Bio-climatic impact on malaria prevalence in Ghana: A multi-scale spatial modeling

Abstract

Whilst climate change is expected to tremendously influence the regional transmission of malaria, the available data reveal conflicting results. This study provides contextual evidence. We adopted multi-scale geographically weighted regression (MGWR) modelling approach. AICc and local r2 were used to evaluate performance of the MGWR. The MGWR analysis showed that LST (β = −0.667), maximum temperature (β = −0.507), mean temperature (β = −0.480), and distance from streams (β = −0.487) were negatively associated with malaria prevalence. However, enhanced vegetation index correlated positively with malaria prevalence (β = 0.663). Our results may be important for public health interventions.