Data Analytics of climatic factor influence on the impact of malaria incidence

Abstract

Predicting association between the malaria risk and its climatic predictors provides individuals and public health officials with prior knowledge for effective prevention and control measures. This paper presents an integrated analysis of a total of 2,148 confirmed cases of malaria incidence for Aboh Mbaise General Hospital, together with the satellite meteorological data downloaded from National Centre for Environmental Prediction (NCEP). By pre-whitening the climatic data sets and analysing their cross-correlation with the malaria incidence, we find that temperature and precipitation have negligible lagged effects on the malaria occurrence in the study area. A further analysis reveals that relative humidity shows significant association (P-value < 0:05) with the malaria incidence. However, regression model with autoregressive error structure AR(1) is then used to establish the relationship between the malaria incidence and relative humidity time series. The findings look to confirm the significant contribution of relative humidity to the malaria incidence in the study area due to its high humidity characteristics (about 74% average relative humidity) occurring mostly during the wet season.