Understanding the importance of spatial correlation in identifying spatio-temporal variation of disease risk, in the case of malaria risk mapping in southern Ethiopia

Malaria remains a major health problem in developing countries despite a significant reduction in incidence in the last few years. Disease mapping thus helps to understand the spatial pattern and identify areas characterized by unusual risks. Several spatial models have been used to analyze the incidence of malaria. We aim to compare the predictive performance of these models and investigate the effect of ignoring spatial correlation. The reported malaria case counts of genus P.falciparum in 149 districts of southern Ethiopia from January 2016 to May 2019 were analyzed using the spatial time series model (STS) that ignores spatial correlation, Spatiotemporal conditional autoregressive model (STCAR), Spatio-temporal geostatistical model (STG) and Spatio-temporal spatial discrete approximation to log Gaussian cox process (STSDALGCP). We assess the predictive performance of the models using root mean square error, mean absolute error, and coverage probability. We found that monthly average rainfall, temperature, humidity, and EVI are significantly associated with malaria risk. The spatial variation of malaria incidence changes with time, in particular, the high incidence was observed from November to December, months after heavy rainfall, and more pronounced in the southwest of the country. STSDALGCP gives a small prediction error in test set and captures the uncertainties better than other models, while the STS model gives a high prediction error. Accounting for spatial correlation is crucial for disease risk mapping and leads to better prediction of disease risk. Since malaria transmission operates in a spatially continuous manner, a spatially continuous model should be considered when it is computationally feasible.