Analytical Hierarchical Process for Modelling Malaria Vulnerability Index Among Local Government Areas in Bayelsa State, Nigeria

Analytical Hierarchical Process for Modelling Malaria Vulnerability Index Among Local Government Areas in Bayelsa State, Nigeria | 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 Analytical Hierarchical Process for Modelling Malaria Vulnerability Index Among Local Government Areas in Bayelsa State, Nigeria Okpachi Abbah, Olalekan John Taiwo, James Olaoye Oyeleye, Ganiyat Eshikhena, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8250166/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 18 Apr, 2026 Read the published version in Discover Public Health → Version 1 posted 15 You are reading this latest preprint version Abstract Background Persistent malaria transmission in Africa underscores the need for spatially explicit tools that identify highly endemic areas for targeted control. Although multi-criteria decision analysis (MCDA) offers a structured approach, its application has been limited by outdated environmental inputs and inconsistent factor aggregation methods. This study developed an ecology-informed Malaria Vulnerability Index (MVI) for Bayelsa State, Nigeria, using up-to-date, open-source geospatial datasets and a transparent weighting framework. Methods Thirteen environmental predictors were sourced from OpenStreetMap, Google Earth Engine, WorldPop, and GRID3. Using the Analytical Hierarchy Process (AHP), a 13×13 pairwise comparison matrix was constructed and solved using the eigenvalue method to derive criterion weights. Weighted predictors were combined to generate the MVI, which was overlaid with gridded population data to quantify population exposure. Associations between population counts across low, medium, and high vulnerability zones and reported malaria cases were assessed using correlation analysis. Results The highest-priority criteria were distance to streams, distance to wetlands, precipitation, topographic wetness index, and land surface temperature. Medium vulnerability dominated the landscape (77.1%), followed by low (17.2%) and high (5.7%) vulnerability. High-vulnerability areas were concentrated in riverine LGAs, particularly Southern Ijaw (40.25%), Brass (19.30%), Ekeremor (17.36%), and Sagbama (15.89%). Population exposure reflected these patterns: 3.63% of residents lived in high-vulnerability zones, 74.66% in medium, and 21.70% in low zones. Population in low-vulnerability areas showed a strong correlation with reported malaria cases (r = 0.914), while total population also correlated with cases (r = 0.719). Conclusion Malaria vulnerability in Bayelsa State is primarily driven by hydrological and hydroclimatic conditions, especially proximity to streams and wetlands, rainfall, and microtopographic wetness. The AHP-based MCDA framework provides a rigorous and transparent approach for integrating environmental factors, supporting hydrology-focused targeting of malaria surveillance and vector control, and enabling reproducible MVI mapping using open-source geospatial data. Malaria Vulnerability Index (MVI) Analytic Hierarchy Process (AHP) Multicriteria Decision Analysis (MCDA) Geographic Information System (GIS) Bayelsa State Spatial Epidemiology Health Geographics Figures Figure 1 Figure 2 Figure 3 1. Introduction Malaria remains a leading cause of preventable morbidity and mortality in sub-Saharan Africa, with Nigeria persistently accounting for a substantial share of the continent’s Plasmodium falciparum burden ( 1 – 4 ). Transmission is intensely heterogeneous over short distances due to interactions among climate, hydrology, vector ecology, human settlement, and access to prevention and care ( 5 – 7 ). Bayelsa State, situated in the coastal Niger Delta, exemplifies a hydro-ecological template of low elevation, mangrove-swamp mosaics, and high, year-round rainfall that sustains Anopheles receptivity and complicates uniform program strategies ( 8 , 9 ). Socio-environmental conditions, including housing quality and peri-domestic exposure, further modulate risk, reinforcing the need to integrate multiple determinants when assessing vulnerability ( 10 ). To guide intervention planning in such settings, decision-ready metrics that integrate multiple vulnerability drivers are essential. Multicriteria decision analysis (MCDA) provides a principled framework for integrating diverse malaria determinants into a single, interpretable index, aligning with the broader movement toward transparent, evidence-based priority setting in health( 11 ) ( 12 ). Within Geographic Information System (GIS) based MCDA, the Analytic Hierarchy Process (AHP) formalises expert judgment into weights while enforcing internal coherence via a consistency ratio (CR), and Weighted Linear Combination (WLC) offers an intuitive overlay for combining normalised criteria into continuous vulnerability surfaces ( 13 , 14 ). In infectious disease and malaria risk assessment, GIS-MCDA approaches have proved useful for stratification, site selection, and targeting of interventions by combining remotely sensed layers (e.g., land cover, elevation, Normalized Difference Vegetation Index (NDVI), rainfall) with program data (e.g., Insecticide Treated Net/ Indoor Residual Spraying (ITN/IRS) coverage, test positivity) to capture vulnerability that no single indicator can reveal ( 5 , 14 ). Methodological advances, including sensitivity analysis to examine the stability of results underweight perturbations, strengthen the credibility and transferability of MCDA outputs ( 15 ). While malaria risk mapping has widely leveraged remote sensing and routine surveillance to characterise spatial heterogeneity reproducible AHP–WLC applications that transparently document weighting, produce LGA-resolved indices, estimate population exposure by risk class, and validate composite outputs against routine caseloads remain sparse in high-burden, ecologically complex settings like Bayelsa State, Nigeria ( 2 , 5 , 8 ). These gaps are operationally consequential in Bayelsa, where decision makers must prioritise interventions across hydrologically diverse LGAs with varying access to prevention and care. Existing subnational risk assessments often rely on single-domain proxies (e.g., environmental suitability alone or routine incidence alone), apply ad-hoc or opaque weighting, and seldom quantify uncertainty or assess convergence with epidemiological indicators, and these limitations blunt their programmatic value ( 14 – 16 ); ( 17 ); ( 18 ). To address these needs, this study develops a transparent, reproducible Malaria Vulnerability Index (MVI) for Bayelsa State using AHP and WLC, tightly coupled to routine data. Our objectives are to develop a comprehensive malaria vulnerability assessment for Bayelsa State through the creation of a consistent pairwise comparative matrix of vulnerability indicators, generation of a composite malaria vulnerability index map, ranking of Local Government Areas based on vulnerability scores, estimation of population proportions within each vulnerability category, and analysis of the relationship between confirmed uncomplicated malaria cases and areas under different vulnerability classifications. Together, these objectives link mechanistic vulnerability to observed burden and quantify how many people reside in each class, which is key information for prioritisation ( 6 , 7 ). 2. Methodology 2.1 Data sources and processing We constructed a spatially explicit Malaria Vulnerability Index (MVI) for Bayelsa State, Nigeria, by integrating harmonised geospatial datasets on hydroclimate, terrain, land use/land cover (LULC), hydrology, accessibility, population, administrative boundaries, and routine malaria surveillance. Details of these are presented in Table 1 . Table 1 Data Sources and Processing for Malaria Vulnerability Index (MVI) Dataset Source Resolution Application Digital Elevation Model (DEM) NASA/USGS Shuttle Radar Topography Mission (SRTM v3) 1 arc-second (~ 30 m) Topographic and terrain derivatives; slope and Topographic Wetness Index (TWI) computation for hydrological modelling. Slope & Topographic Wetness Index (TWI) Derived from SRTM DEM using hydrologic conditioning (D8 flow direction/accumulation framework) ~ 30 m Indicators of surface runoff, soil moisture, and mosquito habitat suitability. Precipitation (CHIRPS v2.0) Climate Hazards Group InfraRed Precipitation with Stations ( 19 ) 0.05° (~ 5 km) Hydroclimate driver of mosquito breeding and malaria transmission risk. Land Surface Temperature (LST, MOD11A2 v6.1) MODIS Terra ( 20 ) 1 km Thermal suitability for malaria vectors and parasite development. Vegetation Index (NDVI, MCD13Q1 v6.1) MODIS Terra/Aqua combined ( 21 ) 250 m Proxy for vegetation cover, mosquito resting/breeding habitats. Land Use/Land Cover (LULC) Esri/Microsoft Impact Observatory Global Land Cover ( 22 ) 10 m Binary masks & proximity surfaces for open water, wetlands, cropland, built-up areas, trees, rangeland; linked to malaria vulnerability. Hydrologic Networks (Rivers/Streams)) HydroSHEDS/HydroRIVERS ( 23 ) Vector (polyline) Hydrologically consistent networks for computing proximity to rivers/water bodies Accessibility Boundaries (Road Networks) OpenStreetMap ( 24 ) Vector (line features) Accessibility surfaces; Euclidean distance to transportation routes. Health Facilities (Public & Private) GRID3 Nigeria ( 25 ) Geocoded point features Euclidean distance to facilities; health service accessibility. Administrative Boundaries (LGA) GRID3 Nigeria ( 25 ) Vector (Polygon) Alignment with reporting units; aggregation of surveillance and demographic data Population Counts Data WorldPop 2020 Nigeria gridded counts ( 26 , 27 ) ~ 100 m Denominators for malaria incidence calculation; population vulnerability mapping. Routine Malaria Surveillance DHIS2-based Health Management Information System (2024) LGA-level counts Laboratory-confirmed malaria cases; incidence calculation following WHO standards. 2.2 Data Analysis 2.2.1 Data harmonisation All spatial data sets were standardised to a single analysis grid to avoid resampling errors( 28 )( 29 ) The study used the WGS 84/UTM Zone 32N coordinate system for metric accuracy. The Landuse/Landcover (LULC) raster, with a native resolution of 10m, served as the reference (snap raster)and defined the processing cell size. All rasters were aligned to this grid, while vector layers were reprojected before rasterisation. We utilised malaria case data for 2024, whereas population denominators were derived from WorldPop 2020. Although this introduces a temporal mismatch, such is common in subnational studies. The use of externally validated WorldPop estimates reduces the risk of denominator bias and was accounted for in the uncertainty assessment ( 30 , 31 ). 2.2.2 Criteria selection and surface derivation Criteria were chosen to represent well-established drivers of malaria receptivity, exposure, and vulnerability in sub-Saharan Africa ( 1 , 2 , 7 , 8 , 32 , 33 ). These included: Hydrology and moisture:open water, wetlands, Topographic Wetness Index (TWI), proximity to rivers. These capture larval habitat availability and persistence ( 34 – 36 ). Climate and greenness: Precipitation, land surface temperature (LST), NDVI. These approximate moisture and thermal suitability for vector and parasite development ( 9 , 37 – 40 ). Terrain: Elevation, slope. These modulates temperature and drainage ( 36 ). LULC and accessibility variables:Proximity to cropland, rangeland, trees, built-up and bare ground; distance to roads; distance to health facilities. These reflect human–-=environment interactions and care access ( 7 , 41 – 43 ). 2.2.3 Standardisation and vulnerability coding To place heterogeneous inputs on a common scale and direction, all rasters were linearly rescaled to [0, 1], with 0 denoting the highest vulnerability and 1 denoting the lowest. Transformations were guided by epidemiological evidence: Variables positively associated with vulnerability (higher raw values imply higher risk) were mapped with decreasing transforms so that higher raw values yield lower standardised scores. This applied to open water and wetlands (presence), TWI, precipitation, proximity to rivers (shorter distances imply higher vulnerability), cropland presence, NDVI within the local dynamic range, and LST within transmission-relevant bounds ( 9 , 34 , 35 , 37 , 38 , 40 ). Variables negatively associated with vulnerability were mapped with increasing transforms so that higher raw values imply higher standardised scores; this applied to elevation, slope, and LULC classes generally protective or less favourable for stable transmission in the West African urban context (trees, rangeland, bare ground, built-up) ( 7 , 44 – 46 ). For access variables, greater distance to roads or health facilities increases vulnerability; rescaling, therefore, yielded lower standardised scores at larger distances ( 2 , 41 , 43 ). Binary LULC presences received scores consistent with their direction (e.g., open water present = 0; absent = 1). Continuous variables were min–max transformed within the study area. 2.2.4 AHP Weighting Weights for the criteria selected after expert consultation and judgement were assigned using the Analytic Hierarchy Process (AHP).Expert judgments were elicited via pairwise comparisons on Saaty’s 1–9 scale to form a 13 × 13 reciprocal judgment matrix. Normalised criteria weights were extracted from the principal right eigenvector. Internal consistency was evaluated using the Consistency Index (CI) and Consistency ratio (CR). $$\:CI=\frac{\left({{\lambda\:}}_{max}-n\right)}{\left(n-1\right)},\hspace{1em}CR=\frac{CI}{RI},\hspace{1em}RI13=1.56$$ Where \(\:{{\lambda\:}}_{max}\) is the Principal eigenvalue of the pairwise comparison matrix, n is the number of criteria(size of the matrix), RI is the random index, and RI13 is the Random index at the 13th criteria. Where \(\:CR\:\le\:\:0.10\) was considered an acceptable coherence ( 13 , 47 – 49 ). 2.2.4 Composite MVI construction and classification The continuous MVI was computed as a weighted linear combination: where \(\:{s}_{i\left(x\right)}\) s is the standardised score for criterion \(\:i\) at location \(\:x\) and \(\:{w}_{i}\) is its AHP-derived weight. Because of the coding, lower \(\:MVI\) values denote greater vulnerability. The continuous surface was reclassified into three ordinal classes (high, medium, low) using equal interval categorisation to ensure comparability ( 14 ). 2.2.5 Population at risk We quantified the population residing in each vulnerability class within each LGA by overlaying the raster WorldPop 2020 with class-specific binary masks (low, medium and high vulnerability). Class totals and proportions were summarised using zonal statistics with LGA polygons. Islands and open water within LGA boundaries were masked before extraction to avoid over-counting uninhabitable areas. 2.2.6 Malaria incidence and association analysis Confirmed malaria cases for 2024 were aggregated at the LGA level. Incidence per 1000 population was calculated using the WorldPop denominators.( 31 ). Associations between incidence and MVI-derived population classes were evaluated using Pearson correlation coefficients, with 95% confidence intervals (CIs) reported. To adjust for multiple testing, the Benjamini–Hochberg correction was applied. Because of potential non-normality and the modest sample size, Spearman rank correlations were also conducted as sensitivity analyses. Paired differences between class-specific population counts and malaria cases were tested using paired t-tests, with statistical significance set at p ≤ 0.05 (two-tailed). Effect sizes were calculated as Hedges’ g, with corresponding 95% CIs. All interpretations emphasised the scale dependence of counts and the ecological nature of the analysis. 2.2.7 Quality assurance and uncertainty We appraised key sources of uncertainty: (i) temporal mismatch between 2020 denominators and 2024 cases, (ii) the modifiable areal unit problem (MAUP) due to aggregation at the LGA level, (iii) the subjectivity inherent in AHP, and (iv) classification and standardisation choices We mitigated these risks by aligning all raster operations to the Landuse/cover grid, analysing proportions where possible, using fixed reclassification thresholds, and checking the internal consistency of AHP weights. Spatial dependence was considered using Moran’s I for LGA-level outcomes where relevant, with permutation-based inference to guard against inflated significance( 50 , 51 ). 3. Results 3.1 AHP-derived criterion weights Bayelsa State is a Nigerian state located within the Niger Delta region. Administratively, it has eight Local Government Areas (LGAs) and 105 local wards (Fig. 1 ). The LGAs are divided into two: four upland and four riverine. Dominant vegetation comprises mangroves and freshwater swamp forest. ( 52 ) We derived the criterion weights for the Bayelsa State malaria vulnerability index using Saaty’s Analytic Hierarchy Process (AHP). After redundancy screening and expert review, thirteen criteria were retained for the Analytic Hierarchy Process (AHP) weighting to balance parsimony and ecological coverage. Class-specific rasters and distance surfaces were derived as described above. Distances to rivers, roads, and health facilities were computed under planar (UTM) geometry and constrained to the state boundary before normalisation. Slope was expressed as a per cent rise. The 13×13 pairwise comparison matrix was built to reflect the ecology of malaria vectors in the Niger Delta, where dense hydrographic networks, seasonally inundated wetlands, and low relief dominate the landscape, and was solved with the eigenvalue method (Table 2 ). The normalised priority vector places the strongest emphasis on hydrological and hydro-meteorological determinants. Distance to streams emerged as the most influential criterion (w ≈ 0.218), followed by distance to wetland areas (w ≈ 0.199), precipitation (w ≈ 0.128), and the topographic wetness index (TWI; w ≈ 0.099) (Table 3 ). Land surface temperature (LST) carried a moderate weight (w ≈ 0.082), consistent with its well-documented role in modulating vector and parasite development. Secondary contributions were assigned to distance from cropland (w ≈ 0.068) and elevation (w ≈ 0.049), while slope and NDVI had smaller, roughly equivalent weights (both w ≈ 0.032). Access and infrastructure-related factors such as distance to healthcare facilities, distance to the road network, and distance from built-up areas were given modest and nearly identical weights (each w ≈ 0.026). Distance from bare ground made the smallest contribution (w ≈ 0.011). The priority ordering therefore followed: Distance to stream > Distance to wetland > Precipitation > TWI > LST > Distance from cropland > Elevation > Slope ≈ NDVI > Distance to healthcare ≈ Distance to roads ≈ Distance from built-up > Distance from bare ground. Model consistency was high: the maximum eigenvalue was approximately 13.64, yielding a consistency index (CI) of 0.0536 and a consistency ratio (CR) of 0.0343 using Saaty’s random index for n = 13 (RI = 1.56), well below the 0.10 threshold ( 13 , 49 ). Table 2 Pairwise Comparison Matrix using the AHP method Criteria C1 C2 C3 C4 C5 C6 C7 C8 C9 C10 C11 C12 C13 Distance to Stream (C1) 1 1 3 3 3 5 5 7 7 7 7 7 9 Distance to Wetland Area (C2) 1 1 3 3 3 3 5 5 5 7 7 7 9 Precipitation (C3) 0.33 0.33 1 1 3 3 3 5 5 5 5 5 9 Topographic Wetness Index (TWI) (C4) 0.33 0.33 1 1 1 1 3 3 3 5 5 5 9 Land Surface Temperature (LST) (C5) 0.33 0.33 0.333 1 1 1 3 3 3 3 3 3 9 Distance from Cropland Area (C6) 0.2 0.33 0.333 0.333 1 1 1 3 3 3 3 3 9 Elevation (C7) 0.2 0.2 0.333 0.333 0.333 1 1 1 1 3 3 3 5 Slope (C8) 0.14 0.2 0.2 0.333 0.333 0.333 1 1 1 1 1 1 5 Normalised Vegetation Index (NDVI) (C9) 0.14 0.2 0.2 0.333 0.333 0.333 1 1 1 1 1 1 5 Distance to Healthcare Facilities (C10) 0.14 0.14 0.2 0.2 0.333 0.333 0.333 1 1 1 1 1 3 Distance to Road Network (C11) 0.14 0.14 0.2 0.2 0.333 0.333 0.333 1 1 1 1 1 3 Distance from Built-up Area (C12) 0.14 0.14 0.2 0.2 0.333 0.333 0.333 1 1 1 1 1 3 Distance from Bareground (C13) 0.11 0.11 0.111 0.111 0.111 0.111 0.2 0.2 0.2 0.333 0.333 0.333 1 Column Sum 4.225 4.472 10.110 11.043 14.109 16.776 24.199 32.200 32.200 38.333 38.333 38.333 79.000 Table 3 Analytical Hierarchical Process Weighted Priority Matrix Criteria Priority Weight Percentage Distance to Stream 0.2186 21.90% Distance to Wetland Area 0.1999 20.00% Precipitation 0.1288 12.90% Topographic Wetness Index (TWI) 0.0991 9.90% Land Surface Temperature (LST) 0.082 8.20% Distance from Cropland Area 0.0686 6.90% Elevation 0.0492 4.90% Slope 0.0321 3.20% Normalised Vegetation Index (NDVI) 0.0321 3.20% Distance to Healthcare Facilities 0.0261 2.60% Distance to Road Network 0.0261 2.60% Distance from Built-up Area 0.0261 2.60% Distance from Bareground 0.0112 1.10% 3.2 Statewide distribution of vulnerability classes Across Bayelsa State, the MVI was dominated by the medium vulnerability class, which covered 77.1% of the area, with low and high classes covering 17.2% and 5.7%, respectively (Figs. 2 and 3 ). Medium vulnerability was the largest component in every LGA, ranging from 57.20% in Kolokuma/Opokuma to 89.05% in Nembe. Brass (8.84%), Sagbama (8.67%), and Southern Ijaw (7.74%) exhibited comparatively larger high-vulnerability areas, whereas Ogbia (0.24%), Kolokuma/Opokuma (0.67%), and Yenagoa (1.75%) had very small high-vulnerability fractions (Table 4 ). Low vulnerability was most prominent in Kolokuma/Opokuma (42.12%), Ogbia (34.25%), and Yenagoa (32.77%). Table 4 Areal extent of MVI classes by LGA (area units; row percentages in parentheses) LGANAME Highly Vulnerable Moderately Vulnerable Low Vulnerable Total Ekeremor 89.42 (4.99%) 1399.9 (78.05%) 304.29 (16.97%) 1793.61 Southern Ijaw 207.38 (7.74%) 2317.48 (86.47%) 155.38 (5.80%) 2680.24 Nembe 21.73 (2.79%) 692.88 (89.05%) 63.5 (8.16%) 778.11 Brass 99.41 (8.84%) 866.62 (77.10%) 158.06 (14.06%) 1124.09 Ogbia 1.61 (0.24%) 445.63 (65.51%) 232.96 (34.25%) 680.2 Yenegoa 11.34 (1.75%) 423.39 (65.48%) 211.89 (32.77%) 646.62 Kolokuma/Opokuma 2.41 (0.67%) 204.53 (57.20%) 150.6 (42.12%) 357.54 Sagbama 81.88 (8.67%) 593.55 (57.20%) 268.82 (42.12%) 944.25 Total 515.18 6943.98 1545.5 9004.66 LGAs contributing most to the statewide high-vulnerability area were Southern Ijaw (40.25%), Brass (19.30%), Ekeremor (17.36%), and Sagbama (15.89%) (Table 5 ). For medium vulnerability, Southern Ijaw (33.37%) and Ekeremor (20.16%) dominated, with Brass (12.48%) and Nembe (9.98%) also contributing appreciably. Low-vulnerability contributions were led by Ekeremor (19.69%) and Sagbama (17.39%), followed by Ogbia (15.07%) and Yenagoa (13.71%). Table 5 Percentage Contribution of LGA to Various Vulnerability Index LGANAME High Vulnerability Medium Vulnerability Low Vulnerability Ekeremor 17.36 20.16 19.69 Southern Ijaw 40.25 33.37 10.05 Nembe 4.22 9.98 4.11 Brass 19.30 12.48 10.23 Ogbia 0.31 6.42 15.07 Yenegoa 2.20 6.10 13.71 Kolokuma/Opokuma 0.47 2.95 9.74 Sagbama 15.89 8.55 17.39 3.3. Population at risk by LGA At the state level, 3.63% of residents lived in high-vulnerability zones, 74.66% in medium, and 21.70% in low vulnerability zones. The medium vulnerability areas represented the majority in every LGA, ranging from 56.91% in Kolokuma/Opokuma to 84.56% in Nembe (Table 6 ). High-vulnerability proportions were modest but heterogeneous, spanning 0.32–8.25%, with Sagbama (8.25%), Southern Ijaw (6.09%), Brass (5.40%), and Ekeremor (4.64%) at the upper end. Low vulnerability was most prominent in Kolokuma/Opokuma (42.77%), Ogbia (29.08%), Sagbama (28.46%), and Yenagoa (27.63%). Table 6 Population in each MVI class by LGA (counts; row percentages in parentheses) LGA Name High Vulnerability Population Medium Vulnerability Population Low Vulnerability Population Total Ekeremor 17,900.07 (4.64%) 314,440.13 (81.47%) 53,635.30 (13.90%) 385,975.51 Southern Ijaw 30,070.70 (6.09%) 407,011.32 (82.42%) 56,726.32 (11.49%) 493,808.34 Nembe 2,401.26 (1.41%) 143,834.60 (84.56%) 23,861.34 (14.03%) 170,097.20 Brass 7,700.30 (5.40%) 107,423.43 (75.31%) 27,514.50 (19.29%) 142,638.23 Ogbia 1,357.97 (0.48%) 199,348.57 (70.44%) 82,295.23 (29.08%) 283,001.77 Yenegoa 5,381.89 (0.93%) 411,440.51 (71.43%) 159,151.00 (27.63%) 575,973.40 Kolokuma/Opokuma 345.54 (0.32%) 61,018.05 (56.91%) 45,858.84 (42.77%) 107,222.44 Sagbama 23,742.34 (8.25%) 182,155.90 (63.29%) 81,920.12 (28.46%) 287,818.36 Total 88,900.08 (3.63%) 1,826,672.51 (74.66%) 530,962.65 (21.70%) 2,446,535.25 3.4 Association between vulnerability classes and malaria cases Across LGAs, the mean malaria cases in 2024 were 8,143.6. Paired comparisons between class-specific population counts and case counts primarily reflected differences in scale; as expected, medium- and low-vulnerability populations substantially exceeded case counts (medium: mean difference 220,190.4, 95% CI 111,877.0–328,503.9, p = 0.002; low: 58,226.7, 95% CI 27,551.5–88,901.9, p = 0.003), yielding large effect sizes (Table 7 ). For the high-vulnerability class, the mean difference was small and non-significant (2,968.9, 95% CI − 8,644.4 to 14,582.1, p = 0.565). Table 7 Paired Sample T-Test Between Vulnerability Categories and Malaria Incidents in 2024 Paired Samples Test Paired Differences t Df Sig. (2-tailed) Mean Std. Deviation Std. Error Mean 95% Confidence Interval (CI) of the Difference Lower Upper Pair 1 High Vulnerability Population - Malaria2024 2968.88375 13891.08420 4911.23992 -8644.35327 14582.12077 .605 7 .565 Pair 2 Medium Vulnerability Population - Malaria2024 220190.43875 129558.29020 45805.77278 111876.99758 328503.87992 4.807 7 .002 Pair 3 Low Vulnerability Population - Malaria2024 58226.70625 36691.88070 12972.53883 27551.52633 88901.88617 4.488 7 .003 Pair 4 Total - Malaria 2024 297673.28125 163889.89324 57943.82744 160657.90166 434688.66084 5.137 7 .001 Pearson correlations between class-specific population counts and LGA case counts indicated heterogeneity across vulnerability strata. The low-vulnerability population correlated strongly and significantly with case counts (r = 0.914, p = 0.001; Benjamini–Hochberg-adjusted p = 0.003). The medium-vulnerability population showed a moderate but non-significant correlation (r = 0.618, p = 0.102; adjusted p = 0.153). The high-vulnerability population was uncorrelated with case counts (r = − 0.069, p = 0.870). Total population size correlated positively with cases (r = 0.719, p = 0.044), underscoring the scale dependence of absolute counts. 4. Discussion The AHP results, which prioritised proximity to streams and wetlands alongside rainfall and microtopographic wetness, are epidemiologically coherent for a low-relief, estuarine delta where the Anopheles gambiae complex and brackish-tolerant An. melas exploit shallow, sunlit, and seasonally persistent water bodies ( 8 , 53 ). Land surface temperature’s moderate weight aligns with the strong temperature dependence of vector development and Plasmodium sporogony, with transmission potential peaking at intermediate temperatures typical of the lowland tropics ( 9 , 38 ). The comparatively smaller weights assigned to slope and NDVI reflect Bayelsa’s exceptionally low relief and the dominance of fine-scale hydrology and inundation over terrain steepness and broad greenness in shaping larval habitat suitability. Accessibility and built environment variables made modest contributions, appropriately reflecting their roles in modulating exposure, care-seeking, and reporting rather than intrinsic entomological hazard ( 10 , 54 ). The spatial patterning of the MVI shows a predominantly medium vulnerability with localised high-vulnerability pockets, which matches the heterogeneous malaria ecology of southern Nigeria and supports risk-stratified planning advocated by the National Malaria Elimination Programme and WHO ( 31 , 55 – 57 ). LGAs such as Southern Ijaw, Brass, and Sagbama contain sizeable high-vulnerability areas, whereas Kolokuma/Opokuma, Ogbia, and Yenagoa retain substantial low-vulnerability extents despite statewide predominance of medium vulnerability. Population overlays underline that large absolute numbers of residents live in medium-vulnerability zones, which may serve as substantial reservoirs of transmission even when the ecological hazard is not extreme. The empirical relationships between vulnerability strata and routine case counts require careful interpretation. The lack of correlation between high-vulnerability population and reported cases could reflect limited statistical power (only eight LGAs), classification mismatch with realised transmission in 2024, or reporting artefacts in hard-to-reach, riverine areas where barriers to diagnosis and care can attenuate the link between ecological vulnerability and routine surveillance ( 58 ). It is also plausible that targeted interventions prioritised to more vulnerable localities (e.g., LLIN campaigns, community case management, IPTp) reduced symptomatic case burdens relative to population at risk within the public reporting system ( 1 , 31 ). By contrast, the strong positive correlation between the number of residents in low-vulnerability areas and case counts likely reflects two mechanisms. First, urban and peri-urban settings are often classified as lower ecological vulnerability, yet can sustain appreciable transmission via man-made larval habitats, insecticide resistance, and indoor biting despite LLIN availability ( 59 , 60 ). Second, better care access and reporting completeness in such settings strengthen the correspondence between population size and notified cases ( 56 , 58 ). The positive correlation between total population and cases is epidemiologically expected because absolute counts scale with population size; this underscores the value of rate-based metrics or count models with population offsets when comparing LGAs ( 61 , 62 ). Methodologically, future work should relate LGA-level incidence (or case counts with a log-population offset) to the proportions of residents in each vulnerability class, along with climate covariates and intervention coverage, ideally using Poisson or negative binomial models with random effects to handle extra-Poisson variation and unmeasured heterogeneity ( 2 , 61 ). Given the compositional nature of the three class proportions, an isometric log-ratio transform or the use of a referent class can prevent collinearity. The MVI itself could be refined by incorporating explicit indicators of health care access, diagnostic use, and reporting completeness as a second dimension of vulnerability, separate from ecological receptivity, to improve its ability to anticipate observed routine burdens ( 58 ). Limitations include the temporal mismatch between 2020 denominators and 2024 cases, potential MAUP effects from LGA aggregation, the subjectivity inherent in AHP, and small-sample uncertainty. We mitigated these by aligning all raster operations to the Landuse/cover grid, using proportions where feasible, preserving fixed thresholds for class definitions, and verifying internal consistency of AHP judgments. Nonetheless, conclusions regarding associations with routine case counts should be considered preliminary and hypothesis-generating. 5. Conclusion In Bayelsa State, vulnerability to malaria is overwhelmingly structured by hydrology and hydroclimate, with proximity to streams and wetlands, rainfall, and microtopographic wetness driving the MVI. The landscape is dominated by medium vulnerability, with localised high-vulnerability pockets concentrated in riverine LGAs. In routine 2024 data, absolute case counts rose with total population and correlated strongly with the number of residents in areas classified as low vulnerability, but not with those in high-vulnerability zones. These findings likely reflect a combination of ecological and health-system processes and the intrinsic scaling of counts with population size. Incorporating access and reporting indicators into the vulnerability framework and moving to rate-based or offset count models will yield more policy-relevant inference on how vulnerability classes map onto malaria risk, thereby strengthening subnational stratification and targeting in Bayelsa. Abbreviations MVI Malaria Vunerability Index LGA Local Government Areas MAUP Modifiable area unit problem AHP Analytic Hierarchy Process MCDA Multi–Criteria Decision Analysis NDVI Normalized Difference Vegetation Index GIS Geographic Information System CR Consistency Ratio CI Consistency Index CIs Confidence Interval WLC Weighted Linear Combination ITN/IRS Insecticide Treatment Net/ Indoor Residual Spraying LULC Land use/ Land Cover NASA National Aeronautics and Space Administration USGS United States Geological Survey SRTM Shuttle Radar Topography Mission DEM Digital Elevation Model TWI Topographic Wetness Index WHO World Health Organization LST Land Surface Temperature LLIN Long Lasting Insecticide Treatment IPTP Intermittent Preventive Treatment in Pregnancy Declarations Ethics approval and consent to participate Not applicable Consent for publication Not applicable Availability of data and materials Data sharing is not applicable to this article as no datasets were generated or analysed during the current study. Competing interests The authors declare that they have no competing interests Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors. Authors' contributions OA prepared and wrote the initial draft, performed and computed the analysis. OJT and JOO supported the draft preparation and the analysis and the modelling computations. TB and GW contributed to data curation, including maintaining the research data for initial use and later reuse GE , DB, FO, COU and IE critically reviewed and edited the manuscript. CK provided leadership of project design, supervision of project delivery, and supervisory authorship of our manuscript. All authors read and approved the final manuscript. Acknowledgements References 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. 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1","display":"","copyAsset":false,"role":"figure","size":206375,"visible":true,"origin":"","legend":"\u003cp\u003eAdministrative Map of Bayelsa State, Nigeria, Showing Local Government Area.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8250166/v1/532ee1aca559ac8ab7e355bb.png"},{"id":99307418,"identity":"2d655f42-0c8d-4ab0-8ceb-1344d1ef9d3a","added_by":"auto","created_at":"2025-12-31 16:06:14","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":440489,"visible":true,"origin":"","legend":"\u003cp\u003eMalaria Vulnerability Map of Bayelsa State, Nigeria\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8250166/v1/47ef52f7849d6f7f3da2a4b0.png"},{"id":99307450,"identity":"cf518a7a-8362-466b-9bbc-4ef9df1b7618","added_by":"auto","created_at":"2025-12-31 16:06:16","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":316157,"visible":true,"origin":"","legend":"\u003cp\u003eMalaria Vulnerability Categories in Bayelsa State, Nigeria\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8250166/v1/93d0ca9c22d0b27df160963b.png"},{"id":107351933,"identity":"7e5d284c-5593-40a6-b2c3-0cca23ea32cf","added_by":"auto","created_at":"2026-04-20 16:12:47","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1606240,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8250166/v1/0fcf9436-7a50-4a4d-925b-79b16e2a4309.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Analytical Hierarchical Process for Modelling Malaria Vulnerability Index Among Local Government Areas in Bayelsa State, Nigeria","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eMalaria remains a leading cause of preventable morbidity and mortality in sub-Saharan Africa, with Nigeria persistently accounting for a substantial share of the continent\u0026rsquo;s \u003cem\u003ePlasmodium falciparum\u003c/em\u003e burden (\u003cspan additionalcitationids=\"CR2 CR3\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Transmission is intensely heterogeneous over short distances due to interactions among climate, hydrology, vector ecology, human settlement, and access to prevention and care (\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). Bayelsa State, situated in the coastal Niger Delta, exemplifies a hydro-ecological template of low elevation, mangrove-swamp mosaics, and high, year-round rainfall that sustains \u003cem\u003eAnopheles\u003c/em\u003e receptivity and complicates uniform program strategies (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). Socio-environmental conditions, including housing quality and peri-domestic exposure, further modulate risk, reinforcing the need to integrate multiple determinants when assessing vulnerability (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTo guide intervention planning in such settings, decision-ready metrics that integrate multiple vulnerability drivers are essential. Multicriteria decision analysis (MCDA) provides a principled framework for integrating diverse malaria determinants into a single, interpretable index, aligning with the broader movement toward transparent, evidence-based priority setting in health(\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e) (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). Within Geographic Information System (GIS) based MCDA, the Analytic Hierarchy Process (AHP) formalises expert judgment into weights while enforcing internal coherence via a consistency ratio (CR), and Weighted Linear Combination (WLC) offers an intuitive overlay for combining normalised criteria into continuous vulnerability surfaces (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). In infectious disease and malaria risk assessment, GIS-MCDA approaches have proved useful for stratification, site selection, and targeting of interventions by combining remotely sensed layers (e.g., land cover, elevation, Normalized Difference Vegetation Index (NDVI), rainfall) with program data (e.g., Insecticide Treated Net/ Indoor Residual Spraying (ITN/IRS) coverage, test positivity) to capture vulnerability that no single indicator can reveal (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). Methodological advances, including sensitivity analysis to examine the stability of results underweight perturbations, strengthen the credibility and transferability of MCDA outputs (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). While malaria risk mapping has widely leveraged remote sensing and routine surveillance to characterise spatial heterogeneity reproducible AHP\u0026ndash;WLC applications that transparently document weighting, produce LGA-resolved indices, estimate population exposure by risk class, and validate composite outputs against routine caseloads remain sparse in high-burden, ecologically complex settings like Bayelsa State, Nigeria (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThese gaps are operationally consequential in Bayelsa, where decision makers must prioritise interventions across hydrologically diverse LGAs with varying access to prevention and care. Existing subnational risk assessments often rely on single-domain proxies (e.g., environmental suitability alone or routine incidence alone), apply ad-hoc or opaque weighting, and seldom quantify uncertainty or assess convergence with epidemiological indicators, and these limitations blunt their programmatic value (\u003cspan additionalcitationids=\"CR15\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e); (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e); (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). To address these needs, this study develops a transparent, reproducible Malaria Vulnerability Index (MVI) for Bayelsa State using AHP and WLC, tightly coupled to routine data. Our objectives are to develop a comprehensive malaria vulnerability assessment for Bayelsa State through the creation of a consistent pairwise comparative matrix of vulnerability indicators, generation of a composite malaria vulnerability index map, ranking of Local Government Areas based on vulnerability scores, estimation of population proportions within each vulnerability category, and analysis of the relationship between confirmed uncomplicated malaria cases and areas under different vulnerability classifications. Together, these objectives link mechanistic vulnerability to observed burden and quantify how many people reside in each class, which is key information for prioritisation (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e"},{"header":"2. Methodology","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003e2.1 Data sources and processing\u003c/h2\u003e\n \u003cp\u003eWe constructed a spatially explicit Malaria Vulnerability Index (MVI) for Bayelsa State, Nigeria, by integrating harmonised geospatial datasets on hydroclimate, terrain, land use/land cover (LULC), hydrology, accessibility, population, administrative boundaries, and routine malaria surveillance. Details of these are presented in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eData Sources and Processing for Malaria Vulnerability Index (MVI)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDataset\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSource\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eResolution\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eApplication\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDigital Elevation Model (DEM)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNASA/USGS Shuttle Radar Topography Mission (SRTM v3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 arc-second (~\u0026thinsp;30 m)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTopographic and terrain derivatives; slope and Topographic Wetness Index (TWI) computation for hydrological modelling.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSlope \u0026amp; Topographic Wetness Index (TWI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDerived from SRTM DEM using hydrologic conditioning (D8 flow direction/accumulation framework)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e~\u0026thinsp;30 m\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIndicators of surface runoff, soil moisture, and mosquito habitat suitability.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePrecipitation (CHIRPS v2.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eClimate Hazards Group InfraRed Precipitation with Stations (\u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.05\u0026deg; (~\u0026thinsp;5 km)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHydroclimate driver of mosquito breeding and malaria transmission risk.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLand Surface Temperature (LST, MOD11A2 v6.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMODIS Terra (\u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 km\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eThermal suitability for malaria vectors and parasite development.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVegetation Index (NDVI, MCD13Q1 v6.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMODIS Terra/Aqua combined (\u003cspan class=\"CitationRef\"\u003e21\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e250 m\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProxy for vegetation cover, mosquito resting/breeding habitats.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLand Use/Land Cover (LULC)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEsri/Microsoft Impact Observatory Global Land Cover (\u003cspan class=\"CitationRef\"\u003e22\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10 m\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBinary masks \u0026amp; proximity surfaces for open water, wetlands, cropland, built-up areas, trees, rangeland; linked to malaria vulnerability.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHydrologic Networks (Rivers/Streams))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHydroSHEDS/HydroRIVERS (\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVector (polyline)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHydrologically consistent networks for computing proximity to rivers/water bodies\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAccessibility Boundaries (Road Networks)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOpenStreetMap (\u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVector (line features)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAccessibility surfaces; Euclidean distance to transportation routes.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHealth Facilities (Public \u0026amp; Private)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGRID3 Nigeria (\u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGeocoded point features\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEuclidean distance to facilities; health service accessibility.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAdministrative Boundaries (LGA)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGRID3 Nigeria (\u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVector (Polygon)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAlignment with reporting units; aggregation of surveillance and demographic data\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePopulation Counts Data\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWorldPop 2020 Nigeria gridded counts (\u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e~\u0026thinsp;100 m\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDenominators for malaria incidence calculation; population vulnerability mapping.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRoutine Malaria Surveillance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDHIS2-based Health Management Information System (2024)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLGA-level counts\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLaboratory-confirmed malaria cases; incidence calculation following WHO standards.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003e2.2 Data Analysis\u003c/h2\u003e\n \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e\n \u003ch2\u003e2.2.1 Data harmonisation\u003c/h2\u003e\n \u003cp\u003eAll spatial data sets were standardised to a single analysis grid to avoid resampling errors(\u003cspan class=\"CitationRef\"\u003e28\u003c/span\u003e)(\u003cspan class=\"CitationRef\"\u003e29\u003c/span\u003e) The study used the WGS 84/UTM Zone 32N coordinate system for metric accuracy. The Landuse/Landcover (LULC) raster, with a native resolution of 10m, served as the reference (snap raster)and defined the processing cell size. All rasters were aligned to this grid, while vector layers were reprojected before rasterisation.\u003c/p\u003e\n \u003cp\u003eWe utilised malaria case data for 2024, whereas population denominators were derived from WorldPop 2020. Although this introduces a temporal mismatch, such is common in subnational studies. The use of externally validated WorldPop estimates reduces the risk of denominator bias and was accounted for in the uncertainty assessment (\u003cspan class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e31\u003c/span\u003e).\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e\n \u003ch2\u003e2.2.2 Criteria selection and surface derivation\u003c/h2\u003e\n \u003cp\u003eCriteria were chosen to represent well-established drivers of malaria receptivity, exposure, and vulnerability in sub-Saharan Africa (\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e33\u003c/span\u003e). These included:\u003c/p\u003e\n \u003cul\u003e\n \u003cli\u003e\n \u003cp\u003eHydrology and moisture:open water, wetlands, Topographic Wetness Index (TWI), proximity to rivers. These capture larval habitat availability and persistence (\u003cspan class=\"CitationRef\"\u003e34\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e36\u003c/span\u003e).\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eClimate and greenness: Precipitation, land surface temperature (LST), NDVI. These approximate moisture and thermal suitability for vector and parasite development (\u003cspan class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e37\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e40\u003c/span\u003e).\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eTerrain: Elevation, slope. These modulates temperature and drainage (\u003cspan class=\"CitationRef\"\u003e36\u003c/span\u003e).\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eLULC and accessibility variables:Proximity to cropland, rangeland, trees, built-up and bare ground; distance to roads; distance to health facilities. These reflect human\u0026ndash;-=environment interactions and care access (\u003cspan class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e41\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e43\u003c/span\u003e).\u003c/p\u003e\n \u003c/li\u003e\n \u003c/ul\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e\n \u003ch2\u003e2.2.3 Standardisation and vulnerability coding\u003c/h2\u003e\n \u003cp\u003eTo place heterogeneous inputs on a common scale and direction, all rasters were linearly rescaled to [0, 1], with 0 denoting the highest vulnerability and 1 denoting the lowest. Transformations were guided by epidemiological evidence:\u003c/p\u003e\n \u003cul\u003e\n \u003cli\u003e\n \u003cp\u003eVariables positively associated with vulnerability (higher raw values imply higher risk) were mapped with decreasing transforms so that higher raw values yield lower standardised scores. This applied to open water and wetlands (presence), TWI, precipitation, proximity to rivers (shorter distances imply higher vulnerability), cropland presence, NDVI within the local dynamic range, and LST within transmission-relevant bounds (\u003cspan class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e40\u003c/span\u003e).\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eVariables negatively associated with vulnerability were mapped with increasing transforms so that higher raw values imply higher standardised scores; this applied to elevation, slope, and LULC classes generally protective or less favourable for stable transmission in the West African urban context (trees, rangeland, bare ground, built-up) (\u003cspan class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e44\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e46\u003c/span\u003e).\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eFor access variables, greater distance to roads or health facilities increases vulnerability; rescaling, therefore, yielded lower standardised scores at larger distances (\u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e43\u003c/span\u003e). Binary LULC presences received scores consistent with their direction (e.g., open water present\u0026thinsp;=\u0026thinsp;0; absent\u0026thinsp;=\u0026thinsp;1). Continuous variables were min\u0026ndash;max transformed within the study area.\u003c/p\u003e\n \u003c/li\u003e\n \u003c/ul\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e\n \u003ch2\u003e2.2.4 AHP Weighting\u003c/h2\u003e\n \u003cp\u003eWeights for the criteria selected after expert consultation and judgement were assigned using the Analytic Hierarchy Process (AHP).Expert judgments were elicited via pairwise comparisons on Saaty\u0026rsquo;s 1\u0026ndash;9 scale to form a 13 \u0026times; 13 reciprocal judgment matrix. Normalised criteria weights were extracted from the principal right eigenvector. Internal consistency was evaluated using the Consistency Index (CI) and Consistency ratio (CR).\u003c/p\u003e\n \u003cdiv id=\"Equa\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e$$\\:CI=\\frac{\\left({{\\lambda\\:}}_{max}-n\\right)}{\\left(n-1\\right)},\\hspace{1em}CR=\\frac{CI}{RI},\\hspace{1em}RI13=1.56$$\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003eWhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{{\\lambda\\:}}_{max}\\)\u003c/span\u003e\u003c/span\u003e is the Principal eigenvalue of the pairwise comparison matrix, n is the number of criteria(size of the matrix), RI is the random index, and RI13 is the Random index at the 13th criteria. Where \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:CR\\:\\le\\:\\:0.10\\)\u003c/span\u003e\u003c/span\u003e was considered an acceptable coherence (\u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e47\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e49\u003c/span\u003e).\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e\n \u003ch2\u003e2.2.4 Composite MVI construction and classification\u003c/h2\u003e\n \u003cp\u003eThe continuous MVI was computed as a weighted linear combination:\u003c/p\u003e\n \u003cdiv id=\"Equb\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\u003cimg src=\"data:image/png;base64,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\"\u003e\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{s}_{i\\left(x\\right)}\\)\u003c/span\u003e\u003c/span\u003e s is the standardised score for criterion \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:i\\)\u003c/span\u003e\u003c/span\u003e at location \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:x\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{w}_{i}\\)\u003c/span\u003e\u003c/span\u003e is its AHP-derived weight. Because of the coding, lower \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:MVI\\)\u003c/span\u003e\u003c/span\u003e values denote greater vulnerability. The continuous surface was reclassified into three ordinal classes (high, medium, low) using equal interval categorisation to ensure comparability (\u003cspan class=\"CitationRef\"\u003e14\u003c/span\u003e).\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e\n \u003ch2\u003e2.2.5 Population at risk\u003c/h2\u003e\n \u003cp\u003eWe quantified the population residing in each vulnerability class within each LGA by overlaying the raster WorldPop 2020 with class-specific binary masks (low, medium and high vulnerability). Class totals and proportions were summarised using zonal statistics with LGA polygons. Islands and open water within LGA boundaries were masked before extraction to avoid over-counting uninhabitable areas.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e\n \u003ch2\u003e2.2.6 Malaria incidence and association analysis\u003c/h2\u003e\n \u003cp\u003eConfirmed malaria cases for 2024 were aggregated at the LGA level. Incidence per 1000 population was calculated using the WorldPop denominators.(\u003cspan class=\"CitationRef\"\u003e31\u003c/span\u003e). Associations between incidence and MVI-derived population classes were evaluated using Pearson correlation coefficients, with 95% confidence intervals (CIs) reported. To adjust for multiple testing, the Benjamini\u0026ndash;Hochberg correction was applied. Because of potential non-normality and the modest sample size, Spearman rank correlations were also conducted as sensitivity analyses.\u003c/p\u003e\n \u003cp\u003ePaired differences between class-specific population counts and malaria cases were tested using paired t-tests, with statistical significance set at p\u0026thinsp;\u0026le;\u0026thinsp;0.05 (two-tailed). Effect sizes were calculated as Hedges\u0026rsquo; g, with corresponding 95% CIs. All interpretations emphasised the scale dependence of counts and the ecological nature of the analysis.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e\n \u003ch2\u003e2.2.7 Quality assurance and uncertainty\u003c/h2\u003e\n \u003cp\u003eWe appraised key sources of uncertainty: (i) temporal mismatch between 2020 denominators and 2024 cases, (ii) the modifiable areal unit problem (MAUP) due to aggregation at the LGA level, (iii) the subjectivity inherent in AHP, and (iv) classification and standardisation choices We mitigated these risks by aligning all raster operations to the Landuse/cover grid, analysing proportions where possible, using fixed reclassification thresholds, and checking the internal consistency of AHP weights. Spatial dependence was considered using Moran\u0026rsquo;s I for LGA-level outcomes where relevant, with permutation-based inference to guard against inflated significance(\u003cspan class=\"CitationRef\"\u003e50\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e51\u003c/span\u003e).\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e\u003cb\u003e3.1 AHP-derived criterion weights\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eBayelsa State is a Nigerian state located within the Niger Delta region. Administratively, it has eight Local Government Areas (LGAs) and 105 local wards (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The LGAs are divided into two: four upland and four riverine. Dominant vegetation comprises mangroves and freshwater swamp forest. (\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWe derived the criterion weights for the Bayelsa State malaria vulnerability index using Saaty\u0026rsquo;s Analytic Hierarchy Process (AHP). After redundancy screening and expert review, thirteen criteria were retained for the Analytic Hierarchy Process (AHP) weighting to balance parsimony and ecological coverage. Class-specific rasters and distance surfaces were derived as described above. Distances to rivers, roads, and health facilities were computed under planar (UTM) geometry and constrained to the state boundary before normalisation. Slope was expressed as a per cent rise. The 13\u0026times;13 pairwise comparison matrix was built to reflect the ecology of malaria vectors in the Niger Delta, where dense hydrographic networks, seasonally inundated wetlands, and low relief dominate the landscape, and was solved with the eigenvalue method (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The normalised priority vector places the strongest emphasis on hydrological and hydro-meteorological determinants. Distance to streams emerged as the most influential criterion (w\u0026thinsp;\u0026asymp;\u0026thinsp;0.218), followed by distance to wetland areas (w\u0026thinsp;\u0026asymp;\u0026thinsp;0.199), precipitation (w\u0026thinsp;\u0026asymp;\u0026thinsp;0.128), and the topographic wetness index (TWI; w\u0026thinsp;\u0026asymp;\u0026thinsp;0.099) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Land surface temperature (LST) carried a moderate weight (w\u0026thinsp;\u0026asymp;\u0026thinsp;0.082), consistent with its well-documented role in modulating vector and parasite development. Secondary contributions were assigned to distance from cropland (w\u0026thinsp;\u0026asymp;\u0026thinsp;0.068) and elevation (w\u0026thinsp;\u0026asymp;\u0026thinsp;0.049), while slope and NDVI had smaller, roughly equivalent weights (both w\u0026thinsp;\u0026asymp;\u0026thinsp;0.032). Access and infrastructure-related factors such as distance to healthcare facilities, distance to the road network, and distance from built-up areas were given modest and nearly identical weights (each w\u0026thinsp;\u0026asymp;\u0026thinsp;0.026). Distance from bare ground made the smallest contribution (w\u0026thinsp;\u0026asymp;\u0026thinsp;0.011). The priority ordering therefore followed: Distance to stream\u0026thinsp;\u0026gt;\u0026thinsp;Distance to wetland\u0026thinsp;\u0026gt;\u0026thinsp;Precipitation\u0026thinsp;\u0026gt;\u0026thinsp;TWI\u0026thinsp;\u0026gt;\u0026thinsp;LST\u0026thinsp;\u0026gt;\u0026thinsp;Distance from cropland\u0026thinsp;\u0026gt;\u0026thinsp;Elevation\u0026thinsp;\u0026gt;\u0026thinsp;Slope\u0026thinsp;\u0026asymp;\u0026thinsp;NDVI\u0026thinsp;\u0026gt;\u0026thinsp;Distance to healthcare\u0026thinsp;\u0026asymp;\u0026thinsp;Distance to roads\u0026thinsp;\u0026asymp;\u0026thinsp;Distance from built-up\u0026thinsp;\u0026gt;\u0026thinsp;Distance from bare ground. Model consistency was high: the maximum eigenvalue was approximately 13.64, yielding a consistency index (CI) of 0.0536 and a consistency ratio (CR) of 0.0343 using Saaty\u0026rsquo;s random index for n\u0026thinsp;=\u0026thinsp;13 (RI\u0026thinsp;=\u0026thinsp;1.56), well below the 0.10 threshold (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e).\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\u003ePairwise Comparison Matrix using the AHP method\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"14\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCriteria\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eC1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eC2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eC4\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eC5\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eC6\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eC7\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eC8\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eC9\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eC10\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003eC11\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\u003e \u003cp\u003eC12\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c14\"\u003e \u003cp\u003eC13\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDistance to Stream (C1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDistance to Wetland Area (C2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrecipitation (C3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTopographic Wetness Index (TWI) (C4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLand Surface Temperature (LST) (C5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDistance from Cropland Area (C6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eElevation (C7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSlope (C8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNormalised Vegetation Index (NDVI) (C9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDistance to Healthcare Facilities (C10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDistance to Road Network (C11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDistance from Built-up Area (C12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDistance from Bareground (C13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eColumn Sum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.225\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.472\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e14.109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e16.776\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e24.199\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e32.200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e32.200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e38.333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e38.333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e38.333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e79.000\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\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\u003eAnalytical Hierarchical Process Weighted Priority Matrix\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCriteria\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePriority Weight\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePercentage\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDistance to Stream\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.2186\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21.90%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDistance to Wetland Area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.1999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20.00%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrecipitation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.1288\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12.90%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTopographic Wetness Index (TWI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.0991\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.90%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLand Surface Temperature (LST)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.082\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.20%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDistance from Cropland Area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.0686\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.90%\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\u003e0.0492\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.90%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSlope\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.0321\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.20%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNormalised Vegetation Index (NDVI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.0321\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.20%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDistance to Healthcare Facilities\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.0261\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.60%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDistance to Road Network\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.0261\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.60%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDistance from Built-up Area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.0261\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.60%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDistance from Bareground\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.0112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.10%\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=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Statewide distribution of vulnerability classes\u003c/h2\u003e \u003cp\u003eAcross Bayelsa State, the MVI was dominated by the medium vulnerability class, which covered 77.1% of the area, with low and high classes covering 17.2% and 5.7%, respectively (Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Medium vulnerability was the largest component in every LGA, ranging from 57.20% in Kolokuma/Opokuma to 89.05% in Nembe. Brass (8.84%), Sagbama (8.67%), and Southern Ijaw (7.74%) exhibited comparatively larger high-vulnerability areas, whereas Ogbia (0.24%), Kolokuma/Opokuma (0.67%), and Yenagoa (1.75%) had very small high-vulnerability fractions (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Low vulnerability was most prominent in Kolokuma/Opokuma (42.12%), Ogbia (34.25%), and Yenagoa (32.77%).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \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\u003eAreal extent of MVI classes by LGA (area units; row percentages in parentheses)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLGANAME\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHighly Vulnerable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModerately Vulnerable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLow Vulnerable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\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\u003eEkeremor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e89.42 (4.99%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1399.9 (78.05%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e304.29 (16.97%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1793.61\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\u003e207.38 (7.74%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2317.48 (86.47%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e155.38 (5.80%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2680.24\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\u003e21.73 (2.79%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e692.88 (89.05%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e63.5 (8.16%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e778.11\u003c/p\u003e \u003c/td\u003e \u003c/tr\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\u003e99.41 (8.84%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e866.62 (77.10%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e158.06 (14.06%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1124.09\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\u003e1.61 (0.24%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e445.63 (65.51%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e232.96 (34.25%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e680.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYenegoa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11.34 (1.75%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e423.39 (65.48%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e211.89 (32.77%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e646.62\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\u003e2.41 (0.67%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e204.53 (57.20%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e150.6 (42.12%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e357.54\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\u003e81.88 (8.67%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e593.55 (57.20%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e268.82 (42.12%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e944.25\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\u003e515.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6943.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1545.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9004.66\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\u003eLGAs contributing most to the statewide high-vulnerability area were Southern Ijaw (40.25%), Brass (19.30%), Ekeremor (17.36%), and Sagbama (15.89%) (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). For medium vulnerability, Southern Ijaw (33.37%) and Ekeremor (20.16%) dominated, with Brass (12.48%) and Nembe (9.98%) also contributing appreciably. Low-vulnerability contributions were led by Ekeremor (19.69%) and Sagbama (17.39%), followed by Ogbia (15.07%) and Yenagoa (13.71%).\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\u003ePercentage Contribution of LGA to Various Vulnerability Index\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLGANAME\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh Vulnerability\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMedium Vulnerability\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLow Vulnerability\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\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\u003e17.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e19.69\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\u003e40.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e33.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10.05\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\u003e4.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.11\u003c/p\u003e \u003c/td\u003e \u003c/tr\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\u003e19.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10.23\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\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYenegoa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13.71\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\u003e0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9.74\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\u003e15.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17.39\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=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Population at risk by LGA\u003c/h2\u003e \u003cp\u003eAt the state level, 3.63% of residents lived in high-vulnerability zones, 74.66% in medium, and 21.70% in low vulnerability zones. The medium vulnerability areas represented the majority in every LGA, ranging from 56.91% in Kolokuma/Opokuma to 84.56% in Nembe (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). High-vulnerability proportions were modest but heterogeneous, spanning 0.32\u0026ndash;8.25%, with Sagbama (8.25%), Southern Ijaw (6.09%), Brass (5.40%), and Ekeremor (4.64%) at the upper end. Low vulnerability was most prominent in Kolokuma/Opokuma (42.77%), Ogbia (29.08%), Sagbama (28.46%), and Yenagoa (27.63%).\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\u003ePopulation in each MVI class by LGA (counts; row percentages in parentheses)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLGA Name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh Vulnerability Population\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMedium Vulnerability Population\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLow Vulnerability Population\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\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\u003eEkeremor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e17,900.07 (4.64%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e314,440.13 (81.47%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e53,635.30 (13.90%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e385,975.51\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\u003e30,070.70 (6.09%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e407,011.32 (82.42%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e56,726.32 (11.49%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e493,808.34\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\u003e2,401.26 (1.41%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e143,834.60 (84.56%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e23,861.34 (14.03%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e170,097.20\u003c/p\u003e \u003c/td\u003e \u003c/tr\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\u003e7,700.30 (5.40%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e107,423.43 (75.31%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e27,514.50 (19.29%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e142,638.23\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\u003e1,357.97 (0.48%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e199,348.57 (70.44%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e82,295.23 (29.08%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e283,001.77\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYenegoa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5,381.89 (0.93%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e411,440.51 (71.43%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e159,151.00 (27.63%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e575,973.40\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\u003e345.54 (0.32%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e61,018.05 (56.91%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e45,858.84 (42.77%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e107,222.44\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\u003e23,742.34 (8.25%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e182,155.90 (63.29%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e81,920.12 (28.46%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e287,818.36\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\u003e88,900.08 (3.63%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,826,672.51 (74.66%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e530,962.65 (21.70%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2,446,535.25\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=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Association between vulnerability classes and malaria cases\u003c/h2\u003e \u003cp\u003eAcross LGAs, the mean malaria cases in 2024 were 8,143.6. Paired comparisons between class-specific population counts and case counts primarily reflected differences in scale; as expected, medium- and low-vulnerability populations substantially exceeded case counts (medium: mean difference 220,190.4, 95% CI 111,877.0\u0026ndash;328,503.9, p\u0026thinsp;=\u0026thinsp;0.002; low: 58,226.7, 95% CI 27,551.5\u0026ndash;88,901.9, p\u0026thinsp;=\u0026thinsp;0.003), yielding large effect sizes (Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). For the high-vulnerability class, the mean difference was small and non-significant (2,968.9, 95% CI \u0026minus;\u0026thinsp;8,644.4 to 14,582.1, p\u0026thinsp;=\u0026thinsp;0.565).\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\u003ePaired Sample T-Test Between Vulnerability Categories and Malaria Incidents in 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=\"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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"10\" nameend=\"c10\" namest=\"c1\"\u003e \u003cp\u003ePaired Samples Test\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" morerows=\"2\" nameend=\"c2\" namest=\"c1\" rowspan=\"3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c7\" namest=\"c3\"\u003e \u003cp\u003ePaired Differences\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003et\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eDf\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eSig. (2-tailed)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eStd. Deviation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eStd. Error Mean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e95% Confidence Interval (CI) of the Difference\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLower\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eUpper\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePair 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh Vulnerability Population - Malaria2024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2968.88375\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13891.08420\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4911.23992\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-8644.35327\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e14582.12077\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.605\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.565\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePair 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMedium Vulnerability Population - Malaria2024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e220190.43875\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e129558.29020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e45805.77278\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e111876.99758\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e328503.87992\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4.807\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePair 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow Vulnerability Population - Malaria2024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e58226.70625\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e36691.88070\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12972.53883\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e27551.52633\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e88901.88617\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4.488\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePair 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal - Malaria 2024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e297673.28125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e163889.89324\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e57943.82744\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e160657.90166\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e434688.66084\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5.137\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.001\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\u003ePearson correlations between class-specific population counts and LGA case counts indicated heterogeneity across vulnerability strata. The low-vulnerability population correlated strongly and significantly with case counts (r\u0026thinsp;=\u0026thinsp;0.914, p\u0026thinsp;=\u0026thinsp;0.001; Benjamini\u0026ndash;Hochberg-adjusted p\u0026thinsp;=\u0026thinsp;0.003). The medium-vulnerability population showed a moderate but non-significant correlation (r\u0026thinsp;=\u0026thinsp;0.618, p\u0026thinsp;=\u0026thinsp;0.102; adjusted p\u0026thinsp;=\u0026thinsp;0.153). The high-vulnerability population was uncorrelated with case counts (r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.069, p\u0026thinsp;=\u0026thinsp;0.870). Total population size correlated positively with cases (r\u0026thinsp;=\u0026thinsp;0.719, p\u0026thinsp;=\u0026thinsp;0.044), underscoring the scale dependence of absolute counts.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThe AHP results, which prioritised proximity to streams and wetlands alongside rainfall and microtopographic wetness, are epidemiologically coherent for a low-relief, estuarine delta where the Anopheles gambiae complex and brackish-tolerant An. melas exploit shallow, sunlit, and seasonally persistent water bodies (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e). Land surface temperature\u0026rsquo;s moderate weight aligns with the strong temperature dependence of vector development and Plasmodium sporogony, with transmission potential peaking at intermediate temperatures typical of the lowland tropics (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e). The comparatively smaller weights assigned to slope and NDVI reflect Bayelsa\u0026rsquo;s exceptionally low relief and the dominance of fine-scale hydrology and inundation over terrain steepness and broad greenness in shaping larval habitat suitability. Accessibility and built environment variables made modest contributions, appropriately reflecting their roles in modulating exposure, care-seeking, and reporting rather than intrinsic entomological hazard (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe spatial patterning of the MVI shows a predominantly medium vulnerability with localised high-vulnerability pockets, which matches the heterogeneous malaria ecology of southern Nigeria and supports risk-stratified planning advocated by the National Malaria Elimination Programme and WHO (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan additionalcitationids=\"CR56\" citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e). LGAs such as Southern Ijaw, Brass, and Sagbama contain sizeable high-vulnerability areas, whereas Kolokuma/Opokuma, Ogbia, and Yenagoa retain substantial low-vulnerability extents despite statewide predominance of medium vulnerability. Population overlays underline that large absolute numbers of residents live in medium-vulnerability zones, which may serve as substantial reservoirs of transmission even when the ecological hazard is not extreme.\u003c/p\u003e \u003cp\u003eThe empirical relationships between vulnerability strata and routine case counts require careful interpretation. The lack of correlation between high-vulnerability population and reported cases could reflect limited statistical power (only eight LGAs), classification mismatch with realised transmission in 2024, or reporting artefacts in hard-to-reach, riverine areas where barriers to diagnosis and care can attenuate the link between ecological vulnerability and routine surveillance (\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e). It is also plausible that targeted interventions prioritised to more vulnerable localities (e.g., LLIN campaigns, community case management, IPTp) reduced symptomatic case burdens relative to population at risk within the public reporting system (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBy contrast, the strong positive correlation between the number of residents in low-vulnerability areas and case counts likely reflects two mechanisms. First, urban and peri-urban settings are often classified as lower ecological vulnerability, yet can sustain appreciable transmission via man-made larval habitats, insecticide resistance, and indoor biting despite LLIN availability (\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e). Second, better care access and reporting completeness in such settings strengthen the correspondence between population size and notified cases (\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e). The positive correlation between total population and cases is epidemiologically expected because absolute counts scale with population size; this underscores the value of rate-based metrics or count models with population offsets when comparing LGAs (\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMethodologically, future work should relate LGA-level incidence (or case counts with a log-population offset) to the proportions of residents in each vulnerability class, along with climate covariates and intervention coverage, ideally using Poisson or negative binomial models with random effects to handle extra-Poisson variation and unmeasured heterogeneity (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e). Given the compositional nature of the three class proportions, an isometric log-ratio transform or the use of a referent class can prevent collinearity. The MVI itself could be refined by incorporating explicit indicators of health care access, diagnostic use, and reporting completeness as a second dimension of vulnerability, separate from ecological receptivity, to improve its ability to anticipate observed routine burdens (\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eLimitations include the temporal mismatch between 2020 denominators and 2024 cases, potential MAUP effects from LGA aggregation, the subjectivity inherent in AHP, and small-sample uncertainty. We mitigated these by aligning all raster operations to the Landuse/cover grid, using proportions where feasible, preserving fixed thresholds for class definitions, and verifying internal consistency of AHP judgments. Nonetheless, conclusions regarding associations with routine case counts should be considered preliminary and hypothesis-generating.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eIn Bayelsa State, vulnerability to malaria is overwhelmingly structured by hydrology and hydroclimate, with proximity to streams and wetlands, rainfall, and microtopographic wetness driving the MVI. The landscape is dominated by medium vulnerability, with localised high-vulnerability pockets concentrated in riverine LGAs. In routine 2024 data, absolute case counts rose with total population and correlated strongly with the number of residents in areas classified as low vulnerability, but not with those in high-vulnerability zones. These findings likely reflect a combination of ecological and health-system processes and the intrinsic scaling of counts with population size. Incorporating access and reporting indicators into the vulnerability framework and moving to rate-based or offset count models will yield more policy-relevant inference on how vulnerability classes map onto malaria risk, thereby strengthening subnational stratification and targeting in Bayelsa.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMVI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMalaria Vunerability Index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLGA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLocal Government Areas\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMAUP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eModifiable area unit problem\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAHP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAnalytic Hierarchy Process\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMCDA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMulti\u0026ndash;Criteria Decision Analysis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNDVI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNormalized Difference Vegetation Index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGIS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGeographic Information System\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eConsistency Ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eConsistency Index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCIs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eConfidence Interval\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eWLC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eWeighted Linear Combination\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eITN/IRS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eInsecticide Treatment Net/ Indoor Residual Spraying\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLULC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLand use/ Land Cover\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNASA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNational Aeronautics and Space Administration\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eUSGS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eUnited States Geological Survey\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSRTM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eShuttle Radar Topography Mission\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDEM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDigital Elevation Model\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTWI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTopographic Wetness Index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eWHO\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eWorld Health Organization\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLST\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLand Surface Temperature\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLLIN\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLong Lasting Insecticide Treatment\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIPTP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eIntermittent Preventive Treatment in Pregnancy\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u0026nbsp;\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\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eData sharing is not applicable to this article as no datasets were generated or analysed during the current study.\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\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOA\u003c/strong\u003e prepared and wrote the initial draft, performed and computed the analysis. \u003cstrong\u003eOJT\u0026nbsp;\u003c/strong\u003eand\u003cstrong\u003e\u0026nbsp;JOO\u0026nbsp;\u003c/strong\u003esupported the draft preparation and the analysis and the modelling computations. \u003cstrong\u003eTB\u0026nbsp;\u003c/strong\u003eand\u003cstrong\u003e\u0026nbsp;GW\u0026nbsp;\u003c/strong\u003econtributed to data curation, including maintaining the research data for initial use and later reuse \u003cstrong\u003eGE\u003c/strong\u003e, \u003cstrong\u003eDB, FO, COU\u0026nbsp;\u003c/strong\u003eand\u003cstrong\u003e\u0026nbsp;IE\u003c/strong\u003e critically reviewed and edited the manuscript. \u003cstrong\u003eCK\u003c/strong\u003e provided leadership of project design, supervision of project delivery, and supervisory authorship of our manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\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 Oct 16;526(7572):207\u0026ndash;11. \u003c/li\u003e\n\u003cli\u003eWeiss DJ, Nelson A, Vargas-Ruiz CA, Gligorić K, Bavadekar S, Gabrilovich E, et al. Global maps of travel time to healthcare facilities. Nat Med. 2020 Dec 28;26(12):1835\u0026ndash;8. \u003c/li\u003e\n\u003cli\u003eSnow RW. Global malaria eradication and the importance of Plasmodium falciparum epidemiology in Africa. BMC Med. 2015;13(1):23. \u003c/li\u003e\n\u003cli\u003eSnow RW, Guerra CA, Noor AM, Myint HY, Hay SI. The global distribution of clinical episodes of Plasmodium falciparum malaria. 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BMC Public Health. 2023 Nov 30;23(1):2380. \u003c/li\u003e\n\u003cli\u003eFMoH, NMEP. National Malaria Strategic Plan 2021\u0026ndash;2025. Abuja; 2020 Oct. \u003c/li\u003e\n\u003cli\u003eNMEP, NPC, ICF. Nigeria Malaria Indicator Survey 2021 Final Report. . Abuja; 2022. \u003c/li\u003e\n\u003cli\u003eWHO. Global technical strategy for malaria, 2016-2030. Global Malaria Programme, World Health Organization; 2021. 29 p. \u003c/li\u003e\n\u003cli\u003eAlegana VA, Wright JA, Pentrina U, Noor AM, Snow RW, Atkinson PM. Spatial modelling of healthcare utilisation for treatment of fever in Namibia. Int J Health Geogr. 2012;11(1):6. \u003c/li\u003e\n\u003cli\u003eHancock PA, Hendriks CJM, Tangena JA, Gibson H, Hemingway J, Coleman M, et al. Mapping trends in insecticide resistance phenotypes in African malaria vectors. PLoS Biol. 2020 Jun 25;18(6):e3000633. \u003c/li\u003e\n\u003cli\u003eTatem AJ, Gething PW, Smith DL, Hay SI. Urbanization and the global malaria recession. Malar J. 2013 Dec 17;12(1):133. \u003c/li\u003e\n\u003cli\u003eHilbe JM. Negative binomial regression. Cambridge University Press; 2011. 553 p. \u003c/li\u003e\n\u003cli\u003eMorgenstern H. Ecologic Studies in Epidemiology: Concepts, Principles, and Methods. Annu Rev Public Health. 1995 May;16(1):61\u0026ndash;81. \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Public Health](https://link.springer.com/journal/12982)","snPcode":"12982","submissionUrl":"https://submission.springernature.com/new-submission/12982/3","title":"Discover Public Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Malaria Vulnerability Index (MVI), Analytic Hierarchy Process (AHP), Multicriteria Decision Analysis (MCDA), Geographic Information System (GIS), Bayelsa State, Spatial Epidemiology, Health Geographics","lastPublishedDoi":"10.21203/rs.3.rs-8250166/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8250166/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003ePersistent malaria transmission in Africa underscores the need for spatially explicit tools that identify highly endemic areas for targeted control. Although multi-criteria decision analysis (MCDA) offers a structured approach, its application has been limited by outdated environmental inputs and inconsistent factor aggregation methods. This study developed an ecology-informed Malaria Vulnerability Index (MVI) for Bayelsa State, Nigeria, using up-to-date, open-source geospatial datasets and a transparent weighting framework.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThirteen environmental predictors were sourced from OpenStreetMap, Google Earth Engine, WorldPop, and GRID3. Using the Analytical Hierarchy Process (AHP), a 13\u0026times;13 pairwise comparison matrix was constructed and solved using the eigenvalue method to derive criterion weights. Weighted predictors were combined to generate the MVI, which was overlaid with gridded population data to quantify population exposure. Associations between population counts across low, medium, and high vulnerability zones and reported malaria cases were assessed using correlation analysis.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe highest-priority criteria were distance to streams, distance to wetlands, precipitation, topographic wetness index, and land surface temperature. Medium vulnerability dominated the landscape (77.1%), followed by low (17.2%) and high (5.7%) vulnerability. High-vulnerability areas were concentrated in riverine LGAs, particularly Southern Ijaw (40.25%), Brass (19.30%), Ekeremor (17.36%), and Sagbama (15.89%). Population exposure reflected these patterns: 3.63% of residents lived in high-vulnerability zones, 74.66% in medium, and 21.70% in low zones. Population in low-vulnerability areas showed a strong correlation with reported malaria cases (r\u0026thinsp;=\u0026thinsp;0.914), while total population also correlated with cases (r\u0026thinsp;=\u0026thinsp;0.719).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eMalaria vulnerability in Bayelsa State is primarily driven by hydrological and hydroclimatic conditions, especially proximity to streams and wetlands, rainfall, and microtopographic wetness. The AHP-based MCDA framework provides a rigorous and transparent approach for integrating environmental factors, supporting hydrology-focused targeting of malaria surveillance and vector control, and enabling reproducible MVI mapping using open-source geospatial data.\u003c/p\u003e","manuscriptTitle":"Analytical Hierarchical Process for Modelling Malaria Vulnerability Index Among Local Government Areas in Bayelsa State, Nigeria","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-22 17:21:44","doi":"10.21203/rs.3.rs-8250166/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-02-16T14:20:17+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-15T16:45:00+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-12T09:46:40+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"113949264669168435731297064871631174443","date":"2026-02-06T18:28:46+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"225138190963187364612213870050353808747","date":"2026-02-06T15:51:20+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"81417131931582448461883501214552657620","date":"2026-02-04T21:48:00+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-24T12:48:56+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"3837255515058804653167197839454598560","date":"2025-12-20T00:26:32+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"70687134928374957408567698330565137667","date":"2025-12-19T16:00:06+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"73193110683216162873919069371729866589","date":"2025-12-19T01:03:21+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-12-18T17:26:20+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-12-09T07:00:41+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-01T13:29:52+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-12-01T13:28:03+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Public Health","date":"2025-12-01T11:59:03+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Public Health](https://link.springer.com/journal/12982)","snPcode":"12982","submissionUrl":"https://submission.springernature.com/new-submission/12982/3","title":"Discover Public Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c3ce0ed8-5617-47b9-9af8-48506414f3ad","owner":[],"postedDate":"December 22nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-04-20T16:10:09+00:00","versionOfRecord":{"articleIdentity":"rs-8250166","link":"https://doi.org/10.1186/s12982-026-01927-w","journal":{"identity":"discover-public-health","isVorOnly":false,"title":"Discover Public Health"},"publishedOn":"2026-04-18 15:59:55","publishedOnDateReadable":"April 18th, 2026"},"versionCreatedAt":"2025-12-22 17:21:44","video":"","vorDoi":"10.1186/s12982-026-01927-w","vorDoiUrl":"https://doi.org/10.1186/s12982-026-01927-w","workflowStages":[]},"version":"v1","identity":"rs-8250166","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8250166","identity":"rs-8250166","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}