Spatial and spatio-temporal analysis of malaria cases in Zimbabwe
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RESEARCH ARTICLE
Spatial and spatio‑temporal analysis of malaria cases in Zimbabwe Isaiah Gwitira1* , Munashe Mukonoweshuro1, Grace Mapako1, Munyaradzi D. Shekede1, Joconiah Chirenda2 and Joseph Mberikunashe3
Abstract Background: Although effective treatment for malaria is now available, approximately half of the global population remain at risk of the disease particularly in developing countries. To design effective malaria control strategies there is need to understand the pattern of malaria heterogeneity in an area. Therefore, the main objective of this study was to explore the spatial and spatio-temporal pattern of malaria cases in Zimbabwe based on malaria data aggregated at district level from 2011 to 2016. Methods: Geographical information system (GIS) and spatial scan statistic were applied on passive malaria data collected from health facilities and aggregated at district level to detect existence of spatial clusters. The global Moran’s I test was used to infer the presence of spatial autocorrelation while the purely spatial retrospective analyses were performed to detect the spatial clusters of malaria cases with high rates based on the discrete Poisson model. Furthermore, space-time clusters with high rates were detected through the retrospective space-time analysis based on the discrete Poisson model. Results: Results showed that there is significant positive spatial autocorrelation in malaria cases in the study area. In addition, malaria exhibits spatial heterogeneity as evidenced by the existence of statistically significant (P 1.96) indicated that neighbouring districts have similar malaria cases at county level. Detecting malaria clusters using SaTScan
In this study, scan statistics [42] was applied in SaTScan v9.6 (https://www.satscan.org/) software to detect high cluster rate of malaria. In this case, spatial scan statistic, based on the discrete Poisson model, was applied to identify purely spatial clusters of malaria cases by year. On the other hand, the space-time scan statistic, based on Space-Time Poisson model was adopted to determine the presence of space-time clusters of malaria cases by month over the study period. Three datasets were prepared for use in SaTScan and these were: a case file representing annual malaria cases per
Gwitira et al. Infect Dis Poverty
(2020) 9:146
each district (n = 59) from 2011 to 2016; a coordinate file representing geographic coordinates of the centroid of each district; and a population file representing the projected total population for each year from 2011 to 2016 for the respective district. The program identified statistically significant retrospective clusters based on annual malaria cases aggregated per district in Zimbabwe from 2011 to 2016. SaTscan tests whether the number of malaria cases within any spatial window exceeds the number expected by a random process [57]. To achieve this, the centroid of each district was first determined and extracted in a GIS environment. The spatial join function in a GIS was then used to lin
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