Data source
Data for this study was obtained from the demographic and health survey (DHS) program website (https://dhsprogram.com) funded by the United States Agency for International Developments (USAIDS) following a written administrative clearance(29). The DHS program has been described elsewhere(27). Households are grouped as either an urban (city block or apartment building) or rural (village or group of villages) cluster points (figure 1) and displaced a distance up to 2 km for urban-city clusters and 5 km for rural clusters due to confidentiality. For this study, points and lattice data for the Cameroon DHS VI malaria survey was obtained from the DHS spatial data repository site(30). The malaria survey data for 2015 was linked with environmental covariates data; enhanced vegetation index (EVI), rainfall, drought episodes, population density, aridity, proximity to water, and analyzed using the ArcGIS 10.3 (ESRI, Redlands, California, USA) software.
Malaria and environmental data description
The WHO recommends that all cases of suspected malaria should be confirmed using parasite-based diagnostic testing (either microscopy or rapid diagnostic test) before administering treatment. Thus, malaria cases were confirmed based on both rapid diagnostic tests and on laboratory analysis. A clinical case was defined as a malaria-attributable febrile episode (body temperature in excess of 37.5 °C), accompanied by headaches, nausea, excess sweating and/or fatigue censored by a 30-day window (6,27). Since the households are the variables to be analyzed, a malaria year as described in this study, is the average number of people per year who show clinical symptoms of Plasmodium falciparum malaria within the cells whose centroid falls within a radius of 10 km (for rural points) or 2 km (for urban points)(27). The environmental covariates data set used for this study are described in table 1.
Permission to use the data was obtained through a written request and subsequent approval from the DHS division of the USAID. During the DHS project, interviews and blood test analyses are conducted only if the respondent provides voluntary informed consent. Written informed consent was obtained from all participants.
Statistical analysis
Getis-Ord Gi* statistics
The Getis-Ord Gi* statistics is a local statistic that allows us to discover new locations with significant clusters of hot and coldspots. It assesses each malaria household cluster (or feature) within the context of neighboring malaria households and compares the local situation to the global situation. The 2015 Malaria indicator survey data was analyzed for malaria hotspots.
The Getis-Ord Gi* local statistics is given as:
![](data:image/png;base64,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)
Where is the attribute value for feature j; is the spatial weight between i and j; n is equal to the total number of features and:
![](data:image/png;base64,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)
Spatial regression analysis
To investigate the spatial relationship between the distribution of malaria hotspots and environmental covariates, we used the regression analysis technique. The mathematical computation applied to the global OLS is given as;
![](data:image/png;base64,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)
is the dependent or response variable for the observation i (the process to be predicted or understood), for example, the malaria hotspot cases, Xik is the value of the independent or explanatory variable kth for i used to model or predict the dependent variables. The explanatory variables include: population density ( ), enhanced vegetation index ( ), rainfall ( ), aridity ( ) drought episodes( ), and proximity to water ( ).The spatial model to be built will explain if the distribution or occurrence of malaria hotspots are due to the combination of these explanatory variables. This will help us create a prediction map that can be used for public health resource allocations due to the spatial relationship between the dependent (malaria hotspots) and explanatory variables. βk is the regression coefficients for the variable k. They are values, one for each explanatory variable, that represent the strength and type of relationship the explanatory variable has to the dependent variable. is the regression intercept. It represents the expected value for the dependent variable if all the independent variables are zero. The residuals represent the potion of the dependent variable that isn’t explained by the model(14).
The global OLS method is based on the use of only one equation (a) to explore the relationship between variables. To account for the spatial component of the OLS variables for a specified model, the GWR local regression model creates an equation (b) for each element of the dependent variable data set, in order to capture geographic variations (23). The GWR model is computed as;
![](data:image/png;base64,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)
where ui and vi represent the point coordinates of ith in space Thus, the GWR equation (b) distinguishes that spatial variations in relationships might exist and provides a way in which they can be measured and this is based mainly in its spatial ability in a GIS environment with emphasis on the differences regarding space and local disaggregation of the local statistics as thoroughly illustrated by Fotheringham(39). Although the GWR model can be advantageous in order to distinguish heterogeneity of the space itself, it also makes it easier to go from a global OLS perspective to a local analysis, thus obtaining a better grade of details and precision using the GIS environment(39).
For this study, our regression models (OLS and GWR) was built using the modelling spatial relationship in ArcGIS. Multiple models were made using a set of candidate variables until selecting a model with high explanatory power, and that contained the six explanatory variables ( to relevant for a properly specified model with respect to malaria transmission(23). A positive coefficient of an OLS candidate variable means the explanatory X variables and the response or dependent Y variables changed in the same direction and if the environmental risk factor increases, then the number of confirmed malaria cases will increase. Similarly, a negative coefficient means X and Y changed in reverse directions (15). To have a properly specified GWR model, the generated OLS summary result of the model variables should pass a statistically significant six-test check (data pre-processing background analysis).
The GWR model was constructed using a default fixed distance as the kernel type and AICc (Akaike's Information Criterion) as the bandwidth method to find the optimal distance for better model performance. The GWR generates a map that was represented as raster surfaces for the model predictions (combined strength of the relationship amongst the variables used), residuals, local R squared (model significance), condition number (difficulties identifying spatial relationship) and coefficients (explaining the strength of the relationship between the dependent and explanatory variables) and an output table demonstrating the strength of adjusted R2 significance (from 0- 1).