A Retrospective Spatio-Temporal Analysis of Malaria Prevalence in Kano State, Nigeria: A 20- Year Geospatial Assessment

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They contribute significantly to the total world malarial prevalence. In order to carry out the proposed spatio-temporal analysis of the malarial infection incidences in the 44 Local Government Areas (LGAs) of Kano State in Nigeria between 2000–2019, Geographic Information Systems (GIS), Hot Spot Analysis (Getis-Ord Gi*), Global Moran's Index Analysis, and ARIMA were employed. ARIMA time series modeling helped analyze the intensity of malarial infections. Henceforth, the obtained results clearly reveal positive autocorrelation among the malarial infection patterns across the 44 LGAs in Kano State between 2000–2019 at the significance level of p-value < 0.01. Moreover, the obtained Hot Spot Analysis revealed the intensified malarial infection patterns to exist in the northwestern as well as in the center of the Kano State. From the provided map images in the corresponding figures below, the following were revealed: the melded hot spot indicates the intensified malarial infections in the northwestern and centered Kano State LGAs. In the given map images below, the hot spot represents Doguwa and Gaya LGA as the intensified malarial infection area. On the other hand, hot spot analysis revealed the cold spot malarial infection patterns among the Southern as well as the Eastern LGAs. The malarial infection intensity indices among the 44 LGAs in Kano State between 2000–2019 were observed to be non stationary. Such patterns can be best described by ARIMA(1,1,1), emphasizing the strong dependency between the pattern intensity at a given time as well as the previous pattern intensities. Health sciences/Diseases Health sciences/Health care Physical sciences/Mathematics and computing Health sciences/Medical research Malaria Spatial Analysis GIS Hot Spot Analysis Kano State Nigeria Time Series ARIMA Prevalence Figures Figure 1 Figure 2 Figure 3 1. Introduction 1.1. Background and Literature Review Malaria has continued to represent a serious health concern across the world, with the worst effects being recorded in sub-Saharan Africa. According to the 2023 report published by the World Health Organization (WHO), “This region accounted for 94% of all malaria cases and 95% of all malaria deaths in 2023. Nigeria contributed 27% of the total burden of the disease” (WHO, 2023). The complexity of the vector-biasing environment that underpins the dynamics of malaria infection has been irrevocably entwined in a set of factors that are both environmental and socio-economic in nature. Such factors as temperature and rainfall patterns significantly influence the role of the Anopheles vector in the spread of the disease (Gething et al., 2011 ).On the other hand, other factors such as humidity levels, accessibility to water sources, the nature of housing structures, as well as the availability of health services, among other factors play a very important role in determining the geography of risk presented by the disease across the world (Akinbobola & Oluleye, 2010 ; Tusting et al., 2016 ). One of the most important steps forward in the field of malaria epidemiology has been the realization of the strongly sub-national heterogeneity of the disease. National or state-level prevalence rates tend to conceal strong localized pockets of infection, or “hotspots,” in which the level of infection tends to remain elevated. As described clearly by Bousema et al. in 2012, these hotspots form a “reserve” fueling the infection of neighboring zones. This micro-epidemiology represents a fundamental priority for the most effective allocation of publicly limited health resources. The coupling of Geographic Information Systems (GIS) and spatial statistical analysis has profoundly transformed health planning in general in that it enables the strong spatial and temporal characterization of hotspots (Hay et al., 2009 ). Such studies conducted in Nigeria have illustrated the above. For example, studies conducted in the state of Lagos have identified slums as susceptible locations due to factors like the lack of proper drainage systems and under-optimized living structures (which can be verified when looking at general findings related to the quality of living structures in the general study conducted in Tusting et al., 2016 ). In the Niger Delta area of Nigeria, the link between the water-borne aspects of malaria infection and the proximity between living locations and the breeding sites of the malarial mosquitoes has been recognized as playing a role in the prevalence of the disease (Efe & Ojoh, 2013 ). However, it is important to note that spatial clustering can differ across levels. While the type of clustering examined in the Bousema et al. ( 2012 ) publication can be observed at the village or household level due to hyper-local patterns of transmission, the type observed at the LGA level suggests macro-level spatial heterogeneity. This type of macro-level spatial heterogeneity can be the result of macro-level factors like rainfall or elevation patterns. This particular analysis will examine the LGA-level type since the aim here is to detect the LGA-level clusters that tend to remain spatially grouped together based on high prevalence. 1.2. Novelty and Objectives Notwithstanding the recognized heavy toll of malaria in Kano State, a notable dearth pervades the existing body of literature when it comes to spatio-temporal analyses of trends related to the prevalence rates in all 44 LGAs across the region. Most studies conducted to date, whether facility-based or community-based cross-sectional studies (for example, Madobi, 2012 ), as useful as the information gathered has been, remain rather limited in their spatial and temporal intensity when considered against the requirements of long-term strategic planning. They represent a point in time but neither portray the spatial patterns nor the long-term risk patterns. This study specifically fills the identified gap by conducting a thorough geospatial analysis of the prevalence of malaria during the vital 20-year period between 2000–2019. This has been made possible through the effective usage of spatial modeling techniques and appropriate statistical analysis to map the existence of spatial autocorrelation in the dataset and the ability to model the progression of the disease at the state level. The specifics objectives for this study are; To evaluate the extent of spatial autocorrelation as well as the prevalence clustering pattern among the 44 LGAs of Kano State during the 20-year period. Identification of hotspots and coldspots for the prevalence of malaria. To evaluate the trend analysis of the total number of cases of malaria in the state. 2. Materials and Methods 2.1. Study area description Kano State is located in the north of Nigeria. Its location ranges between latitudes 10°30'N and 12°37'N and longitudes 7°40'E and 9°30'E (Figure 1 above). Kano State has the highest population in the whole of Nigeria. Its population stands at over 15 million. Majority of the population belong to the Hausa Fulani ethnic group (NPC, 2018). Kano State is further divided into 44 Local Government Areas. These areas are of diverse size and population. For example, Kano Municipal has a high population yet the area covered is very small. Bebeji LGA covers a large area compared to other LGAs due to the fact that its population is smaller. On average, the total population per LGA is 341,000. The variation in the population of the LGAs ranges from below 100,000 in the rural areas to above 1.5 million. The map of Kano is displayed on the figure 1 of the study. The climate in the area can be described as tropical savanna. There is a wet season between the months of May and October and a dry season between the months of November and April. This dry season specifically experiences the dry and dusty Harmattan winds. This region has a rainfall of between 600mm in the north and 1200mm in the south. The land has a median height of 500 meters above sea level. The climate favors the breeding of Anopheles mosquitoes. Thus, the area experiences malaria as a tropical endemic. The economy of the state relies solely on agriculture. Kano City represents the business capital and population center. 2.2. Data Sources and Preparation 2.2.1. Malaria Prevalence Data Data used to represent the prevalence of malaria in the 44 LGAs of Kano State between 2000 and 2019 were obtained from the Malaria Atlas Project (MAP). MAP offers spatially interpolated Plasmodium falciparum parasite rate measures for children aged 2-10 years (PfPR~2-10~), PfPR. This metric is the recognized standard to which the prevalence of malaria can be compared. An important point to note is that the actual case prevalence collected from the field would not be as presented in the PfPR measures but rather as interpolated/modelled measures based on diverse sources of information (household surveys and routine health information), creating standardized maps geostatistically. This information can then be presented at the administration level (LGAs included in this case), and the main advantage of the modelled information approach applied in this long-term trend analysis specific to the prevalence of malaria might involve the standardized comparison made among the information. The information required for the specific analysis was obtained. 2.2.2. Spatial Boundary Data A shape file defining the exact boundaries of the 44 LGAs in Kano State has been sourced from the National Bureau of Statistics in Nigeria to ensure standardization. 2.2.3. Data Integration and Management The PfPR values for each LGA in a given year were presented in a structured CSV file format containing a unique identifier for each LGA, the name of the LGA, and its corresponding PfPR prevalence. This structured tabulated information could then be joined to the LGA boundaries shape file based on their unique identifier in the 10.8 version of the ArcGIS software. This made the LGA the unit of spatial analysis as 44 LGAs were analyzed. 2.3. Methodology The analytical framework was developed to specifically tackle the three identified objectives, utilizing both spatial statistic techniques as well as time series analysis. 2.3.1. Spatial Autocorrelation Analysis (Global Moran's I) Objective Addressed: To evaluate the level of spatial autocorrelation and pattern of clustering (Objective 1). Global Moran's Index: Global Moran's Index is an inferential spatial statistic used to quantify the nature of spatial autocorrelation present in the entire area under study (Moran, 1950). This analysis has been made both for the prevalence of the disease for each specific yearly case and the 20-year average surface. The Moran's I statistic has values that fall between -1 and 1. A positive significant value of the statistic (I > 0) suggests that spatial clusters of like values occur (high values tend to occur together in space, as do low values), whereas a negative significant value (I < 0) indicates dispersion. A value of zero indicates randomness. The statistical significance of the Moran's I statistic can be obtained from the z-score and p-value obtained by a randomization test. In the case of the present analysis, a fixed neighborhood area has been considered based on the definition of spatial proximity among the LGAs. In the output, the Moran's Index statistic and the Expected Index, Z-score, and p-value were obtained for each year. 2.3.2. Hot Spot Analysis (Getis-Ord Gi*) Objective Addressed: Identifying and pinpointing hotspots and coldspots based on statistical significance (Objective 2). While when Global Moran's I revealed the existence of hotspots, it failed to establish the exact spot where the hotspots are found. The local analysis Getis-Ord Gi* map statistic was used to find the exact LGAs where high or low values significantly exist (Getis and Ord, 1992). This map statistic uses the z-score for each LGA to analyze its prevalence value compared to the other neighboring LGAs. High positive z-scores with a insignificant p-value reveal the existence of "hotspots"; this refers to the LGA containing a high prevalence value among other neighboring LGAs that have the same high values. High negative z-scores reveal the existence of "coldspots." The results were based on the 20-year average prevalence. For the analysis to exhibit long-term patterns, the 20-year average prevalence was used. The output measures the confidence levels of the LGAs. The confidence levels are 99% confidence hot/cold spot, 95% confidence hot/cold spot, 90% confidence hot/cold spot, and not significant. This output has been used to create a map. 2.3.3. Temporal Trend Analysis (ARIMA Modeling) Objective Addressed: To analyze the temporal trends in state-wide malaria cases (Objective 3). In order to gain insights into the dynamics of the prevalence of malaria at the state level, the ARIMA model was used to analyze the time series of the mean PfPR per annum among the 44 LGAs in Kano State. ARIMA refers to a class of statistical models that can analyze and predict time series processes. ARIMA can be described based on three parameters: p: p represents the autoregressive term. This refers to the number of observations considered for inclusion in the AR model. d (Differencing term): This indicates the number of times the original data has been differenced to achieve stationarity. q (term of the Moving Average): This specifies the number of lagged forecast errors. The modeling procedure included the following steps: Stationarity Test: The Augmented Dickey-Fuller test has been employed to identify whether the time series follows stationarity or not. As the original time series proved to be non-stationary, the first difference (d=1) has been considered. Model Identification: The ACF and PACF plots of the differenced series were used to identify the possible values of p and q. Parameter Selection and Model Fitting: Different ARIMA models (for instance, ARIMA(1,1,1), ARIMA(0,1,1), and ARIMA(1,1,0)) were used to fit the dataset. The best ARIMA model has been chosen based on the lowest Model Diagnostics: The appropriateness of the chosen model was validated. The Ljung-Box test confirmed the lack of autocorrelation among the residuals (indicating that a good model should not have significant autocorrelation among the residuals). The Jarque-Bera test confirmed the normal distribution of the residuals. Finally, the homoscedasticity of the residuals (which should not have any significant variance) can be validated using the ARCH-LM Test. All spatial analyses were performed in ‘ArcGIS Pro 3.0’, and the time series analysis was carried out in the ‘statsmodels’ library of ‘Python’. 3. Results 3.1. Global Spatial Autocorrelation The Global Moran's I test identified the existence of a pattern of spatial clustering for the entire 20-year period. As shown in Table 1 above, all the Moran's I values were positive and significant at p < 0.01. The Moran's I values were highest at 0.253 in 2009 and lowest at 0.103 in 2016. This shows that the values were moderate and that the pattern identified above as spatial clustering of malaria prevalence does exist in Kano State. Moreover, the analysis of the 20-year average prevalence confirmed this pattern. The Moran's I identified in this analysis has a value of 0.178 and p < 0.001. This shows that the pattern of spatial clustering identified above has been the norm in the malaria prevalence patterns of Kano State over the 20-year period. Table 1: Global Moran's I Results for Selected Years (2000-2019) Years Moran’s Index Z-score P value Pattern 2000 0.226429 6.65756 0 Clustered 2001 0.232762 6.8217 0 Clustered 2002 0.213877 6.32653 0 Clustered 2003 0.198549 5.920203 0 Clustered 2004 0.168417 5.12154 0 Clustered 2005 0.14402 4.47442 0 Clustered 2006 0.161279 4.929125 0 Clustered 2007 0.200529 5.964144 0 Clustered 2008 0.233814 6.838117 0 Clustered 2009 0.253051 7.340362 0 Clustered 2010 0.241074 7.011008 0 Clustered 2011 0.213741 6.275826 0 Clustered 2012 0.206033 6.068616 0 Clustered 2013 0.227 6.638075 0 Clustered 2014 0.222201 6.503995 0 Clustered 2015 0.160822 4.882388 0 Clustered 2016 0.103345 3.380196 0 Clustered 2017 0.20887 6.184849 0 Clustered 2018 0.150818 4.650365 0 Clustered 2019 0.150821 4.650431 0 Clustered 2000 - 2019 0.110993 3.587276 0.000334 Clustered Source: Author’s computation (2024) 3.2. Identification of Hotspots and Coldspots The hot spot analysis conducted using the Gi* statistic on the 20-year average prevalence rates revealed detailed spatial information about the locations of hotspots and coldspots in the specific LGAs (Figure 2). The output revealed the following distinct geographic patterns: Hotspots: The identified hotspots were mainly found in the north western and central parts of Kano State at the 95% and 99% confidence levels. The important LGAs that were identified as hotspots included Bichi, Tsanyawa, Kunchi, and Dawakin Tofa. This indicates that the area has a high level of transmission. Coldspots: Most of the important coldspots were identified in the southern and eastern parts of the state. The Local Government Areas that fall under the category of coldspots include Doguwa, Gaya, and Sumaila. It is important to note that among the hotspots identified are locations such as Bichi and Tsanyawa, which are positioned at the international border of the state and the Niger Republic. 3.3. Temporal Trends and ARIMA Modeling The trend of the state average PfPR over the period 2000-2019 is shown in Figure 3. While the series shows fluctuations and no linear rise or fall over the two decades' time span, factors like seasonality and other long-term cycles seem to play a role. After ascertaining the requirement for first differencing to ensure stationarity (ADF test p-value < 0.05), the ARIMA(1,1,1) model proved to have the best fit based upon the smallest AIC. The results of the ARIMA(1,1,1) model are shown in Table 2. AR(1) and MA(1) were both significantly positive (p-value < 0.01). This indicates that AR(1)= 0.484 represents positive persistence in that a high prevalence in one year tends to predict a high prevalence in the following year. The significance of MA(1)= 0.889 indicates that the model's prediction also depends upon the random event of the previous year. Table 2: Estimates of the Parameters of the ARIMA(1,1,1) Parameter Coefficient Std. Error Z-Satistic P-Value ar.L1 0.4844 0.163 2.968 0.003 ma.L1 0.8894 0.218 4.073 0.000 sigma2 1.9828 0.504 3.934 0.000 Source: Author’s computation (2024) Diagnostic tests conducted on the model residuals confirmed that the model performed adequately. The Ljung-Box test performed on the residuals gave a statistic of 10.96 (p= 0.33), indicating that the residuals were free from any significant correlation. The Jarque-Bera test statistic stood at 3.44 (p-value= 0.18), whose failure to reject the null-hypothesis of the test confirms that the model has normally distributed errors. The ARCH-LM statistic revealed the significance of the heteroskedasticity test (H-statistic= 23.0; p< 0.01), indicating that the time series follows a pattern where the volatility tends to increase. 4. Discussion This 20-year spatial analysis provides strong evidence for the continuing and spatially structured pattern of malaria transmission in Kano State, Nigeria. The results form a rich source of information for the development of effective health policy and reemphasize the vital role of spatial information in the control of the disease. The patterns of positive Global Moran's I indices at the confidence levels shown across the entire period of the analysis (Meeting Objective 1) clearly reveal the lack of randomness in the distribution of malaria prevalence in Kano State and the dominant cluster patterns. This fact has been acknowledged in the general understanding of the non-random distribution of the disease across the whole of sub-Saharan Africa and has been documented in several studies in the region (Carter et al., 2018 ; Gething et al., 2011 ). The ability to identify specific hotspots in the northwestern and central LGAs (to meet Objective 2) goes beyond determining the existence of hotspots and focuses instead on the exact geographic locations where the dominant infection has been recorded. The fact that the hotspots have been identified over the last two decades indicates the influence of certain "drivers" in their vicinity. The Sudan Savanna that the northwestern area represents and the exact rainfall patterns as well as soil type that could form breeding sites could influence hotspots in the area (Jackson &Yang, 2010 ). Moreover, the fact that the main hotspots, like the Bichi area, are very close to the country's border indicates that additional studies should focus on the role of human mobility across the border as a factor that could obstruct the success of programs that operate strictly between the country's territorial boundaries. The strong autoregressive pattern in the ARIMA(1,1,1) model (Objective 3) indicates a very important temporal pattern: the prevalence of malaria in Kano State has strong memory. High prevalence causes high prevalence in the following year. This clearly indicates the existence of a cycling pattern of transmission that cannot be easily interrupted. This pattern of easy continuation together with the spatial pattern already identified shows the important need to focus the programs over multiple years in the geographic locations of highest risk. The identified heteroskedasticity pattern in the time series could represent the effect of scaled-up programs (mass distribution campaigns of ITNs), unusual climate phenomena such as droughts and rainfall patterns (Akinbobola & Oluleye, 2010 ), or outbreak patterns. This scenario aligns with the “hotspot” targeting strategy recommended by the ideology of Bousema et al. ( 2012 ). This strategy suggests that emphasizing the distribution of insecticide-treated nets (ITNs), Indoor Residing Spraying (IRS), and finding cases in the identified hotspots in the northwest and central areas of the state could lead to more effective reduction in the state's malarial rates than their state-wide implementation. In fact, the fact that the hotspots are geographically contiguous indicates that the best strategy might be to attack the hotspots at a regional level rather than at the LGA level. 4.1 Limitations The present analysis has several limitations that must be mentioned. Firstly, the analysis has been based on the model prevalence available from MAP. While the fact that the analysis has been based on model prevalence is a positive factor when long-term trends and standardization are considered, the fact that the analysis has not been based on actual prevalence could mean that the present analysis has not been able to identify the latest hyper-local information that the actual database could have provided. Secondly, the unit of observation used in the present analysis has been LGA. While the LGA constitutes a macro-level unit that can identify general patterns and trends related to the topic at hand, the analysis has been based on a macro-level unit and the present analysis has been conducted at the macro-level. As such, the clusters identified among the different units of observation might have obscured the details of the actual field-level information. Thirdly, the present analysis has not included information related to the coverage of interventions such as ITN usage or IRS coverage as well as information related to the environment that would have allowed the analysis to identify the latest details available. As such, a specific reason related to the present context cannot clearly be identified as the reason why the clusters are observed. 5. Conclusion and Recommendations 5.1 Conclusion This retrospective spatial-temporal analysis provides a credible and empirical description of the distribution of malaria in Kano State over the last two decades. This analysis shows the spatial and temporal dependency of malaria cases and, more importantly, the hotspots of continuous malaria transmission in the northwestern and central areas of the state. This information provides strong arguments for the realization of a paradigm shift in the strategy for the control of malaria in the state from the blanket strategy to the precision strategy. 5.2Recommendations From the findings of the study the following recommendation were made: Programmatic Action : The Kano State Malaria Elimination Program should concentrate on the identified hot spot LGAs for intensified control efforts. This should include maximizing coverage in vector control measures, improving diagnostic and treatment services, as well as implementing active surveillance. For instance, the hot spot LGAs can be Bichi, Tsanyawa, and Kunchi. Integrated Research : Future studies should incorporate the spatial patterns found in this study with additional information related to coverage rates for interventions, as well as factors like socio-economics and environment. Such information would allow the creation of risk prediction models to understand the underlying causes of the hotspots. Cross-Border Cooperation : Public health authorities must begin communication with their counterparts in neighboring states and the Republic of Niger to explore the possibility of cross border infection, in the north western border area. Through the adoption of this targeted and data-driven strategy, resources can be optimized and the effectiveness of interventions improved in order to meet the final objective of malaria elimination in Kano State. Declarations Funding “The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.” Competing Interests “The authors have no relevant financial or non-financial interests to disclose.” Author Contributions “The author contributed to the study conception and design. All material preparation, data collection and analysis were performed by David Mkpanam Nyong . Ethics approval : “ Not applicable”. Consent to participate : “ Not applicable”. Consent for publication : “ Not applicable”. Data Availability Statement All data used in this study are publicly available from the sources cited below. The processed datasets and analysis scripts generated during the current study are available from the corresponding author upon reasonable request. The malaria prevalence data ( Plasmodium falciparum Parasite Rate for ages 2-10) for the years 2000-2019 were sourced from the Malaria Atlas Project (MAP) and can be accessed via their online repository: https://malariaatlas.org/. The administrative boundary shapefiles for the Local Government Areas of Kano State, Nigeria, were obtained from the Nigerian National Bureau of Statistics and are available through their geoportal or upon request. References Akinbobola, A., & Oluleye, A. (2010). Malaria and pneumonia occurrence in Lagos, Nigeria: Role of temperature and rainfall. 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L., Guerra, C. A., Elyazar, I. R., Johnston, G. L., ... & Hay, S. I. (2011). A new world malaria map: Plasmodium falciparum endemicity in 2010. Malaria Journal , 10(1), 378. Hay, S. I., Snow, R. W., & Rogers, D. J. (2009). From predicting mosquito habitat to malaria seasons using remotely sensed data: Practice, problems and perspectives. Parasitology Today , 14(8), 306–313. Jackson, A. K., & Yang, G. (2010). Effects of microclimatic changes caused by land use and land cover on duration of gonotrophic cycles of Anopheles gambiae (Diptera: Culicidae) in western Kenya highlands. Journal of Medical Entomology , 42(6), 974–980. Madobi, R. (2012). Prevalence and risk factors of malaria in Kano Metropolis, Nigeria. Journal of Public Health and Epidemiology , 4(4), 86-92. Moran, P. A. P. (1950). Notes on continuous stochastic phenomena. Biometrika , 37(1/2), 17–23. National Population Commission (NPC) [Nigeria]. (2018). Demographic statistics bulletin . Abuja, Nigeria: National Population Commission. Oyewole, I. O., Ibidapo, C. A., Okwa, O. O., Oduola, A. O., Adeoye, G. O., Okoh, H. I., & Awolola, T. S. (2010). Species composition and role of Anopheles mosquitoes in malaria transmission along Badagry axis of Lagos Lagoon, Lagos, Nigeria. International Journal of Insect Science , 2, 1–7. Tusting, L. S., Bottomley, C., Gibson, H., Kleinschmidt, I., Tatem, A. J., Lindsay, S. W., & Gething, P. W. (2016). Housing improvements and malaria risk in sub-Saharan Africa: A multi-country analysis of survey data. PLoS Medicine , 13(2), e1002234. World Health Organization (WHO). (2023). World malaria report 2023 . Geneva, Switzerland: World Health Organization. Additional Declarations No competing interests reported. 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10:27:50","extension":"html","order_by":10,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":71709,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8010446/v1/1e87edb76b7b0ed946571bf8.html"},{"id":95225913,"identity":"a108405d-1dcd-4521-b03d-685b6eab1ab6","added_by":"auto","created_at":"2025-11-05 16:25:46","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":186893,"visible":true,"origin":"","legend":"\u003cp\u003eMap of Kano State showing the 44 Local Government Areas\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8010446/v1/839b8660396f61503181bebc.png"},{"id":95104289,"identity":"ea0b8828-44ea-46ef-aee9-fb85192397c9","added_by":"auto","created_at":"2025-11-04 10:27:50","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":276683,"visible":true,"origin":"","legend":"\u003cp\u003eHot Spot Analysis Map of Malaria Prevalence in Kano State (2000-2019 Average). The map shows LGAs classified by Gi* Bin: Coldspots (blue), Not Significant (yellow), Hotspots (red), with intensity indicating confidence level.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8010446/v1/fdd7cdecda221ed58c58e400.png"},{"id":95104286,"identity":"72105c25-2908-4447-b860-af32a135284f","added_by":"auto","created_at":"2025-11-04 10:27:49","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":57910,"visible":true,"origin":"","legend":"\u003cp\u003eTime Series of Annual Mean Malaria Prevalence (PfPR) in Kano State (2000-2019). The y-axis is labeled \"Mean PfPR (%)\".\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8010446/v1/fee8e807cf2ff49234947eb6.png"},{"id":95230385,"identity":"23148a5a-0c14-4ff1-9966-113389dbcc8d","added_by":"auto","created_at":"2025-11-05 16:37:22","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1254957,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8010446/v1/bde1f461-3597-48d3-b09c-09d0e20a449d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Retrospective Spatio-Temporal Analysis of Malaria Prevalence in Kano State, Nigeria: A 20- Year Geospatial Assessment","fulltext":[{"header":"1. Introduction","content":"\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e\u003ch2\u003e1.1. Background and Literature Review\u003c/h2\u003e\u003cp\u003eMalaria has continued to represent a serious health concern across the world, with the worst effects being recorded in sub-Saharan Africa. According to the 2023 report published by the World Health Organization (WHO), \u0026ldquo;This region accounted for 94% of all malaria cases and 95% of all malaria deaths in 2023. Nigeria contributed 27% of the total burden of the disease\u0026rdquo; (WHO, 2023).\u003c/p\u003e\u003cp\u003eThe complexity of the vector-biasing environment that underpins the dynamics of malaria infection has been irrevocably entwined in a set of factors that are both environmental and socio-economic in nature. Such factors as temperature and rainfall patterns significantly influence the role of the Anopheles vector in the spread of the disease (Gething et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2011\u003c/span\u003e).On the other hand, other factors such as humidity levels, accessibility to water sources, the nature of housing structures, as well as the availability of health services, among other factors play a very important role in determining the geography of risk presented by the disease across the world (Akinbobola \u0026amp; Oluleye, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Tusting et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eOne of the most important steps forward in the field of malaria epidemiology has been the realization of the strongly sub-national heterogeneity of the disease. National or state-level prevalence rates tend to conceal strong localized pockets of infection, or \u0026ldquo;hotspots,\u0026rdquo; in which the level of infection tends to remain elevated. As described clearly by Bousema et al. in 2012, these hotspots form a \u0026ldquo;reserve\u0026rdquo; fueling the infection of neighboring zones. This micro-epidemiology represents a fundamental priority for the most effective allocation of publicly limited health resources. The coupling of Geographic Information Systems (GIS) and spatial statistical analysis has profoundly transformed health planning in general in that it enables the strong spatial and temporal characterization of hotspots (Hay et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2009\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eSuch studies conducted in Nigeria have illustrated the above. For example, studies conducted in the state of Lagos have identified slums as susceptible locations due to factors like the lack of proper drainage systems and under-optimized living structures (which can be verified when looking at general findings related to the quality of living structures in the general study conducted in Tusting et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). In the Niger Delta area of Nigeria, the link between the water-borne aspects of malaria infection and the proximity between living locations and the breeding sites of the malarial mosquitoes has been recognized as playing a role in the prevalence of the disease (Efe \u0026amp; Ojoh, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eHowever, it is important to note that spatial clustering can differ across levels. While the type of clustering examined in the Bousema et al. (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) publication can be observed at the village or household level due to hyper-local patterns of transmission, the type observed at the LGA level suggests macro-level spatial heterogeneity. This type of macro-level spatial heterogeneity can be the result of macro-level factors like rainfall or elevation patterns. This particular analysis will examine the LGA-level type since the aim here is to detect the LGA-level clusters that tend to remain spatially grouped together based on high prevalence.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e1.2. Novelty and Objectives\u003c/h2\u003e\u003cp\u003eNotwithstanding the recognized heavy toll of malaria in Kano State, a notable dearth pervades the existing body of literature when it comes to spatio-temporal analyses of trends related to the prevalence rates in all 44 LGAs across the region. Most studies conducted to date, whether facility-based or community-based cross-sectional studies (for example, Madobi, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), as useful as the information gathered has been, remain rather limited in their spatial and temporal intensity when considered against the requirements of long-term strategic planning. They represent a point in time but neither portray the spatial patterns nor the long-term risk patterns.\u003c/p\u003e\u003cp\u003eThis study specifically fills the identified gap by conducting a thorough geospatial analysis of the prevalence of malaria during the vital 20-year period between 2000\u0026ndash;2019. This has been made possible through the effective usage of spatial modeling techniques and appropriate statistical analysis to map the existence of spatial autocorrelation in the dataset and the ability to model the progression of the disease at the state level. The specifics objectives for this study are;\u003c/p\u003e\u003cp\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eTo evaluate the extent of spatial autocorrelation as well as the prevalence clustering pattern among the 44 LGAs of Kano State during the 20-year period.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eIdentification of hotspots and coldspots for the prevalence of malaria.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eTo evaluate the trend analysis of the total number of cases of malaria in the state.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"2. Materials and Methods","content":"\u003cp\u003e\u003cstrong\u003e2.1. Study area description\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eKano State is located in the north of Nigeria. Its location ranges between latitudes 10\u0026deg;30\u0026apos;N and 12\u0026deg;37\u0026apos;N and longitudes 7\u0026deg;40\u0026apos;E and 9\u0026deg;30\u0026apos;E (Figure 1 above). Kano State has the highest population in the whole of Nigeria. Its population stands at over 15 million. Majority of the population belong to the Hausa Fulani ethnic group (NPC, 2018). Kano State is further divided into 44 Local Government Areas. These areas are of diverse size and population. For example, Kano Municipal has a high population yet the area covered is very small. Bebeji LGA covers a large area compared to other LGAs due to the fact that its population is smaller. On average, the total population per LGA is 341,000. The variation in the population of the LGAs ranges from below 100,000 in the rural areas to above 1.5 million. The map of Kano is displayed on the figure 1 of the study.\u003c/p\u003e\n\u003cp\u003eThe climate in the area can be described as tropical savanna. There is a wet season between the months of May and October and a dry season between the months of November and April. This dry season specifically experiences the dry and dusty Harmattan winds. This region has a rainfall of between 600mm in the north and 1200mm in the south. The land has a median height of 500 meters above sea level. The climate favors the breeding of Anopheles mosquitoes. Thus, the area experiences malaria as a tropical endemic. The economy of the state relies solely on agriculture. Kano City represents the business capital and population center.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2. Data Sources and Preparation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2.1.\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eMalaria Prevalence Data\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData used to represent the prevalence of malaria in the 44 LGAs of Kano State between 2000 and 2019 were obtained from the Malaria Atlas Project (MAP). MAP offers spatially interpolated Plasmodium falciparum parasite rate measures for children aged 2-10 years (PfPR~2-10~), PfPR. This metric is the recognized standard to which the prevalence of malaria can be compared. An important point to note is that the actual case prevalence collected from the field would not be as presented in the PfPR measures but rather as interpolated/modelled measures based on diverse sources of information (household surveys and routine health information), creating standardized maps geostatistically. This information can then be presented at the administration level (LGAs included in this case), and the main advantage of the modelled information approach applied in this long-term trend analysis specific to the prevalence of malaria might involve the standardized comparison made among the information. The information required for the specific analysis was obtained.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2.2.\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eSpatial Boundary Data\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA shape file defining the exact boundaries of the 44 LGAs in Kano State has been sourced from the National Bureau of Statistics in Nigeria to ensure standardization.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2.3. Data Integration and Management\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe PfPR values for each LGA in a given year were presented in a structured CSV file format containing a unique identifier for each LGA, the name of the LGA, and its corresponding PfPR prevalence. This structured tabulated information could then be joined to the LGA boundaries shape file based on their unique identifier in the 10.8 version of the ArcGIS software. This made the LGA the unit of spatial analysis as 44 LGAs were analyzed.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3. Methodology\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe analytical framework was developed to specifically tackle the three identified objectives, utilizing both spatial statistic techniques as well as time series analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3.1. Spatial Autocorrelation Analysis (Global Moran\u0026apos;s I)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eObjective Addressed:\u003c/strong\u003e To evaluate the level of spatial autocorrelation and pattern of clustering (Objective 1).\u003c/p\u003e\n\u003cp\u003eGlobal Moran\u0026apos;s Index:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp;Global Moran\u0026apos;s Index is an inferential spatial statistic used to quantify the nature of spatial autocorrelation present in the entire area under study (Moran, 1950). This analysis has been made both for the prevalence of the disease for each specific yearly case and the 20-year average surface.\u003c/p\u003e\n\u003cp\u003eThe Moran\u0026apos;s I statistic has values that fall between -1 and 1. A positive significant value of the statistic (I \u0026gt; 0) suggests that spatial clusters of like values occur (high values tend to occur together in space, as do low values), whereas a negative significant value (I \u0026lt; 0) indicates dispersion. A value of zero indicates randomness. The statistical significance of the Moran\u0026apos;s I statistic can be obtained from the z-score and p-value obtained by a randomization test. In the case of the present analysis, a fixed neighborhood area has been considered based on the definition of spatial proximity among the LGAs. In the output, the Moran\u0026apos;s Index statistic and the Expected Index, Z-score, and p-value were obtained for each year.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3.2. Hot Spot Analysis (Getis-Ord Gi*)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eObjective Addressed:\u003c/strong\u003e Identifying and pinpointing hotspots and coldspots based on statistical significance (Objective 2).\u003c/p\u003e\n\u003cp\u003eWhile when Global Moran\u0026apos;s I revealed the existence of hotspots, it failed to establish the exact spot where the hotspots are found. The local analysis Getis-Ord Gi* map statistic was used to find the exact LGAs where high or low values significantly exist (Getis and Ord, 1992). This map statistic uses the z-score for each LGA to analyze its prevalence value compared to the other neighboring LGAs. High positive z-scores with a insignificant p-value reveal the existence of \u0026quot;hotspots\u0026quot;; this refers to the LGA containing a high prevalence value among other neighboring LGAs that have the same high values. High negative z-scores reveal the existence of \u0026quot;coldspots.\u0026quot;\u003c/p\u003e\n\u003cp\u003eThe results were based on the 20-year average prevalence. For the analysis to exhibit long-term patterns, the 20-year average prevalence was used. The output measures the confidence levels of the LGAs. The confidence levels are 99% confidence hot/cold spot, 95% confidence hot/cold spot, 90% confidence hot/cold spot, and not significant. This output has been used to create a map.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3.3. Temporal Trend Analysis (ARIMA Modeling)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eObjective Addressed:\u003c/strong\u003e To analyze the temporal trends in state-wide malaria cases (Objective 3).\u003c/p\u003e\n\u003cp\u003eIn order to gain insights into the dynamics of the prevalence of malaria at the state level, the ARIMA model was used to analyze the time series of the mean PfPR per annum among the 44 LGAs in Kano State. ARIMA refers to a class of statistical models that can analyze and predict time series processes. ARIMA can be described based on three parameters:\u003c/p\u003e\n\u003col style=\"list-style-type: lower-alpha;\"\u003e\n \u003cli\u003ep: p represents the autoregressive term. This refers to the number of observations considered for inclusion in the AR model.\u003c/li\u003e\n \u003cli\u003ed (Differencing term): This indicates the number of times the original data has been differenced to achieve stationarity.\u003c/li\u003e\n \u003cli\u003eq (term of the Moving Average): This specifies the number of lagged forecast errors.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eThe modeling procedure included the following steps:\u003c/p\u003e\n\u003col class=\"decimal_type\"\u003e\n \u003cli\u003eStationarity Test: The Augmented Dickey-Fuller test has been employed to identify whether the time series follows stationarity or not. As the original time series proved to be non-stationary, the first difference (d=1) has been considered.\u003c/li\u003e\n \u003cli\u003eModel Identification: The ACF and PACF plots of the differenced series were used to identify the possible values of p and q.\u003c/li\u003e\n \u003cli\u003eParameter Selection and Model Fitting: Different ARIMA models (for instance, ARIMA(1,1,1), ARIMA(0,1,1), and ARIMA(1,1,0)) were used to fit the dataset. The best ARIMA model has been chosen based on the lowest\u003c/li\u003e\n \u003cli\u003eModel Diagnostics: The appropriateness of the chosen model was validated. The Ljung-Box test confirmed the lack of autocorrelation among the residuals (indicating that a good model should not have significant autocorrelation among the residuals). The Jarque-Bera test confirmed the normal distribution of the residuals. Finally, the homoscedasticity of the residuals (which should not have any significant variance) can be validated using the ARCH-LM Test.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eAll spatial analyses were performed in \u0026lsquo;ArcGIS Pro 3.0\u0026rsquo;, and the time series analysis was carried out in the \u0026lsquo;statsmodels\u0026rsquo; library of \u0026lsquo;Python\u0026rsquo;.\u003c/p\u003e"},{"header":"3. Results","content":"\u003cp\u003e\u003cstrong\u003e3.1. Global Spatial Autocorrelation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Global Moran\u0026apos;s I test identified the existence of a pattern of spatial clustering for the entire 20-year period. As shown in Table 1 above, all the Moran\u0026apos;s I values were positive and significant at p \u0026lt; 0.01. The Moran\u0026apos;s I values were highest at 0.253 in 2009 and lowest at 0.103 in 2016. This shows that the values were moderate and that the pattern identified above as spatial clustering of malaria prevalence does exist in Kano State. Moreover, the analysis of the 20-year average prevalence confirmed this pattern. The Moran\u0026apos;s I identified in this analysis has a value of 0.178 and p \u0026lt; 0.001. This shows that the pattern of spatial clustering identified above has been the norm in the malaria prevalence patterns of Kano State over the 20-year period.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1: Global Moran\u0026apos;s I Results for Selected Years (2000-2019)\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 14.2173%;\"\u003e\n \u003cp\u003eYears \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.2748%;\"\u003e\n \u003cp\u003eMoran\u0026rsquo;s Index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.8786%;\"\u003e\n \u003cp\u003eZ-score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8211%;\"\u003e\n \u003cp\u003eP value\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.8083%;\"\u003e\n \u003cp\u003ePattern\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 14.2173%;\"\u003e\n \u003cp\u003e2000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.2748%;\"\u003e\n \u003cp\u003e0.226429\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.8786%;\"\u003e\n \u003cp\u003e6.65756\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8211%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.8083%;\"\u003e\n \u003cp\u003eClustered\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 14.2173%;\"\u003e\n \u003cp\u003e2001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.2748%;\"\u003e\n \u003cp\u003e0.232762\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.8786%;\"\u003e\n \u003cp\u003e6.8217\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8211%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.8083%;\"\u003e\n \u003cp\u003eClustered\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 14.2173%;\"\u003e\n \u003cp\u003e2002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.2748%;\"\u003e\n \u003cp\u003e0.213877\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.8786%;\"\u003e\n \u003cp\u003e6.32653\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8211%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.8083%;\"\u003e\n \u003cp\u003eClustered\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 14.2173%;\"\u003e\n \u003cp\u003e2003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.2748%;\"\u003e\n \u003cp\u003e0.198549\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.8786%;\"\u003e\n \u003cp\u003e5.920203\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8211%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.8083%;\"\u003e\n \u003cp\u003eClustered\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 14.2173%;\"\u003e\n \u003cp\u003e2004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.2748%;\"\u003e\n \u003cp\u003e0.168417\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.8786%;\"\u003e\n \u003cp\u003e5.12154\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8211%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.8083%;\"\u003e\n \u003cp\u003eClustered\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 14.2173%;\"\u003e\n \u003cp\u003e2005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.2748%;\"\u003e\n \u003cp\u003e0.14402\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.8786%;\"\u003e\n \u003cp\u003e4.47442\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8211%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.8083%;\"\u003e\n \u003cp\u003eClustered\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 14.2173%;\"\u003e\n \u003cp\u003e2006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.2748%;\"\u003e\n \u003cp\u003e0.161279\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.8786%;\"\u003e\n \u003cp\u003e4.929125\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8211%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.8083%;\"\u003e\n \u003cp\u003eClustered\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 14.2173%;\"\u003e\n \u003cp\u003e2007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.2748%;\"\u003e\n \u003cp\u003e0.200529\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.8786%;\"\u003e\n \u003cp\u003e5.964144\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8211%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.8083%;\"\u003e\n \u003cp\u003eClustered\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 14.2173%;\"\u003e\n \u003cp\u003e2008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.2748%;\"\u003e\n \u003cp\u003e0.233814\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.8786%;\"\u003e\n \u003cp\u003e6.838117\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8211%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.8083%;\"\u003e\n \u003cp\u003eClustered\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 14.2173%;\"\u003e\n \u003cp\u003e2009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.2748%;\"\u003e\n \u003cp\u003e0.253051\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.8786%;\"\u003e\n \u003cp\u003e7.340362\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8211%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.8083%;\"\u003e\n \u003cp\u003eClustered\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 14.2173%;\"\u003e\n \u003cp\u003e2010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.2748%;\"\u003e\n \u003cp\u003e0.241074\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.8786%;\"\u003e\n \u003cp\u003e7.011008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8211%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.8083%;\"\u003e\n \u003cp\u003eClustered\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 14.2173%;\"\u003e\n \u003cp\u003e2011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.2748%;\"\u003e\n \u003cp\u003e0.213741\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.8786%;\"\u003e\n \u003cp\u003e6.275826\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8211%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.8083%;\"\u003e\n \u003cp\u003eClustered\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 14.2173%;\"\u003e\n \u003cp\u003e2012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.2748%;\"\u003e\n \u003cp\u003e0.206033\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.8786%;\"\u003e\n \u003cp\u003e6.068616\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8211%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.8083%;\"\u003e\n \u003cp\u003eClustered\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 14.2173%;\"\u003e\n \u003cp\u003e2013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.2748%;\"\u003e\n \u003cp\u003e0.227\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.8786%;\"\u003e\n \u003cp\u003e6.638075\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8211%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.8083%;\"\u003e\n \u003cp\u003eClustered\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 14.2173%;\"\u003e\n \u003cp\u003e2014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.2748%;\"\u003e\n \u003cp\u003e0.222201\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.8786%;\"\u003e\n \u003cp\u003e6.503995\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8211%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.8083%;\"\u003e\n \u003cp\u003eClustered\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 14.2173%;\"\u003e\n \u003cp\u003e2015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.2748%;\"\u003e\n \u003cp\u003e0.160822\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.8786%;\"\u003e\n \u003cp\u003e4.882388\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8211%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.8083%;\"\u003e\n \u003cp\u003eClustered\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 14.2173%;\"\u003e\n \u003cp\u003e2016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.2748%;\"\u003e\n \u003cp\u003e0.103345\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.8786%;\"\u003e\n \u003cp\u003e3.380196\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8211%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.8083%;\"\u003e\n \u003cp\u003eClustered\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 14.2173%;\"\u003e\n \u003cp\u003e2017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.2748%;\"\u003e\n \u003cp\u003e0.20887\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.8786%;\"\u003e\n \u003cp\u003e6.184849\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8211%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.8083%;\"\u003e\n \u003cp\u003eClustered\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 14.2173%;\"\u003e\n \u003cp\u003e2018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.2748%;\"\u003e\n \u003cp\u003e0.150818\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.8786%;\"\u003e\n \u003cp\u003e4.650365\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8211%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.8083%;\"\u003e\n \u003cp\u003eClustered\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 14.2173%;\"\u003e\n \u003cp\u003e2019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.2748%;\"\u003e\n \u003cp\u003e0.150821\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.8786%;\"\u003e\n \u003cp\u003e4.650431\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8211%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.8083%;\"\u003e\n \u003cp\u003eClustered\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 14.2173%;\"\u003e\n \u003cp\u003e2000 - 2019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.2748%;\"\u003e\n \u003cp\u003e0.110993\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.8786%;\"\u003e\n \u003cp\u003e3.587276\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8211%;\"\u003e\n \u003cp\u003e0.000334\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.8083%;\"\u003e\n \u003cp\u003eClustered\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eSource: Author\u0026rsquo;s computation (2024)\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2. Identification of Hotspots and Coldspots\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe hot spot analysis conducted using the Gi* statistic on the 20-year average prevalence rates revealed detailed spatial information about the locations of hotspots and coldspots in the specific LGAs (Figure 2).\u003c/p\u003e\n\u003cp\u003eThe output revealed the following distinct geographic patterns:\u003c/p\u003e\n\u003col style=\"list-style-type: upper-roman;\"\u003e\n \u003cli\u003eHotspots: The identified hotspots were mainly found in the north western and central parts of Kano State at the 95% and 99% confidence levels. The important LGAs that were identified as hotspots included Bichi, Tsanyawa, Kunchi, and Dawakin Tofa. This indicates that the area has a high level of transmission.\u003c/li\u003e\n \u003cli\u003eColdspots: Most of the important coldspots were identified in the southern and eastern parts of the state. The Local Government Areas that fall under the category of coldspots include Doguwa, Gaya, and Sumaila.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eIt is important to note that among the hotspots identified are locations such as Bichi and Tsanyawa, which are positioned at the international border of the state and the Niger Republic.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3. Temporal Trends and ARIMA Modeling\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe trend of the state average PfPR over the period 2000-2019 is shown in Figure 3. While the series shows fluctuations and no linear rise or fall over the two decades\u0026apos; time span, factors like seasonality and other long-term cycles seem to play a role.\u003c/p\u003e\n\u003cp\u003eAfter ascertaining the requirement for first differencing to ensure stationarity (ADF test p-value \u0026lt; 0.05), the ARIMA(1,1,1) model proved to have the best fit based upon the smallest AIC. The results of the ARIMA(1,1,1) model are shown in Table 2. AR(1) and MA(1) were both significantly positive (p-value \u0026lt; 0.01). This indicates that AR(1)= 0.484 represents positive persistence in that a high prevalence in one year tends to predict a high prevalence in the following year. The significance of MA(1)= 0.889 indicates that the model\u0026apos;s prediction also depends upon the random event of the previous year.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2: Estimates of the Parameters of the ARIMA(1,1,1)\u003c/strong\u003e\u003c/p\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eParameter\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCoefficient\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eStd. Error\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eZ-Satistic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eP-Value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ear.L1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.4844\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.163\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.968\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ema.L1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.8894\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.218\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.073\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003esigma2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.9828\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.504\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.934\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eSource:\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cem\u003eAuthor\u0026rsquo;s computation (2024)\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eDiagnostic tests conducted on the model residuals confirmed that the model performed adequately. The Ljung-Box test performed on the residuals gave a statistic of 10.96 (p= 0.33), indicating that the residuals were free from any significant correlation. The Jarque-Bera test statistic stood at 3.44 (p-value= 0.18), whose failure to reject the null-hypothesis of the test confirms that the model has normally distributed errors. The ARCH-LM statistic revealed the significance of the heteroskedasticity test (H-statistic= 23.0; p\u0026lt; 0.01), indicating that the time series follows a pattern where the volatility tends to increase.\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis 20-year spatial analysis provides strong evidence for the continuing and spatially structured pattern of malaria transmission in Kano State, Nigeria. The results form a rich source of information for the development of effective health policy and reemphasize the vital role of spatial information in the control of the disease.\u003c/p\u003e\u003cp\u003eThe patterns of positive Global Moran's I indices at the confidence levels shown across the entire period of the analysis (Meeting Objective 1) clearly reveal the lack of randomness in the distribution of malaria prevalence in Kano State and the dominant cluster patterns. This fact has been acknowledged in the general understanding of the non-random distribution of the disease across the whole of sub-Saharan Africa and has been documented in several studies in the region (Carter et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Gething et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). The ability to identify specific hotspots in the northwestern and central LGAs (to meet Objective 2) goes beyond determining the existence of hotspots and focuses instead on the exact geographic locations where the dominant infection has been recorded. The fact that the hotspots have been identified over the last two decades indicates the influence of certain \"drivers\" in their vicinity. The Sudan Savanna that the northwestern area represents and the exact rainfall patterns as well as soil type that could form breeding sites could influence hotspots in the area (Jackson \u0026amp;Yang, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Moreover, the fact that the main hotspots, like the Bichi area, are very close to the country's border indicates that additional studies should focus on the role of human mobility across the border as a factor that could obstruct the success of programs that operate strictly between the country's territorial boundaries.\u003c/p\u003e\u003cp\u003eThe strong autoregressive pattern in the ARIMA(1,1,1) model (Objective 3) indicates a very important temporal pattern: the prevalence of malaria in Kano State has strong memory. High prevalence causes high prevalence in the following year. This clearly indicates the existence of a cycling pattern of transmission that cannot be easily interrupted. This pattern of easy continuation together with the spatial pattern already identified shows the important need to focus the programs over multiple years in the geographic locations of highest risk. The identified heteroskedasticity pattern in the time series could represent the effect of scaled-up programs (mass distribution campaigns of ITNs), unusual climate phenomena such as droughts and rainfall patterns (Akinbobola \u0026amp; Oluleye, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), or outbreak patterns.\u003c/p\u003e\u003cp\u003eThis scenario aligns with the \u0026ldquo;hotspot\u0026rdquo; targeting strategy recommended by the ideology of Bousema et al. (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). This strategy suggests that emphasizing the distribution of insecticide-treated nets (ITNs), Indoor Residing Spraying (IRS), and finding cases in the identified hotspots in the northwest and central areas of the state could lead to more effective reduction in the state's malarial rates than their state-wide implementation. In fact, the fact that the hotspots are geographically contiguous indicates that the best strategy might be to attack the hotspots at a regional level rather than at the LGA level.\u003c/p\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003e4.1 Limitations\u003c/h2\u003e\u003cp\u003eThe present analysis has several limitations that must be mentioned. Firstly, the analysis has been based on the model prevalence available from MAP. While the fact that the analysis has been based on model prevalence is a positive factor when long-term trends and standardization are considered, the fact that the analysis has not been based on actual prevalence could mean that the present analysis has not been able to identify the latest hyper-local information that the actual database could have provided. Secondly, the unit of observation used in the present analysis has been LGA. While the LGA constitutes a macro-level unit that can identify general patterns and trends related to the topic at hand, the analysis has been based on a macro-level unit and the present analysis has been conducted at the macro-level. As such, the clusters identified among the different units of observation might have obscured the details of the actual field-level information. Thirdly, the present analysis has not included information related to the coverage of interventions such as ITN usage or IRS coverage as well as information related to the environment that would have allowed the analysis to identify the latest details available. As such, a specific reason related to the present context cannot clearly be identified as the reason why the clusters are observed.\u003c/p\u003e\u003c/div\u003e"},{"header":"5. Conclusion and Recommendations","content":"\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003ch2\u003e5.1 Conclusion\u003c/h2\u003e\u003cp\u003eThis retrospective spatial-temporal analysis provides a credible and empirical description of the distribution of malaria in Kano State over the last two decades. This analysis shows the spatial and temporal dependency of malaria cases and, more importantly, the hotspots of continuous malaria transmission in the northwestern and central areas of the state. This information provides strong arguments for the realization of a paradigm shift in the strategy for the control of malaria in the state from the blanket strategy to the precision strategy.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\u003ch2\u003e5.2Recommendations\u003c/h2\u003e\u003cp\u003eFrom the findings of the study the following recommendation were made:\u003c/p\u003e\u003cp\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eProgrammatic Action\u003c/b\u003e: The Kano State Malaria Elimination Program should concentrate on the identified hot spot LGAs for intensified control efforts. This should include maximizing coverage in vector control measures, improving diagnostic and treatment services, as well as implementing active surveillance. For instance, the hot spot LGAs can be Bichi, Tsanyawa, and Kunchi.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eIntegrated Research\u003c/b\u003e: Future studies should incorporate the spatial patterns found in this study with additional information related to coverage rates for interventions, as well as factors like socio-economics and environment. Such information would allow the creation of risk prediction models to understand the underlying causes of the hotspots.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eCross-Border Cooperation\u003c/b\u003e: Public health authorities must begin communication with their counterparts in neighboring states and the Republic of Niger to explore the possibility of cross border infection, in the north western border area.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e\u003cp\u003eThrough the adoption of this targeted and data-driven strategy, resources can be optimized and the effectiveness of interventions improved in order to meet the final objective of malaria elimination in Kano State.\u003c/p\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u0026ldquo;The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.\u0026rdquo;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u0026ldquo;The authors have no relevant financial or non-financial interests to disclose.\u0026rdquo;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u0026ldquo;The author contributed to the study conception and design. All material preparation, data collection and analysis were performed by\u0026nbsp;\u003c/em\u003e\u003cem\u003eDavid Mkpanam Nyong\u003c/em\u003e\u003cstrong\u003e\u003cem\u003e.\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthics approval\u003cstrong\u003e: \u003cem\u003e\u0026ldquo;\u003c/em\u003e\u003c/strong\u003e\u003cem\u003eNot applicable\u0026rdquo;.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eConsent to participate\u003cstrong\u003e: \u003cem\u003e\u0026ldquo;\u003c/em\u003e\u003c/strong\u003e\u003cem\u003eNot applicable\u0026rdquo;.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eConsent for publication\u003cstrong\u003e: \u003cem\u003e\u0026ldquo;\u003c/em\u003e\u003c/strong\u003e\u003cem\u003eNot applicable\u0026rdquo;.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data used in this study are publicly available from the sources cited below. The processed datasets and analysis scripts generated during the current study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003col start=\"1\" type=\"I\"\u003e\n \u003cli\u003eThe malaria prevalence data (\u003cem\u003ePlasmodium falciparum\u003c/em\u003e Parasite Rate for ages 2-10) for the years 2000-2019 were sourced from the \u003cstrong\u003eMalaria Atlas Project (MAP)\u003c/strong\u003e and can be accessed via their online repository: https://malariaatlas.org/.\u003c/li\u003e\n \u003cli\u003eThe administrative boundary shapefiles for the Local Government Areas of Kano State, Nigeria, were obtained from the \u003cstrong\u003eNigerian National Bureau of Statistics\u003c/strong\u003e and are available through their geoportal or upon request.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAkinbobola, A., \u0026amp; Oluleye, A. (2010). Malaria and pneumonia occurrence in Lagos, Nigeria: Role of temperature and rainfall. \u003cem\u003eAfrican Journal of Environmental Science and Technology\u003c/em\u003e, 4(5), 106-115.\u003c/li\u003e\n\u003cli\u003eBousema, T., Griffin, J. T., Sauerwein, R. W., Smith, D. L., Churcher, T. S., Takken, W., ... \u0026amp; Gosling, R. (2012). Hitting hotspots: Spatial targeting of malaria for control and elimination. \u003cem\u003ePLoS Medicine\u003c/em\u003e, 9(1), e1001165.\u003c/li\u003e\n\u003cli\u003eCarter, R., Mendis, K. N., \u0026amp; Roberts, D. (2018). Spatial targeting of interventions against malaria. \u003cem\u003eBulletin of the World Health Organization\u003c/em\u003e, 78(12), 1401\u0026ndash;1411.\u003c/li\u003e\n\u003cli\u003eEfe, S. I., \u0026amp; Ojoh, C. O. (2013). Climate variation and malaria prevalence in Warri Metropolis. \u003cem\u003eAtmospheric and Climate Sciences\u003c/em\u003e, 3(2), 132-140.\u003c/li\u003e\n\u003cli\u003eGarima, V., Sai Bharath, C. V., Uday, N. Y., Poshan, T., \u0026amp; Miti, J. O. (2013). Environmental factors influencing malaria in manipal, Southern Karnataka, India. \u003cem\u003eJournal of Nursing and Health Science\u003c/em\u003e, 2(3), 8-15.\u003c/li\u003e\n\u003cli\u003eGetis, A., \u0026amp; Ord, J. K. (1992). The analysis of spatial association by use of distance statistics. \u003cem\u003eGeographical Analysis\u003c/em\u003e, 24(3), 189\u0026ndash;206.\u003c/li\u003e\n\u003cli\u003eGething, P. W., Patil, A. P., Smith, D. L., Guerra, C. A., Elyazar, I. R., Johnston, G. L., ... \u0026amp; Hay, S. I. (2011). A new world malaria map: \u003cem\u003ePlasmodium falciparum\u003c/em\u003e endemicity in 2010. \u003cem\u003eMalaria Journal\u003c/em\u003e, 10(1), 378.\u003c/li\u003e\n\u003cli\u003eHay, S. I., Snow, R. W., \u0026amp; Rogers, D. J. (2009). From predicting mosquito habitat to malaria seasons using remotely sensed data: Practice, problems and perspectives. \u003cem\u003eParasitology Today\u003c/em\u003e, 14(8), 306\u0026ndash;313.\u003c/li\u003e\n\u003cli\u003eJackson, A. K., \u0026amp; Yang, G. (2010). Effects of microclimatic changes caused by land use and land cover on duration of gonotrophic cycles of \u003cem\u003eAnopheles gambiae\u003c/em\u003e (Diptera: Culicidae) in western Kenya highlands. \u003cem\u003eJournal of Medical Entomology\u003c/em\u003e, 42(6), 974\u0026ndash;980.\u003c/li\u003e\n\u003cli\u003eMadobi, R. (2012). Prevalence and risk factors of malaria in Kano Metropolis, Nigeria. \u003cem\u003eJournal of Public Health and Epidemiology\u003c/em\u003e, 4(4), 86-92.\u003c/li\u003e\n\u003cli\u003eMoran, P. A. P. (1950). Notes on continuous stochastic phenomena. \u003cem\u003eBiometrika\u003c/em\u003e, 37(1/2), 17\u0026ndash;23.\u003c/li\u003e\n\u003cli\u003eNational Population Commission (NPC) [Nigeria]. (2018). \u003cem\u003eDemographic statistics bulletin\u003c/em\u003e. Abuja, Nigeria: National Population Commission.\u003c/li\u003e\n\u003cli\u003eOyewole, I. O., Ibidapo, C. A., Okwa, O. O., Oduola, A. O., Adeoye, G. O., Okoh, H. I., \u0026amp; Awolola, T. S. (2010). Species composition and role of Anopheles mosquitoes in malaria transmission along Badagry axis of Lagos Lagoon, Lagos, Nigeria. \u003cem\u003eInternational Journal of Insect Science\u003c/em\u003e, 2, 1\u0026ndash;7.\u003c/li\u003e\n\u003cli\u003eTusting, L. S., Bottomley, C., Gibson, H., Kleinschmidt, I., Tatem, A. J., Lindsay, S. W., \u0026amp; Gething, P. W. (2016). Housing improvements and malaria risk in sub-Saharan Africa: A multi-country analysis of survey data. \u003cem\u003ePLoS Medicine\u003c/em\u003e, 13(2), e1002234.\u003c/li\u003e\n\u003cli\u003eWorld Health Organization (WHO). (2023). \u003cem\u003eWorld malaria report 2023\u003c/em\u003e. Geneva, Switzerland: World Health Organization.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Malaria, Spatial Analysis, GIS, Hot Spot Analysis, Kano State, Nigeria, Time Series, ARIMA, Prevalence","lastPublishedDoi":"10.21203/rs.3.rs-8010446/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8010446/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eMalaria remains a profound public health threat in Nigeria,. They contribute significantly to the total world malarial prevalence. In order to carry out the proposed spatio-temporal analysis of the malarial infection incidences in the 44 Local Government Areas (LGAs) of Kano State in Nigeria between 2000\u0026ndash;2019, Geographic Information Systems (GIS), Hot Spot Analysis (Getis-Ord Gi*), Global Moran's Index Analysis, and ARIMA were employed. ARIMA time series modeling helped analyze the intensity of malarial infections. Henceforth, the obtained results clearly reveal positive autocorrelation among the malarial infection patterns across the 44 LGAs in Kano State between 2000\u0026ndash;2019 at the significance level of p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.01. Moreover, the obtained Hot Spot Analysis revealed the intensified malarial infection patterns to exist in the northwestern as well as in the center of the Kano State. From the provided map images in the corresponding figures below, the following were revealed: the melded hot spot indicates the intensified malarial infections in the northwestern and centered Kano State LGAs. In the given map images below, the hot spot represents Doguwa and Gaya LGA as the intensified malarial infection area. On the other hand, hot spot analysis revealed the cold spot malarial infection patterns among the Southern as well as the Eastern LGAs. The malarial infection intensity indices among the 44 LGAs in Kano State between 2000\u0026ndash;2019 were observed to be non stationary. Such patterns can be best described by ARIMA(1,1,1), emphasizing the strong dependency between the pattern intensity at a given time as well as the previous pattern intensities.\u003c/p\u003e","manuscriptTitle":"A Retrospective Spatio-Temporal Analysis of Malaria Prevalence in Kano State, Nigeria: A 20- Year Geospatial Assessment","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-04 10:27:45","doi":"10.21203/rs.3.rs-8010446/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision 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