e7341\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e4979\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e7729\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e14391\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e25735\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e78808\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e10745\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e15698\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e20867\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e16174\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e15919\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e21800\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e35642\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e65149\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e202,354\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"10\"\u003eSource: DHIS2\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe temporal trend in confirmed uncomplicated malaria incidence at the state level revealed that the number of cases rose from 10,745 in 2017 to 65,149 in 2024. This represents a 6.06-fold increase, corresponding to a compound annual growth rate (CAGR) of 29.4% (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The annual totals and year-to-year (YoY) changes were as follows: 2017, 10,745; 2018, 15,698 (+\u0026thinsp;46.1%); 2019, 20,867 (+\u0026thinsp;32.9%); 2020, 16,174 (\u0026minus;\u0026thinsp;22.5%); 2021, 16,279 (+\u0026thinsp;0.6%); 2022, 21,800 (+\u0026thinsp;33.9%); 2023, 35,642 (+\u0026thinsp;63.5%); and 2024, 65,149 (+\u0026thinsp;82.8%) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The period was characterised by an increase in the incidence of confirmed uncomplicated malaria from 2018\u0026ndash;2019, a clear reduction in 2020, a minimal net change in 2021, recovery in 2022, and a step change beginning in 2023 that accelerated in 2024. The total of 2024 was 3.85 times greater than the average annual total from 2017\u0026ndash;2022 (16,927).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eYear-over-Year Changes (%) in Confirmed Uncomplicated Malaria Across LGAs in Bayelsa State\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"9\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eLGA\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"7\" nameend=\"c8\" namest=\"c2\"\u003e\u003cp\u003eYear-over-Year Change (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eAvg Annual Change\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2017-18\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2018-19\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2019-20\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2020-21\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2021-22\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2022-23\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2023-24\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBrass\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e37.60%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-24.70%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5.20%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e46.40%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-34.00%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e5.20%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e129.80%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e23.70%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEkeremor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e61.20%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-1.10%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-37.70%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e32.00%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e39.90%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e31.90%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e88.30%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e30.60%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKolokuma/Opokuma\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e173.50%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e58.10%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-38.00%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e50.60%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e11.10%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e58.30%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e27.60%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e48.70%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNembe\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e387.10%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e11.60%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e17.90%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-12.20%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e89.70%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e34.90%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e60.10%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e84.20%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOgbia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e69.90%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-21.80%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-44.70%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e129.90%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e20.80%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e157.00%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e120.80%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e61.70%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSagbama\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e31.70%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-21.00%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.30%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-22.10%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e26.70%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e112.20%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e94.90%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e32.40%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSouthern Ijaw\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e38.70%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-23.40%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-27.00%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e58.00%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e60.00%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e13.10%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e116.00%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e33.60%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYenagoa\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e12.90%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e131.70%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-28.50%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-32.20%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e55.20%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e86.20%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e78.80%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e43.40%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e46.10%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e32.90%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-22.50%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-1.60%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e36.90%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e63.50%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e82.80%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e34.00%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eAt the LGA level, the data revealed that confirmed uncomplicated malaria increased by 2206.64% in Nembe, 1057.23% in Ogbia, 805.53% in Kolokuma/Opokuma, 555.33% in Yenagoa, 378.84% in southern Ijaw, 355.35% in Ekeremor, 343.16% in Sagbama, and 154.62% in Brass LGAs from 2017\u0026ndash;2024. The corresponding approximate CAGRs were as follows: Nembe, 56.5%; Ogbia, 41.9%; Kolokuma/Opokuma, 37.0%; Yenagoa, 30.8%; southern Ijaw, 25.1%; Ekeremor, 24.2%; Sagbama, 23.7%; and Brass, 14.3% (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The 2024 surge relative to each LGA\u0026rsquo;s 2017\u0026ndash;2022 median was highest in Ogbia (7.67 times), followed by a cluster of approximately fourfold in Sagbama (4.20 times), southern Ijaw (4.19 times), Yenagoa (4.18 times), and Nembe (4.17 times). Ekeremor (3.14 times) and Kolokuma/Opokuma (2.67 times) were moderate, and Brass (2.37 times) presented the smallest relative increase.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAnnual Changes (%) in Confirmed Uncomplicated Malaria Across LGAs in Bayelsa State\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLGA\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2017 (Start)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2024 (End)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eGrowth Ratio\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eCAGR (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eClassification\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBrass\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1,159\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2,951\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e14.30%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eModerate Growth\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEkeremor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e972\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4,426\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e24.20%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eHigh Growth\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKolokuma/Opokuma\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e506\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4,582\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e9.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e37.00%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eVery High Growth\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNembe\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e256\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5,905\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e23.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e56.60%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eVery High Growth\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOgbia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e505\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5,844\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e11.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e41.90%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eVery High Growth\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSagbama\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1,879\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8,327\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e23.70%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eHigh Growth\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSouthern Ijaw\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1,541\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7,379\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e25.10%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eVery High Growth\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYenagoa\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3,927\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e25,735\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e30.80%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eVery High Growth\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e10,745\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e65,149\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e29.40%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eVery High Growth\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003eNote: Growth Classification Legend: Very High Growth: CAGR\u0026thinsp;\u0026gt;\u0026thinsp;25%; High Growth: 15% \u0026lt; CAGR\u0026thinsp;\u0026le;\u0026thinsp;25%; Moderate Growth: 5% \u0026lt; CAGR\u0026thinsp;\u0026le;\u0026thinsp;15%; Low/Decline: CAGR\u0026thinsp;\u0026le;\u0026thinsp;5%\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe year-specific patterns revealed that the 2019 state-level rise was strongly influenced by Yenagoa, which accounted for 49.2% of the cases that year (10,272 of 20,867). The 2020 decline was broad-based (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), with the steepest LGA-level declines from 2019\u0026ndash;2020 in Ogbia (\u0026minus;\u0026thinsp;44.7%; 671\u0026ndash;371), Kolokuma/Opokuma (\u0026minus;\u0026thinsp;37.9%; 2,188\u0026ndash;1,357), Ekeremor (\u0026minus;\u0026thinsp;37.7%; 1,550\u0026ndash;965), Yenagoa (\u0026minus;\u0026thinsp;28.5%; 10,272\u0026ndash;7,341), and southern Ijaw (\u0026minus;\u0026thinsp;27.0%; 1,637\u0026ndash;1,195). The subsequent increase was pronounced in 2022\u0026ndash;2023 in Ogbia (+\u0026thinsp;157%; 1,030 to 2,647), Sagbama (+\u0026thinsp;112%; 2,014 to 4,273), Yenagoa (+\u0026thinsp;86%; 7,729 to 14,391), Kolokuma/Opokuma (+\u0026thinsp;58%; 2,269 to 3,591), Ekeremor (+\u0026thinsp;32%; 1,782 to 2,351), Nembe (+\u0026thinsp;35%; 2,734 to 3,689), southern Ijaw (+\u0026thinsp;13%; 3,021 to 3,416), and Brass (+\u0026thinsp;5%; 1,221 to 1,284). It intensified again from 2023\u0026ndash;2024: Brass (+\u0026thinsp;130%; 1,284\u0026ndash;2,951), Ogbia (+\u0026thinsp;121%; 2,647\u0026ndash;5,844), southern Ijaw (+\u0026thinsp;116%; 3,416\u0026ndash;7,379), Sagbama (+\u0026thinsp;95%; 4,273\u0026ndash;8,327), Ekeremor (+\u0026thinsp;88%; 2,351\u0026ndash;4,426), Yenagoa (+\u0026thinsp;79%; 14,391\u0026ndash;25,735), Nembe (+\u0026thinsp;60%; 3,689\u0026ndash;5,905), and Kolokuma/Opokuma (+\u0026thinsp;28%; 3,591\u0026ndash;4,582).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003ePercentage change in confirmed uncomplicated malaria\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLGA\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2017\u0026ndash;2018\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2018\u0026ndash;2019\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2019\u0026ndash;2020\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2020\u0026ndash;2021\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2021\u0026ndash;2022\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2022\u0026ndash;2023\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2023\u0026ndash;2024\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBrass\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e37.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-24.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e46.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-34.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e5.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e129.83\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEkeremor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e61.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-1.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-37.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e32.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e39.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e31.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e88.26\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKolokuma/Opokuma\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e173.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e58.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-37.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e50.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e11.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e58.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e27.60\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNembe\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e387.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e11.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e17.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-12.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e89.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e34.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e60.07\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOgbia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e69.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-21.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-44.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e129.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e20.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e156.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e120.78\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSagbama\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e31.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-20.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-22.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e26.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e112.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e94.87\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSouthern Ijaw\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e38.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-23.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-27.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e57.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e60.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e13.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e116.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYenagoa\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e12.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e131.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-28.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-32.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e55.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e86.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e78.83\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Comparative Analysis of Confirmed Uncomplicated Malaria between Upland and Riverine LGAs in Bayelsa State\u003c/h2\u003e\u003cp\u003eBayelsa State lies within the Niger Delta, where malaria transmission is perennial but ecologically heterogeneous. Inland/upland LGAs are dominated by freshwater systems, while riverine/mangrove zones experience tidal influence and brackish conditions. Because dominant vectors such as \u003cem\u003eAnopheles gambiae sensu stricto\u003c/em\u003e and \u003cem\u003eAnopheles. coluzzii\u003c/em\u003e prefer sunlit, shallow freshwater, whereas brackish habitats are more suitable for more localized \u003cem\u003eAnopheles. melas\u003c/em\u003e, differences in landscape settings may translate into measurable differences in malaria burden (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). We assessed whether confirmed uncomplicated malaria incidence rates differ between upland and riverine LGAs from 2017\u0026ndash;2024.\u003c/p\u003e\u003cp\u003eAcross the eight LGAs, from 2017\u0026ndash;2024, malaria counts were higher in upland LGAs than in riverine LGAs and showed markedly greater dispersion in upland settings. Upland LGAs (n\u0026thinsp;=\u0026thinsp;4) reported a mean of 4,189 cases per LGA per year (SD\u0026thinsp;=\u0026thinsp;5,064; SE\u0026thinsp;=\u0026thinsp;895), with a 95% confidence interval (CI) for the mean of 2,363-6,015 and a range of 371\u0026thinsp;\u0026minus;\u0026thinsp;25,735. Riverine LGAs (n\u0026thinsp;=\u0026thinsp;4) averaged 2,123 cases (SD\u0026thinsp;=\u0026thinsp;1,490; SE\u0026thinsp;=\u0026thinsp;263), 95% CI 1,586\u0026ndash;2,660, ranging from 256\u0026ndash;7,379 (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The mean difference was 2,067 cases, and the ratio of group means was approximately 1.97 (upland\u0026thinsp;\u0026asymp;\u0026thinsp;twice riverine). Relative variability was substantially greater in upland LGAs (coefficient of variation\u0026thinsp;\u0026asymp;\u0026thinsp;121%) than in riverine LGAs (\u0026asymp;\u0026thinsp;70%), reflecting heterogeneous burdens and occasional high-count years in upland areas. Overall, across all observations, the mean was 3,156 cases (SD\u0026thinsp;=\u0026thinsp;3,847; 95% CI 2,195\u0026ndash;4,117; range 256\u0026ndash;25,735). Upland LGAs report higher rates of confirmed uncomplicated malaria than Riverine LGAs do in Bayelsa State (2017\u0026ndash;2024).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDescriptive Comparison of Malaria Incidence between Upland and Riverine LGAs in Bayelsa State\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"9\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eMean\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eStd. Deviation\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eStd. Error\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e95% Confidence Interval for Mean\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eMinimum\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eMaximum\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eLower Bound\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eUpper Bound\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUpland\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4189.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5064.530\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e895.291\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2363.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e6015.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e371\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e25735\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRiverine\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2122.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1489.519\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e263.312\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1585.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e2659.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e256\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e7379\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3156.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3846.745\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e480.843\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2195.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e4117.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e256\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e25735\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eA one-way ANOVA was used to test whether the mean number of confirmed uncomplicated malaria cases differed between upland and riverine LGAs (2017\u0026ndash;2024). The analysis revealed a statistically significant group effect (F\u003csub\u003e(1, 62)\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;4.904, p\u0026thinsp;=\u0026thinsp;0.030) (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Thus, the upland and riverine LGAs do not have the same average malaria counts. Given the descriptive results reported earlier (upland mean\u0026thinsp;\u0026asymp;\u0026thinsp;4,189 vs riverine mean\u0026thinsp;\u0026asymp;\u0026thinsp;2,123) (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e), the direction of the effect indicates higher counts in upland LGAs.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eANOVA of Confirmed Uncomplicated Malaria between Inland and Riverine LGAs in Bayelsa State\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSum of Squares\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDf\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMean Square\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eF\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eSig.\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBetween Groups\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e68326756.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e68326756.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e4.904\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.030\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWithin Groups\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e863912188.438\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e13934067.555\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e932238944.438\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eAccording to the ANOVA results (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e), the between-group mean square (68,326,756) is approximately 4.9 times greater than the within-group mean square (13,934,067.56), producing the observed F ratio (4.904). In terms of practical importance, effect size estimates computed for this comparison indicate that the group factor accounts for approximately 6\u0026ndash;7% of the total variance (η\u0026sup2; \u0026asymp; 0.073; ω\u0026sup2; \u0026asymp; 0.057), representing a small to moderate effect by conventional benchmarks.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Pattern of Confirmed Uncomplicated Malaria among LGAs in Bayelsa from 2017\u0026ndash;2024\u003c/h2\u003e\u003cp\u003eAll Moran\u0026rsquo;s I coefficients are negative, indicating that the global pattern of confirmed uncomplicated malaria is one of spatial dispersion (i.e., a tendency for dissimilar values to be neighbours, a \u0026ldquo;checkerboard\u0026rdquo; pattern) rather than clustering (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). The coefficients are small in absolute value (|I| \u0026lt; 0.20), and crucially, they hover around the null expectation for N\u0026thinsp;=\u0026thinsp;8 (\u0026thinsp;\u0026asymp;\u0026thinsp;\u0026minus;\u0026thinsp;0.1429). The Moran\u0026rsquo;s I was very close to the null value in 2017 (\u0026minus;\u0026thinsp;0.141), 2020 (\u0026minus;\u0026thinsp;0.149), and 2021 (\u0026minus;\u0026thinsp;0.131), while the Moran\u0026rsquo;s I was more negative than the null value in 2022. The most dispersed year was 2022 (\u0026minus;\u0026thinsp;0.168), while the year 2023 (\u0026minus;\u0026thinsp;0.069) indicates the weakest negative autocorrelation. There is mild interannual fluctuation with a trough (mostly negative) in 2022, followed by a rebound toward zero in 2023 and a slight decrease in 2024 (Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). No monotonic trend is evident. Given the small number of LGAs (N\u0026thinsp;=\u0026thinsp;8) and the proximity of most estimates to the null expectation (\u0026thinsp;\u0026asymp;\u0026thinsp;\u0026minus;\u0026thinsp;0.143), the global pattern is most consistent with spatial randomness in many years (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e)\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMoran\u0026rsquo;s I statistics for confirmed uncomplicated malaria incidence from 2017\u0026ndash;2024 among LGAs in Bayelsa State, Nigeria.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"9\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYear\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2017\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2018\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2019\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2020\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2021\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2022\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2023\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003e2024\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMoran\u0026rsquo;s I\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.141\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.122\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.149\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.131\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.168\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-0.069\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-0.099\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eNigeria remains a high burden setting where malaria risk is heterogeneous over short distances due to environmental, infrastructural, and intervention coverage gradients (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). Such heterogeneity can generate local clusters even when the statewide (global) pattern appears randomly. Across 2017\u0026ndash;2024, confirmed uncomplicated malaria in Bayelsa State exhibited weak negative global spatial autocorrelation, with coefficients clustered around the null expectation for the eight LGAs. The statewide pattern is largely consistent with spatial randomness, punctuated by year-to-year fluctuations.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e3.4 Association between Malaria Incidence and Environmental Predictors\u003c/h2\u003e\u003cp\u003eMalaria transmission in southern Nigeria is perennial and intense, driven by efficient Anopheles vectors and a warm, humid deltaic environment (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). While climate sets broad suitability, within-state heterogeneity often reflects how people live, move, and access care (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). We examined LGA-level correlations between average malaria incidence and environmental/infrastructural covariates in Bayelsa to understand which landscape features align with the observed patterns.\u003c/p\u003e\u003cp\u003eBayelsa\u0026rsquo;s Niger Delta ecology ensures perennial malaria receptivity, with \u003cem\u003eAnopheles gambiae s.l.\u003c/em\u003e and \u003cem\u003eAnopheles funestus\u003c/em\u003e sustained by warm temperatures and high humidity (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e). Within-state heterogeneity is therefore expected to reflect human environmental modification, population aggregation, mobility, and differential access to testing and care (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). The built environment indicators, such as the area of built-up land (r\u0026thinsp;=\u0026thinsp;0.954, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), exhibited the strongest positive correlations with confirmed uncomplicated malaria. Nightlights (r\u0026thinsp;=\u0026thinsp;0.706, p\u0026thinsp;=\u0026thinsp;0.025) supported this pattern, which is consistent with these variables acting as complementary proxies for settlement density and urban/peri-urban expansion (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e). There was also a strong positive association between the incidence of confirmed uncomplicated malaria and the length of roads in each LGA (r\u0026thinsp;=\u0026thinsp;0.912, p\u0026thinsp;=\u0026thinsp;0.001, r\u0026sup2;\u0026asymp;0.83). Roads both concentrate people and create larval habitats through borrow pits, drainage failures, and puddling along verges; they also facilitate parasite flow via human movement (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). The number of confirmed uncomplicated malaria incidents was also positively correlated with the number of healthcare facilities (r\u0026thinsp;=\u0026thinsp;0.808, p\u0026thinsp;=\u0026thinsp;0.008). This likely reflects detection/reporting intensity, such that LGAs with more facilities test and report more, inflating the observed incidence relative to areas with poorer access (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). This may also reflect the rational allocation of facilities to higher burden LGAs. The number of confirmed uncomplicated malaria incidents was also positively correlated with the area of cropland (r\u0026thinsp;=\u0026thinsp;0.711, p\u0026thinsp;=\u0026thinsp;0.024). Agricultural landscapes, especially low-lying and poorly drained fields, irrigation ditches, ponds, and wheel ruts, frequently generate sunlit, shallow water bodies favored by major vectors (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). The vegetation indices (SAVI, r\u0026thinsp;=\u0026thinsp;0.108; NDVI, r\u0026thinsp;=\u0026thinsp;0.162; EVI, r\u0026thinsp;=\u0026thinsp;0.142) and NDWI (r\u0026thinsp;=\u0026thinsp;0.129) showed little explanatory power. LST (r\u0026thinsp;=\u0026thinsp;0.108) and precipitation (r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.250) were also weak. Elevation (r\u0026thinsp;=\u0026thinsp;0.399) and soil pH (r\u0026thinsp;=\u0026thinsp;0.586, p\u0026thinsp;=\u0026thinsp;0.063) tended to increase but were not significant. River length was weakly negative (r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.284). In Bayelsa\u0026rsquo;s consistently warm, rainy environment, climatic gradients between LGAs are modest and remain near the thermal optimum for \u003cem\u003ePlasmodium falciparum\u003c/em\u003e transmission, limiting their explanatory value at this spatial scale (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). Large rivers and tidal channels are typically poor larval habitats compared with small, sunlit, stagnant pools (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e3.5 Environmental Predictors of Confirmed Uncomplicated Malaria in Bayelsa State\u003c/h2\u003e\u003cp\u003eThe exploratory regression analysis yielded three potential models for explaining variations in confirmed uncomplicated malaria among LGAs in Bayelsa State. These candidate models had very high fits (AdjR\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026ge;\u0026thinsp;0.97) and passed key diagnostics (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). AICc differences and diagnostics, however, identify Model 1 as the strongest, with Model 2 being a plausible alternative and Model 3 receiving comparatively less support. Model 1 (best-supported) identified increased VIIRS Nighttime Light (+), reduced NDVI (-), increased Elevation (+) and increased length of streams/rivers (+) as the most likely predictors of confirmed uncomplicated malaria among LGAs in Bayelsa State (Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eHighest adjusted R-square results from the exploratory regression analysis\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAdjR2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAICc\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eJB\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eK(BP)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eVIF\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eSA\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eModel\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e0.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e204.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e6.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e+VIIRS_Night*** -Mean_NDVI_*** +Elevation_*** +Rivers***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e0.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e207.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e5.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-Soil Moisture*** +Elevation_** +Avg_Precip** +Built-up***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e0.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e208.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e+VIIRS_Night*** +Mean_Lst_2*** +Rivers** +Cropland***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe VIIRS nighttime lights (positive) proxy settlement density, electrification, and economic activity. Brighter, denser LGAs typically exhibit more peri-urban habitats (blocked drains, borrow pits, water containers) that support \u003cem\u003eAnopheles gambiae s.l.\u003c/em\u003e, sustaining focal transmission (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e). The negative NDVI suggests that greener and more vegetated LGAs are less prone to malaria at this scale, which is consistent with the preference of dominant West African vectors for small, sunlit, shallow freshwater pools rather than shaded habitats. Increased greenness often corresponds to canopy covers or wetlands that are less productive for these vectors (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). The positive elevation aligns with Bayelsa\u0026rsquo;s ecology. The slightly higher inland/upland LGAs provide fresher water bodies, whereas low-lying, saline/brackish tidal zones are less suitable for \u003cem\u003eAnopheles. gambiae s.s./Anopheles. coluzzii\u003c/em\u003e; coastal \u003cem\u003eAnopheles. melas\u003c/em\u003e is more saline-tolerant but have a narrower distribution (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). Additionally, the positive relationship between the length of rivers/streams likely captures floodplain edges, borrow pits, and human settlements along waterways, where overbank flooding and poor drainage create discrete larval habitats and increase exposure (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). This model combines the lowest AICc with a very strong fit and acceptable diagnostics, indicating the best trade-off between explanatory power and parsimony (Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). Its predictors also match established transmission mechanisms: anthropogenic settlement intensity coupled with freshwater availability.\u003c/p\u003e\u003cp\u003eThe second model is ecologically coherent and statistically sound, but its higher AICc provides less support than Model 1 does. Using Akaike weights, Model 1 receives\u0026thinsp;~\u0026thinsp;72% of the support, and Model 2 receives\u0026thinsp;~\u0026thinsp;19%. Model 3 diagnostics are strong, and multicollinearity is minimal, but the higher AICc and slightly lower fit make this model less competitive (Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eNighttime lights and built-up areas consistently appear in the best models, indicating that settlement density, nighttime lights, and rapid peri-urban growth structure malaria risk, likely via the creation of artificial breeding sites and increased human‒vector contact (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e). Hydroclimate factors, such as soil moisture, precipitation, rivers, and cropland, confirm that water availability and management affect habitat persistence, especially in floodplain/upland mosaics typical of Bayelsa (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). The nonsignificant Moran\u0026rsquo;s I on residuals (SA p\u0026thinsp;\u0026ge;\u0026thinsp;0.32) indicates no remaining unmodelled spatial structure, while the Koenker test p\u0026thinsp;\u0026gt;\u0026thinsp;0.1 suggests that relationships are spatially stable at the LGA scale and that the VIF values are within accepted limits, indicating manageable collinearity (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe variables identified by the exploratory regression were further analyzed OLS regression in ArcGIS Pro software to identify the contributions of the identified explanatory factors to malaria incidence in Bayelsa State. All four predictors in Model 1 are highly significant under both classical and robust inference (all p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), indicating stable associations even if the residual variance is nonconstant. The variance inverse factors (VIF) values are acceptable: 1.19 (VIIRS), 3.44 (NDVI), 6.19 (elevation), and 2.73 (river) (Table\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e9\u003c/span\u003e). Although elevation shows moderate collinearity, it remains below the common thresholds (\u0026le;\u0026thinsp;7.5) recommended for ArcGIS OLS (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e) Additionally, because covariates are at different scales, the relative importance is best gauged by t-statistics. With respect to robust t values, the variable influence ranks were as follows: VIIRS (25.86)\u0026thinsp;\u0026gt;\u0026thinsp;Elevation (18.89)\u0026thinsp;\u0026gt;\u0026thinsp;NDVI (|12.88|)\u0026thinsp;\u0026gt;\u0026thinsp;Rivers (11.23) (Table\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e9\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab9\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 9\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSummary of OLS Results\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"9\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCoefficienta\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eStdError\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003et-Statistic\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eProbabilityb\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eRobust_SE\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eRobust_t\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eRobust_Prb\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eVIFc\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIntercept\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-5713.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1055.711\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-5.41181\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.000033*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e445.1671\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-12.8341\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.000000*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e--------\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVIIRS Nighttime Light\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e10880.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e608.2618\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e17.8878\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.000000*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e420.7523\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e25.85955\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.000000*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.194224\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNDVI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-48331.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5441.864\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-8.88148\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.000000*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e3752.763\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-12.879\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.000000*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e3.440061\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eElevation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e817.857\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e59.59614\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e13.72332\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.000000*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e43.30437\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e18.88625\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.000000*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e6.189371\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLength of Stream/Rivers\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.383418\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.375365\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e9.013671\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.000000*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.301398\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e11.22575\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.000000*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e2.729606\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe VIIRS nighttime light data show a strong positive association with confirmed uncomplicated malaria across the LGAs in Bayelsa State from 2017\u0026ndash;2024 (β\u0026thinsp;=\u0026thinsp;10,880.47; robust p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; VIF 1.19). Nighttime light radiance is a validated proxy for settlement density, electrification, and economic activity (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e). Brighter LGAs are typically more urban/peri-urban, where rapid expansion, poor drainage, construction of burrow pits, and container storage create numerous sunlit freshwater habitats near households. These settings sustain \u003cem\u003eAnopheles gambiae\u003c/em\u003e. and maintain focal transmission despite some urbanization-related protection (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). The NDVI (β = \u0026minus;48,331.81; robust p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; VIF 3.44) also showed a strong negative association with confirmed uncomplicated malaria. A higher NDVI indicates denser/greener vegetation, which often corresponds to shaded, forested, or permanently inundated environments that are less favorable for the sunlit, shallow pools preferred by dominant West African vectors (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) Studies repeatedly show that small, open, human-made or modified water bodies in more sparsely vegetated peri-urban/agricultural mosaics are productive larval habitats (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). Elevation was also positively associated with confirmed uncomplicated malaria (β\u0026thinsp;=\u0026thinsp;817.86; robust p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; VIF 6.19). Within Bayelsa\u0026rsquo;s low relief delta, slightly greater inland/upland areas are less brackish and provide fresher breeding waters favoured by \u003cem\u003eAnopheles. gambiae s.s./Anopheles. coluzzii\u003c/em\u003e, whereas the lowest coastal/mangrove zones are more saline and better suited to \u003cem\u003eAnopheles. melas\u003c/em\u003e, which is geographically restricted (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). This gradient is consistent with greater receptivity away from tidal marshes. The length of streams/rivers was positively associated with confirmed uncomplicated malaria (β\u0026thinsp;=\u0026thinsp;3.38; robust p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; VIF\u0026thinsp;=\u0026thinsp;2.73). River networks and their floodplains generate abundant fringe habitats, such as burrow pits and post flood residual pools, while also concentrating settlements and human activity along banks. These conditions increase both larval habitat availability and human\u0026ndash;vector contact (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e"},{"header":"4.0 Discussion","content":"\u003cp\u003eIn this study, we analyzed the spatiotemporal dynamics of confirmed uncomplicated malaria, its pattern and predictors among LGAs in Bayelsa State, Nigeria, and found that malaria incidents are highly heterogeneous among LGAs and that there has been a high incidence since 2023. The spatial pattern of malaria incidence was dispersed, and no clustering was observed based on the results from Moran's I. Although malaria incidents were significantly correlated with built environment indicators such as the area of built-up land, nighttime light intensity, length of roads, number of healthcare facilities, and area of cropland, only increased VIIRS nighttime light, reduced NDVI, increased elevation and increased length of rivers/streams were predictors of confirmed uncomplicated malaria in the state.\u003c/p\u003e\u003cp\u003eUsing DHIS2 routine data, we observed three consistent patterns in Bayelsa State\u0026rsquo;s confirmed uncomplicated malaria burden. There is a sharp inflexion beginning in 2023 and accelerating through 2024. In routine surveillance, abrupt rises can reflect true increases in transmission, step changes in access to and uptake of testing, improvements in reporting completeness, or combinations thereof; disentangling these drivers requires triangulation with testing volumes, test positivity, stockout logs, and timeliness/completeness indicators (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e). The dip in 2020, followed by a rebound in 2021\u0026ndash;2022, is temporally consistent with broad COVID\u0026ndash;19\u0026ndash;related disruptions to malaria services across sub-Saharan Africa and subsequent restoration of care seeking and diagnostics (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e), reinforcing the need for programmatic indicators when interpreting DHIS2 counts.\u003c/p\u003e\u003cp\u003eThe analysis of year-over-year malaria case trends reveals alarming patterns that may demand immediate public health attention. The data show an escalating malaria burden that has reached crisis proportions by 2024, as indicated by the dramatic surge experienced across all LGAs from 2023\u0026ndash;2024, with some LGAs more than doubling their caseloads. Yenagoa, the state capital, saw cases jump by 78.8% from 14,391 to 25,735, Ogba experienced an even more severe 120.8% increase from 2,647 to 5,844 cases. Brass recorded the highest proportional increase at 129.8%, with cases rising from 1,284 to 2,951, and Sagbama showed a substantial 94.9% jump from 4,273 to 8,327 cases. The data also reveals volatility in malaria reporting patterns. Yenagoa demonstrated the most erratic trajectory, swinging dramatically from a 131.8% increase between 2018 and 2019 to a sharp 28.5% decline the following year. Similarly, Nembe experienced explosive early growth, with a staggering 387.1% increase from 2017\u0026ndash;2018, highlighting the unpredictable nature of malaria transmission dynamics in these LGAs. While 2024 was the peak in every LGA, the magnitude of change varied markedly: Nembe showed the largest long-term increase relative to 2017 (\u0026asymp;\u0026thinsp;23.1\u0026times;), and Ogbia recorded the sharpest surge relative to its 2017\u0026ndash;2022 median (\u0026asymp;\u0026thinsp;7.67\u0026times;). Brass and Kolokuma/Opokuma rose less steeply, which could reflect genuinely lower burdens or under detection scenarios with very different operational implications (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). Together, these findings argue for maintaining high-intensity coverage and surveillance in Yenagoa, Sagbama, and southern Ijaw while conducting focused assessments in Ogbia and Nembe to understand the rapid recent increases.\u003c/p\u003e\u003cp\u003eSeveral challenging trends emerged from longitudinal analysis. The year 2020 marked a period of disruption across multiple LGAs, with mixed results that likely reflect the impact of COVID-19 on health reporting systems and healthcare access. However, the 2022\u0026ndash;2024 period shows a consistent pattern of accelerating growth across all LGAs, with average annual increases ranging from 28% to over 50% for most LGAs. This sustained upwards trajectory suggests systemic challenges in malaria control efforts. The total system impact reflects a sobering picture of public health deterioration. Across all eight LGAs, malaria cases increased from 10,745 in 2017 to 65,149 in 2024, representing a more than six-fold increase over seven years. The particularly sharp increases observed in the final two years of the study period raise critical questions about whether this trend reflects improved surveillance systems that ultimately capture the true disease burden or represent a genuine escalation in malaria transmission that requires urgent intervention from state and federal health authorities.\u003c/p\u003e\u003cp\u003eThe inter-LGA differences align strongly with anthropogenic and agroecological correlates. The extent of built-up land is strongly and positively associated with confirmed malaria (r\u0026thinsp;=\u0026thinsp;0.954, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and it covaries with the NDBI (r\u0026thinsp;=\u0026thinsp;0.967, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), total road length (r\u0026thinsp;=\u0026thinsp;0.982, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), number of healthcare facilities (r\u0026thinsp;=\u0026thinsp;0.815, p\u0026thinsp;=\u0026thinsp;0.007), and cropland area (r\u0026thinsp;=\u0026thinsp;0.709, p\u0026thinsp;=\u0026thinsp;0.025). VIIRS nighttime light (NTL), a validated proxy for settlement density and economic activity, shows an equally strong association with malaria (r\u0026thinsp;=\u0026thinsp;0.964, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e). These tightly linked indicators capture a common \u0026ldquo;settlement intensity/connectivity\u0026rdquo; dimension that plausibly raises malaria risk through several mechanisms.\u003c/p\u003e\u003cp\u003eUrban and peri-urban expansion can create abundant, sunlit, shallow freshwater habitats such as blocked drains, construction of burrow pits, tire ruts, and water storage containers close to households. These habitats are highly productive for the dominant West African vectors \u003cem\u003eAnopheles gambiae s.s.\u003c/em\u003e and \u003cem\u003eAnopheles. coluzzii\u003c/em\u003e, sustaining focal transmission even as some features of (e.g., improved housing) may reduce risk on average (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e). The strong agreement between the mapped built-up area and the NDBI further supports the validity of these remotely sensed settlement proxies (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e). NTL\u0026rsquo;s correlation with roads and services is also programmatically meaningful: electrified, economically active corridors tend to be well connected by roads, which generate larval habitats by puddling and drainage failures and facilitate parasite movement through human mobility (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e). Artificial light at night may additionally shift mosquito and human activity patterns in ways that increase evening biting opportunities (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe number of healthcare facilities is a second, complementary pathway. Confirmed uncomplicated malaria incidents were positively associated with the number of healthcare facilities (r\u0026thinsp;=\u0026thinsp;0.808, p\u0026thinsp;=\u0026thinsp;0.008). The number of healthcare facilities covaries with road length (r\u0026thinsp;=\u0026thinsp;0.831, p\u0026thinsp;=\u0026thinsp;0.005) and built-up area (r\u0026thinsp;=\u0026thinsp;0.815, p\u0026thinsp;=\u0026thinsp;0.007), reflecting siting along populated, accessible corridors (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e). Where facilities are numerous, a larger share of infections is tested and reported, inflating the measured incidence relative to areas with poorer access, which is a well-documented surveillance effect in programmatic malaria data (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e). Accordingly, some of the strong positive associations with built environment proxies and NTL likely reflect both ecological risk and enhanced case detection.\u003c/p\u003e\u003cp\u003eAgriculture and elevation add another layer. Cropland area is positively correlated with malaria (r\u0026thinsp;=\u0026thinsp;0.711, p\u0026thinsp;=\u0026thinsp;0.024) and is more extensive at higher elevations in Bayelsa, suggesting a concentration away from tidally influenced, brackish mangrove zones. Upland croplands and farmadjacent drainage features provide the clear, sunlit freshwater microhabitats preferred by \u003cem\u003eAnopheles. gambiae s.s./Anopheles. coluzzii\u003c/em\u003e; in contrast, brackish habitats of coastal marshes are less suitable for these vectors, with the salt-tolerant \u003cem\u003eAn. melas\u003c/em\u003e has a more restricted coastal distribution (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). Urban/peri-urban agriculture is repeatedly associated with higher vector densities and local transmission in African cities, echoing the patterns we observe here (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e). Roads link these agroecological patches, enabling both habitat creation (e.g., borrow pits) and parasite flow via mobility (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe Bayelsa data, therefore, suggest that a constellation of settlement-related indicators (built-up extent, VIIR), connectivity (roads), health-service availability (number of healthcare facilities), and freshwater- favorable land use (cropland, upland locations) shape where malaria is most frequently detected. These are correlations rather than causal estimates, but they are congruent with established mechanisms linking urban/peri-urban growth, mobility, and agriculture to vector ecology and transmission dynamics (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e). Programmatically, these factors argue for (i) sustained high-intensity coverage in Yenagoa and other bright, highly connected upland LGAs; (ii) targeted environmental management of \u0026ldquo;few, fixed, and findable\u0026rdquo; habitats in construction zones, roadside corridors, and farm edges; and (iii) routine triangulation of DHIS2 counts with testing volume, test positivity, supply chain, and reporting completeness to separate true epidemiologic shifts from surveillance artefacts (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e).\u003c/p\u003e"},{"header":"5.0 Conclusion","content":"\u003cp\u003eFrom 2017 to 2024, Bayelsa State experienced a sharp increase in confirmed uncomplicated malaria cases beginning in 2023, with a persistent concentration of burden in Yenagoa and heterogeneous surges elsewhere (notably Nembe and Ogbia LGAs). Spatial variation in the DHIS2-reported burden is most strongly aligned with a latent \u0026ldquo;settlement connectivity\u0026rdquo; axis, captured by built-up extent, NTL, and road length, reinforced by the number of healthcare facilities and upland cropland, all of which jointly increase both the ecological receptivity and the visibility of cases (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e). In a thermally optimal, uniformly wet setting, these human landscape features overshadow coarse climate and vegetation metrics. The findings point toward peri-urban environmental management and housing improvements, alongside strengthened, unbiased surveillance, as priorities for reducing the malaria burden in Bayelsa State. The immediate priorities are to sustain high coverage in the most affected upland, highly connected LGAs; to address peri-urban and roadside larval habitats and settlement drainage; and to verify the 2023\u0026ndash;2024 step change through systematic triangulation with testing and reporting indicators to determine the relative contributions of epidemiologic versus operational drivers and refine subnational prioritization in line with national and WHO guidance (\u003cspan additionalcitationids=\"CR50\" citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e). As Bayelsa advances subnational stratification, these findings support geographically targeted vector control and surveillance that explicitly account for the coupled effects of urbanization, mobility, and agroecology on malaria transmission and detection.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003e\u003cb\u003eANOVA\u003c/b\u003e Analysis of variance\u003c/p\u003e\u003cp\u003e\u003cb\u003eAICc\u003c/b\u003e Akaike information criterion\u003c/p\u003e\u003cp\u003e\u003cb\u003eCAGR\u003c/b\u003e Compound annual growth rate\u003c/p\u003e\u003cp\u003e\u003cb\u003eCI\u003c/b\u003e Confidence interval\u003c/p\u003e\u003cp\u003e\u003cb\u003eDHIS 2\u003c/b\u003e District Health Information System 2\u003c/p\u003e\u003cp\u003e\u003cb\u003eESRI\u003c/b\u003e Environmental Systems Research Institute\u003c/p\u003e\u003cp\u003e\u003cb\u003eEVI\u003c/b\u003e Enhanced Vegetation Index\u003c/p\u003e\u003cp\u003e\u003cb\u003eGEE\u003c/b\u003e Google Earth Engine\u003c/p\u003e\u003cp\u003e\u003cb\u003eJB\u003c/b\u003e Jarque\u0026ndash;Bera\u003c/p\u003e\u003cp\u003e\u003cb\u003eK(B)P\u003c/b\u003e Koenker\u0026ndash;Breusch\u0026ndash;Pagan\u003c/p\u003e\u003cp\u003e\u003cb\u003eLST\u003c/b\u003e Land surface temperature\u003c/p\u003e\u003cp\u003e\u003cb\u003eNDBI\u003c/b\u003e Normalized difference built-up index\u003c/p\u003e\u003cp\u003e\u003cb\u003eNDVI\u003c/b\u003e Normalized difference vegetation index\u003c/p\u003e\u003cp\u003e\u003cb\u003eNDWI\u003c/b\u003e Normalized difference water index\u003c/p\u003e\u003cp\u003e\u003cb\u003eNMEP\u003c/b\u003e National Malaria Elimination Program\u003c/p\u003e\u003cp\u003e\u003cb\u003eNTL\u003c/b\u003e Nighttime light\u003c/p\u003e\u003cp\u003e\u003cb\u003eOLS\u003c/b\u003e Ordinary least squares\u003c/p\u003e\u003cp\u003e\u003cb\u003ePh\u003c/b\u003e Potential of hydrogen\u003c/p\u003e\u003cp\u003e\u003cb\u003eSA\u003c/b\u003e Spatial autocorrelation\u003c/p\u003e\u003cp\u003e\u003cb\u003eSAVI\u003c/b\u003e Soil Adjusted Vegetation Index\u003c/p\u003e\u003cp\u003e\u003cb\u003eSPSS\u003c/b\u003e Statistical Package for the Social Sciences\u003c/p\u003e\u003cp\u003e\u003cb\u003eSRTM\u003c/b\u003e Shuttle Radar Topographical Mapping\u003c/p\u003e\u003cp\u003e\u003cb\u003eVIF\u003c/b\u003e Variance Inverse Factor\u003c/p\u003e\u003cp\u003e\u003cb\u003eVIIRS\u003c/b\u003e Visible Infrared Imaging Radiometer Suite\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe malaria case data used in this study were obtained from the District Health Information System 2 (DHIS2) and were granted access by the National Malaria Elimination Program (NMEP), Nigeria. All other data sets used in this study are openly available. The environmental predictor variables were obtained from the Google Earth Engine (GEE). Administrative boundaries were sourced from Grid3.org.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eJOO\u003c/strong\u003e, \u003cstrong\u003eOJT\u0026nbsp;\u003c/strong\u003eand \u003cstrong\u003eOA\u0026nbsp;\u003c/strong\u003edrafted the original manuscript, as well as reviewed and edited the manuscript. \u0026nbsp;\u003cstrong\u003e\u0026nbsp;GE, DA, IE\u0026nbsp;\u003c/strong\u003ewere involved in project administration and reviewing the manuscript. \u003cstrong\u003eLO, AA, ST, TB, GW,\u0026nbsp;\u003c/strong\u003ecurated data. \u003cstrong\u003eJOO\u003c/strong\u003e and \u003cstrong\u003eOJT\u0026nbsp;\u003c/strong\u003econducted a formal analysis. \u003cstrong\u003e\u0026nbsp;CK\u0026nbsp;\u003c/strong\u003ecritically review the manuscript and provide feedback with leadership of project design, supervision of project delivery, and supervisory authorship of our manuscript. \u0026nbsp;All the authors have read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eKar NP, Kumar A, Singh OP, Carlton JM, Nanda N. A review of malaria transmission dynamics in forest ecosystems. Parasit Vectors. 2014;7(1):265.\u003c/li\u003e\n\u003cli\u003eSinka ME, Bangs MJ, Manguin S, Coetzee M, Mbogo CM, Hemingway J, et al. The dominant Anopheles vectors of human malaria in Africa, Europe and the Middle East: occurrence data, distribution maps and bionomic pr\u0026eacute;cis. Parasit Vectors. 2010;3(1):117.\u003c/li\u003e\n\u003cli\u003eMordecai EA, Paaijmans KP, Johnson LR, Balzer C, Ben-Horin T, de Moor E, et al. Optimal temperature for malaria transmission is dramatically lower than previously predicted. Ecol Lett. 2013;16(1):22\u0026ndash;30.\u003c/li\u003e\n\u003cli\u003eMachault V, Vignolles C, Borchi F, Vounatsou P, Pages F, Briolant S, et al. The use of remotely sensed environmental data in the study of malaria. Geospat Health. 2011;5(2):151.\u003c/li\u003e\n\u003cli\u003eHay SI, Guerra CA, Tatem AJ, Atkinson PM, Snow RW. Urbanization, malaria transmission and disease burden in Africa. Nat Rev Microbiol. 2005;3(1):81\u0026ndash;90.\u003c/li\u003e\n\u003cli\u003eTusting LS, Bisanzio D, Alabaster G, Cameron E, Cibulskis R, Davies M, et al. Mapping changes in housing in sub-Saharan Africa from 2000 to 2015. Nature. 2019;568(7752):391\u0026ndash;4.\u003c/li\u003e\n\u003cli\u003eLi X, Elvidge C, Zhou Y, Cao C, Warner T. Remote sensing of night-time light. Int J Remote Sens. 2017;38(21):5855\u0026ndash;9.\u003c/li\u003e\n\u003cli\u003eSmall C, Elvidge CD, Balk D, Montgomery M. Spatial scaling of stable night lights. Remote Sens Environ. 2011;115(2):269\u0026ndash;80.\u003c/li\u003e\n\u003cli\u003eWesolowski A, Eagle N, Tatem AJ, Smith DL, Noor AM, Snow RW, et al. Quantifying the Impact of Human Mobility on Malaria. Science (1979). 2012;338(6104):267\u0026ndash;70.\u003c/li\u003e\n\u003cli\u003eBhatt S, Weiss DJ, Cameron E, Bisanzio D, Mappin B, Dalrymple U, et al. The effect of malaria control on Plasmodium falciparum in Africa between 2000 and 2015. Nature. 2015;526(7572):207\u0026ndash;11.\u003c/li\u003e\n\u003cli\u003eWeiss DJ, Nelson A, Vargas-Ruiz CA, Gligoric K, Bavadekar S, Gabrilovich E, et al. Global maps of travel time to healthcare facilities. Nat Med. 2020;26(12):1835\u0026ndash;8.\u003c/li\u003e\n\u003cli\u003eCeccato P, Connor SJ, Jeanne I, Thomson MC. Application of Geographical Information Systems and Remote Sensing technologies for assessing and monitoring malaria risk. Parassitologia. 2005; 47:81\u0026ndash;96.\u003c/li\u003e\n\u003cli\u003eIjumba JN, Lindsay SW. Impact of irrigation on malaria in Africa: paddies paradox. Med Vet Entomol. 2001;15(1):1\u0026ndash;11.\u003c/li\u003e\n\u003cli\u003eMunga S, Minakawa N, Zhou G, Mushinzimana E, Barrack OOJ, Githeko AK, et al. Association Between Land Cover and Habitat Productivity of Malaria Vectors in Western Kenya Highlands. Am J Trop Med Hyg. 2006;74(1):69\u0026ndash;75.\u003c/li\u003e\n\u003cli\u003eKlinkenberg E, McCall P, Wilson MD, Amerasinghe FP, Donnelly MJ. Impact of urban agriculture on malaria vectors in Accra, Ghana. Malar J. 2008;7(1):151.\u003c/li\u003e\n\u003cli\u003eCoetzee M, Hunt RH, Wilkerson R, Torre A Della, Coulibaly MB, Besansky NJ. Anopheles coluzzii and Anopheles amharicus, new members of the Anopheles gambiae complex. Zootaxa. 2013;3619(3).\u003c/li\u003e\n\u003cli\u003eAliyu A, Amadu L. Urbanization, cities, and health: The challenges to Nigeria \u0026ndash; A review. Ann Afr Med. 2017;16(4):149.\u003c/li\u003e\n\u003cli\u003eRuktanonchai NW, Smith DL, De Leenheer P. Parasite sources and sinks in a patched Ross\u0026ndash;Macdonald malaria model with human and mosquito movement: Implications for control. Math Biosci. 2016; 279:90\u0026ndash;101.\u003c/li\u003e\n\u003cli\u003eStresman G, Cameron A, Drakeley C. Freedom from Infection: Confirming Interruption of Malaria Transmission. Trends Parasitol. 2017;33(5):345\u0026ndash;52.\u003c/li\u003e\n\u003cli\u003eYusuf O, Okoronkwo C, Ademu C, Silal SP. Investigating the relationship between climatic factors and malaria transmission dynamics in Southwest states of Nigeria. 2024.\u003c/li\u003e\n\u003cli\u003eGorelick N, Hancher M, Dixon M, Ilyushchenko S, Thau D, Moore R. Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sens Environ. 2017; 202:18\u0026ndash;27.\u003c/li\u003e\n\u003cli\u003eEsri. https://livingatlas.arcgis.com/landcoverexplorer/. ArcGIS Living Atlas of the World.\u003c/li\u003e\n\u003cli\u003eKarra K, Kontgis C, Statman-Weil Z, Mazzariello JC, Mathis M, Brumby SP. Global land use/land cover with Sentinel 2 and deep learning. In: 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS. IEEE; 2021. p. 4704\u0026ndash;7.\u003c/li\u003e\n\u003cli\u003eLiu M, Zhang Y. Impact of climate change on dengue fever: a bibliometric analysis. Geospat Health. 2025;20(1).\u003c/li\u003e\n\u003cli\u003eEllis PD. The essential guide to effect sizes: statistical power, meta-analysis, and the interpretation of research results. Cambridge University Press; 2010. 173 p.\u003c/li\u003e\n\u003cli\u003eMoran PAP. Notes on Continuous Stochastic Phenomena. Biometrika. 1950;37(1/2):17.\u003c/li\u003e\n\u003cli\u003eGetis A. Cliff, A.D. and Ord, J.K. 1973: Spatial autocorrelation. London: Pion. Prog Hum Geogr. 1995 Jun 1;19(2):245\u0026ndash;9.\u003c/li\u003e\n\u003cli\u003eAnselin L, Syabri I, Kho Y. GeoDa: An Introduction to Spatial Data Analysis. In: Handbook of Applied Spatial Analysis. Berlin, Heidelberg: Springer Berlin Heidelberg; 2010. p. 73\u0026ndash;89.\u003c/li\u003e\n\u003cli\u003eAnselin L. Local Indicators of Spatial Association\u0026mdash;LISA. Geogr Anal. 1995;27(2):93\u0026ndash;115.\u003c/li\u003e\n\u003cli\u003eWaller Lance A, Gotway Carol A. Applied Spatial Statistics for Public Health Data. John Wiley \u0026amp; Sons; 2004.\u003c/li\u003e\n\u003cli\u003eAnselin Luc. Spatial econometrics: methods and models. Kluwer Academic Publishers; 1988. 284 p.\u003c/li\u003e\n\u003cli\u003eDormann CF, Elith J, Bacher S, Buchmann C, Carl G, Carr\u0026eacute; G, et al. Collinearity: a review of methods to deal with it and a simulation study evaluating their performance. Ecography. 2013;36(1):27\u0026ndash;46.\u003c/li\u003e\n\u003cli\u003eWHO. Malaria surveillance, monitoring and evaluation: a reference manual. In Malaria surveillance, monitoring and evaluation: a reference manual. 2018.\u003c/li\u003e\n\u003cli\u003eCiski M, Rzasa K. Multiscale Geographically Weighted Regression in the Investigation of Local COVID-19 Anomalies Based on Population Age Structure in Poland. Int J Environ Res Public Health. 2023;20(10):5875.\u003c/li\u003e\n\u003cli\u003eWHO. WHO Malaria Policy Advisory Group (MPAG) Meeting Report. 2023. https://www.who.int/publications/i/item/9789240074385. Accessed 20 July 2025.\u003c/li\u003e\n\u003cli\u003eElvidge CD, Baugh KE, Kihn EA, Kroehl HW, Davis ER. Mapping city lights with nighttime data from the DMSP Operational Linescan System. Photogramm Eng Remote Sensing. 1997;63(6):727\u0026ndash;34.\u003c/li\u003e\n\u003cli\u003eGitheko AK, Lindsay SW, Confalonieri UE, Patz JA. Climate change and vector-borne diseases: a regional analysis. Bulletin of the World Health Organization Special Theme\u0026ndash; Environment and Health. 2000;78(9):1136\u0026ndash;47.\u003c/li\u003e\n\u003cli\u003eKeiser J, J\u0026uuml;rg Utzinger, Marcia Caldas De Castro, Thomas A. Smith, Marcel Tanner, Burton H. Singer. Urbanization in Sub-Saharan Africa and Implication for Malaria Control. In: The Intolerable Burden of Malaria II: What\u0026rsquo;s New, What\u0026rsquo;s Needed: Supplement to Volume 71 (2) of the American Journal of Tropical Medicine and Hygiene. 2nd ed. American Society of Tropical Medicine and Hygiene, Northbrook (IL); 2004.\u003c/li\u003e\n\u003cli\u003eElvidge CD, Zhizhin M, Ghosh T, Hsu FC, Taneja J. Annual Time Series of Global VIIRS Nighttime Lights Derived from Monthly Averages: 2012 to 2019. Remote Sens (Basel). 2021;13(5):922.\u003c/li\u003e\n\u003cli\u003eElvidge CD, Baugh KE, Zhizhin M, Hsu FC. Why VIIRS data are superior to DMSP for mapping nighttime lights. In: Proceedings of the Asia-Pacific Advanced Network. 2013. p. 62.\u003c/li\u003e\n\u003cli\u003eMinakawa N, Munga S, Atieli F, Mushinzimana E, Zhou G, Githeko AK, et al. Spatial Distribution of Anopheline Larval Habitats in Western Kenyan Highlands: Effects of Land Cover Types and Topography. Am J Trop Med Hyg. 2005;73(1):157\u0026ndash;65.\u003c/li\u003e\n\u003cli\u003eWHO. WHO malaria policy advisory group (MPAG) meeting report. World Health Organization; 2024. https://www.who.int/publications/i/item/9789240095298. Accessed 14 July 2025.\u003c/li\u003e\n\u003cli\u003eSherrard-Smith E, Hogan AB, Hamlet A, Watson OJ, Whittaker C, Winskill P, et al. The potential public health consequences of COVID-19 on malaria in Africa. Nat Med. 2020;26(9):1411\u0026ndash;6.\u003c/li\u003e\n\u003cli\u003eZha Y, Gao J, Ni S. Use of normalized difference built-up index in automatically mapping urban areas from TM imagery. Int J Remote Sens. 2003;24(3):583\u0026ndash;94.\u003c/li\u003e\n\u003cli\u003eSattler MA, Mtasiwa D, Kiama M, Premji Z, Tanner M, Killeen GF, et al. Habitat characterization and spatial distribution of Anopheles sp. mosquito larvae in Dar es Salaam (Tanzania) during an extended dry period. Malar J. 2005;4(1):4.\u003c/li\u003e\n\u003cli\u003eRund S, O\u0026rsquo;Donnell A, Gentile J, Reece S. Daily Rhythms in Mosquitoes and Their Consequences for Malaria Transmission. Insects. 2016;7(2):14.\u003c/li\u003e\n\u003cli\u003eNoor AM, Zurovac D, Hay SI, Ochola SA, Snow RW. Defining equity in physical access to clinical services using geographical information systems as part of malaria planning and monitoring in Kenya. Tropical Medicine \u0026amp; International Health. 2003;8(10):917\u0026ndash;26.\u003c/li\u003e\n\u003cli\u003eStresman G, Bousema T, Cook J. Malaria Hotspots: Is There Epidemiological Evidence for Fine-Scale Spatial Targeting of Interventions? Trends Parasitol. 2019;35(10):822\u0026ndash;34.\u003c/li\u003e\n\u003cli\u003eNMEP, NPC, ICF. Nigeria Malaria Indicator Survey 2021 Final Report. Abuja; 2022. 150 p.\u003c/li\u003e\n\u003cli\u003eWHO. Global technical strategy for malaria, 2016-2030. Global Malaria Program, World Health Organization; 2021. 29 p.\u003c/li\u003e\n\u003cli\u003eWHO. Report on the first and second meetings of the Technical Advisory Group on Malaria Elimination and Certification. World Health Organization, 2023. 2023.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Cluster, environment, epidemiology, endemic diseases, malaria incidence, Nigeria, Plasmodium falciparum, spatial analysis","lastPublishedDoi":"10.21203/rs.3.rs-8165329/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8165329/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eBayelsa State, Nigeria with a current prevalence rate of 17% based on the 2021 Nigeria Malaria Indicator Survey. Previous studies on malaria incidence in Bayelsa State, Nigeria, have the absence of longitudinal studies, low survey coverage, limited integration of environmental factors into analyses of local government area (LGA)-level malaria patterns, and few or no comparisons between upland and riverine settings. This study quantifies temporal trends and spatial heterogeneity in confirmed uncomplicated malaria across eight (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e) LGAs, compares malaria burdens between upland and riverine LGAs, and identifies and ranks environmental and infrastructural predictors of LGA-level malaria via complementary statistical approaches.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eData on confirmed uncomplicated malaria cases from 2017\u0026ndash;2024 for all LGAs in Bayelsa were downloaded from the DHIS2 database. Administrative data and the number of healthcare facilities were downloaded from Grid 3.org, whereas environmental data were downloaded from the Google Earth Engine website. Descriptive statistics, univariate Moran\u0026rsquo;s I, ANOVA, correlation, and exploratory and ordinary least squares regression were the analytical methods used.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eThe incidence of confirmed uncomplicated malaria in Bayelsa State rose from 10,745 cases in 2017 to 65,149 cases in 2024, a 6.06-fold increase corresponding to a compound annual growth rate of 29.4%. Yenagoa consistently accounted for the largest share, ranging from 28.3% in 2018 to 49.2% in 2019 and 39.5% in 2024. The incidence was generally greater in upland LGAs than in riverine LGAs, with significantly greater dispersion in upland settings (F(1,62)\u0026thinsp;=\u0026thinsp;4.904, p\u0026thinsp;=\u0026thinsp;0.030). The global Moran\u0026rsquo;s I coefficients were weakly negative across years, suggesting spatial dispersion rather than clustering. Regression analysis revealed Visible Infrared Imaging Radiometer Suite (VIIRS) (t\u0026thinsp;=\u0026thinsp;25.86), elevation (t\u0026thinsp;=\u0026thinsp;18.89), and NDVI (|t|=12.88) as the strongest predictors, supported by built-up land (r\u0026thinsp;=\u0026thinsp;0.954, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), roads (r\u0026thinsp;=\u0026thinsp;0.912, p\u0026thinsp;=\u0026thinsp;0.001), cropland (r\u0026thinsp;=\u0026thinsp;0.711, p\u0026thinsp;=\u0026thinsp;0.024), and healthcare facilities (r\u0026thinsp;=\u0026thinsp;0.808, p\u0026thinsp;=\u0026thinsp;0.008).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eThe findings show that settlement expansion and environmental conditions strongly shape malaria dynamics, often outweighing broad climate and vegetation measures in thermally optimal, wet areas. Priorities for reducing the malaria burden include peri-urban environmental management, improved housing, and strengthened unbiased surveillance.\u003c/p\u003e","manuscriptTitle":"Spatiotemporal Dynamics and Environmental Predictors of Confirmed Uncomplicated Malaria in Bayelsa State, Nigeria (2017-2024)","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-04 08:51:53","doi":"10.21203/rs.3.rs-8165329/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-01-05T03:45:17+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-02T09:58:18+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-28T18:07:42+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"200920050856014742943935175215724405273","date":"2025-12-22T11:00:03+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"208535565281341548228997421391252423912","date":"2025-12-21T03:40:05+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-19T18:30:31+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-14T07:50:56+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"283150838457459749018967681493906064736","date":"2025-12-05T06:24:07+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"9918396723753853207476291889188769358","date":"2025-12-03T10:38:59+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"147890332462036736283421440469752800987","date":"2025-12-02T14:34:48+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-12-02T14:27:31+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-11-22T15:51:25+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-21T12:17:45+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-11-21T12:16:06+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Public Health","date":"2025-11-20T13:42:25+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"3a91a4fd-0829-4830-bbab-a84c5f98dc35","owner":[],"postedDate":"December 4th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-03-10T13:38:33+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-04 08:51:53","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8165329","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8165329","identity":"rs-8165329","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}