Understanding and modeling meteorological drivers of the incidence and spread of malaria in South Africa

Abstract

Malaria remains a major challenge globally and in Africa, where climate change is likely to increase its prevalence among communities with low adaptive capacity. The aim of this study was to determine the characteristics of malaria dependence on meteorological drivers, and project incidences and spread of this disease in five district municipalities in Limpopo province, South Africa. We used data from weekly epidemiological reports on hospital admissions in the five municipalities to derive associations with corresponding regional temperature, rainfall, and evapotranspiration. Wavelet transform spectral analysis was applied to identify time lags characteristic for malaria development. We presumed that all the wavelet power spectra (WTS) peaks that we found in our data are characteristic times connected to the periods of development, distribution, and survival of either mosquitoes, as disease vectors, or the pathogens they transmit, or are the periods needed for human incubation of the disease. In this way, we were able to propose a regression model for the number of admissions cases, and to provide critical values of temperature, rainfall, and evapotranspiration that initiate the spread of the disease. Disease projections for 2021-2050 and 2051-2080 were made using Representative Concentration Pathways (RCPs): RCP2.6 and RCP8.5.