The impact of rainfall and temperature on malaria dynamics in the KwaZulu-Natal province, South Africa

Abstract

The impact of climate variability on malaria transmission cannot be overemphasized. The interconnection between them could be well established using both statistical and deterministic models. However, this is difficult due to limited long-term malaria data over the study areas. In this article, analysis of simulated data from the previous study of Abiodun was carried out to draw important inference that offers in-depth understanding on climate-malaria incidence linkages over KwaZulu-Natal province. In particular, linear models based on stepwise regression were formulated for the number of exposed, infected and recovered individuals from malaria based on some climate variables and number of susceptible individuals to malaria. In fitting linear model to malaria data, care must be taken in ensuring that residuals of the model are not serially correlated. Ljung–Box test shows that residuals of the models are serially correlated. As a remedial measure, autoregressive integrated moving average model was fitted to the correlated residuals. In addition, it was found that an increase in daily rain amount and mean temperature significantly raises the chance of exposure to malaria while number of susceptible and exposed individuals affects transmission of malaria infection. The proportion of recovered individuals depends much on the number of malaria infected individuals rather than climatic variables.