Understanding and modeling meteorological drivers of the number of hospital admissions for malaria in South Africa

We preformed statistical analysis of two sets of malaria incidence time series: of daily admissions from two large public hospitals in Limpopo Province in South Africa (records taken in the period 2002-2017), and of weekly epidemiological reports from five districts in the same province (for the period 2000-2020). We analysed these time series in relation to time series of temperature and rainfall ground or satellite data from the same geographical area.

Firstly, we used wavelet transform (WT) cross-correlation analysis to monitor and characterize coincidences in daily changes of meteorological variables and variations in hospital admissions. All our daily admission records had global wavelet power spectra (WTS) of the power-law type, indicating that they are outputs of complex sets of causes acting on different time scales. We found that malaria in South Africa is a seasonal multivariate event, initiated by co-occurrence of heat and rainfall. We then proceeded to utilize obtained results for the analysis of the weekly cases data, using the WTS superposition of signals rule to discern WTS peaks that are time lags between the onset of combined meteorological drivers and hospital admissions for malaria. We presumed that all these peaks are characteristic times connected to the characteristic periods of development, distribution and survival of either mosquitos, as disease vectors, the pathogens they transmit, or the times needed for human incubation of the disease. Thus, we were able to propose a regression model for the number of admissions (for malaria) cases, and to provide critical values of temperature and rainfall for the initiation of the disease spread.

Finally, using the developed model we investigated how future changes of meteorological variables and their combination can affect malaria dynamics, and thus provide information that can be of use for public health preparedness.