Abstract
The north-eastern parts of South Africa, comprising the Limpopo Province, have recorded a sudden rise in the rate of malaria morbidity and mortality in the 2017 malaria season. The epidemiological profiles of malaria, as well as other vector-borne diseases, are strongly associated with climate and environmental conditions. A retrospective understanding of the relationship between climate and the occurrence of malaria may provide insight into the dynamics of the disease’s transmission and its persistence in the north-eastern region. In this paper, the association between climatic variables and the occurrence of malaria was studied in the Mutale local municipality in South Africa over a period of 19-year. Time series analysis was conducted on monthly climatic variables and monthly malaria cases in the Mutale municipality for the period of 1998–2017. Spearman correlation analysis was performed and the Seasonal Autoregressive Integrated Moving Average (SARIMA) model was developed. Microsoft Excel was used for data cleaning, and statistical software R was used to analyse the data and develop the model. Results show that both climatic variables’ and malaria cases’ time series exhibited seasonal patterns, showing a number of peaks and fluctuations. Spearman correlation analysis indicated that monthly total rainfall, mean minimum temperature, mean maximum temperature, mean average temperature, and mean relative humidity were significantly and positively correlated with monthly malaria cases in the study area. Regression analysis showed that monthly total rainfall and monthly mean minimum temperature (R2 = 0.65), at a two-month lagged effect, are the most significant climatic predictors of malaria transmission in Mutale local municipality. A SARIMA (2,1,2) (1,1,1) model fitted with only malaria cases has a prediction performance of about 51%, and the SARIMAX (2,1,2) (1,1,1) model with climatic variables as exogenous factors has a prediction performance of about 72% in malaria cases. The model gives a close comparison between the predicted and observed number of malaria cases, hence indicating that the model provides an acceptable fit to predict the number of malaria cases in the municipality. To sum up, the association between the climatic variables and malaria cases provides clues to better understand the dynamics of malaria transmission. The lagged effect detected in this study can help in adequate planning for malaria intervention.
Highlights
Malaria poses the biggest threat with about 40% of the world’s population at risk of infection among other vector-borne diseases [1]
Analysis revealed a strong association between years of high malaria cases and years of above normal the analysis revealed a strong association between years of high malaria cases and years of above rainfall, which significantly increased relative humidity (RH) to an average of about 65% and both Tmin and Tmax values normal rainfall, which significantly increased RH to an average of about 65% and both Tmin and at an average of 18 °C and 26 °C, respectively
Malaria transmission shows seasonality in accordance with climate in South Africa, only a few studies have been conducted in assessing the relationship between malaria cases and climatic variables at a local level, in the north-eastern part of South Africa
Summary
Malaria poses the biggest threat with about 40% of the world’s population at risk of infection among other vector-borne diseases [1]. According to [3], about 4.9 million of the South African population representing 10% of the total population live in the malaria-endemic area. Malaria is majorly endemic in three provinces, namely Limpopo, Mpumalanga, and KwaZulu-Natal, occasionally few major occurrences are sighted in the Northern Cape and North-West provinces along the Orange and Molopo Rivers as a result of the provision of suitable breeding habitats for mosquitoes to survive (Department of Health, South Africa, 2007). Potential risk factors for malaria transmission such as population movement, population immunity, availability of suitable vector habitats, malaria control measures, social and economic status (a reflection of housing types and housing conditions), environmental factors (land use/cover; vegetation, water body, irrigation/farmlands and elevation) and climatic factors (rainfall, temperature, relative humidity) have been shown to have a significant impact on the transmission of the disease [4,5,6]. Impacts will vary based on where a person lives, how sensitive they are to health risks, how much they are exposed to climate impacts, and how well they and their community are able to adapt to these impacts
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