Influence of Climate on the Spatiotemporal Distribution of Malaria in Thulamela Municipality, Limpopo Province, South Africa
Lungile Makondo1, Abiodun Adeola2, 3, *, Thabo Makgoale2, Joel Botai1, 2, 4, Omolola Adisa1, 2, 5, Christina Botai2
1 Department of Geography, Geoinformatics and Meteorology, University of Pretoria, Pretoria, South Africa
2 South African Weather Service, Pretoria, South Africa
3 School of Health Systems and Public Health, Faculty of Health Sciences, University of Pretoria, Hatfield, South Africa
4 School of Agricultural, Earth and Environmental Sciences, University of KwaZulu-Natal, Westville Campus, Durban, South Africa
5 Department of Information Technology, Central University of Technology, Bloemfontein, South Africa
Malaria, though curable, continues to be a major health and socioeconomic challenge. Malaria cases have been on the rise for the last two years in the malaria-endemic region of South Africa. Thulamela Municipality in Limpopo, South Africa, which falls within several municipalities at Vhembe district that are affected by malaria. About 33,448 malaria cases were reported over a period of 20 years (1998 January-2018 December).
The study aims to determine the influence of climate on the spatiotemporal distribution of malaria cases in Thulamela Municipality for the last two decades (1998 January-2018 December).
The analysis is divided into two sections, including temporal and spatial distribution of malaria cases, and the correlating climatic and environmental factors. Time series analysis is conducted to determine the variations of malaria and climate. Malaria and climatic factors (rainfall, maximum temperature, minimum temperature) were globally correlated using matrix scatterplot spearman correlation with a certain significance level. The Ordinary Least Squares (OLS) regression was performed to determine the significant climate factors that locally affect the spatial distribution of malaria cases. The local environmental factor (rivers) was analyzed using buffering and terrain analysis.
A positive spearman correlation of the time series was found with the significance level of 0.01. The climate variables were not strongly significant to the spatial distribution of malaria at the village level. The villages which continued to record high malaria cases were in proximity to rivers by 2km. The Thulamela municipality falls within 20-30°C, which is essential for the incubation of mosquitoes and transmission of malaria. The areas receiving about 125 to 135 mm of total monthly rainfall record high malaria cases. The temperature, rainfall, and rivers are important factors for malaria transmission.
Knowledge of the drivers of the spatiotemporal distribution of malaria is essential for a predicting system to enhance effective malaria control in communities such as the Thulamela municipality.
Keywords: Malaria, Time series, Spatial analysis, Epidemiology, Ordinary least squares, Rainfall.
open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: https://creativecommons.org/licenses/by/4.0/legalcode. This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
* Address correspondence to this author at the South African Weather Service, Pretoria, South Africa; School of Health Systems and Public Health, Faculty of Health Sciences, University of Pretoria, Hatfield, South Africa; Tel: +27123676268; E-mail: email@example.com