Exploring the Impact of Climate Variability on Malaria Transmission Using a Dynamic Mosquito-Human Malaria Model

Abstract

Introduction: The reasons for malaria resurgence mostly in Africa are yet to be well understood. Although the causes are often linked to regional climate change, it is important to understand the impact of climate variability on the dynamics of the disease. However, this is almost impossible without adequate long-term malaria data over the study areas.

Methods: In this study, we develop a climate-based mosquito-human malaria model to study malaria dynamics in the human population over KwaZulu-Natal, one of the epidemic provinces in South Africa, from 1970-2005. We compare the model output with available observed monthly malaria cases over the province from September 1999 to December 2003. We further use the model outputs to explore the relationship between the climate variables (rainfall and temperature) and malaria incidence over the province using principal component analysis, wavelet power spectrum and wavelet coherence analysis. The model produces a reasonable fit with the observed data and in particular, it captures all the spikes in malaria prevalence.

Results: Our results highlight the importance of climate factors on malaria transmission and show the seasonality of malaria epidemics over the province. Results from the principal component analyses further suggest that, there are two principal factors associated with climates variables and the model outputs. One of the factors indicate high loadings on Susceptible, Exposed and Infected human, while the other is more correlated with Susceptible and Recovered humans. However, both factors reveal the inverse correlation between Susceptible-Infected and Susceptible-Recovered humans respectively. Through the spectrum analysis, we notice a strong annual cycle of malaria incidence over the province and ascertain a dominant of one year periodicity. Consequently, our findings indicate that an average of 0 to 120-day lag is generally noted over the study period, but the 120-day lag is more associated with temperature than rainfall. This is consistence with other results obtained from our analyses that malaria transmission is more tightly coupled with temperature than with rainfall in KwaZulu-Natal province.

Keywords: Anopheles arabiensis; Climate variability; Malaria dynamics; Malaria incidence; Mathematical model; South Africa.

Fig. (1).

The map of KwaZulu-Natal province, South Africa. Source: GIS unit of the Medical Research Council of South Africa.

Fig. (2).

Time series of (a) daily mean temperature, and (b) rainfall of KwaZulu-Natal province from 1970 - 2005.

Fig. (3).

Flow diagram of the mosquito-human malaria model.

Fig. (4).

The modelled and reported cases of malaria over KwaZulu-Natal province, South Africa from September 1999 to December 2003.

Fig. (5).

The wavelet analysis of the climate variables of KwaZulu-Natal province from 1970-2005}. The time series of average monthly (a) rainfall, (d) temperature and (g) simulated infected humans. The wavelet power spectrum of (b) rainfall, (e) temperature and (h) Infected humans time series. The cross-hatched region is the cone of influence, where zero padding has reduced the variance and only pattern above the region are considered reliable. The colour code values from blue (low values) to red (high values). The global wavelet power spectrum of (c) rainfall, (f) temperature and (i) Infected humans have been scaled. The black contour line corresponds to 10% significance level, using the global wavelet as the background spectrum.

Fig. (6).

Cross-correlation coefficients of time series of daily climate variables and simulated infected human at several lags.

Fig. (7).

Wavelet coherence of rainfall and simulated infected human over KwaZulu-Natal province from 1970-2005. The arrows indicate the relative phasing of the variables, while the faded regions represent the cone of influence and are not considered for the analyses.

Fig. (8).

Wavelet coherence of temperature and simulated infected human over KwaZulu-Natal from 1970-2005. The arrows indicate the relative phasing of the variables, while the faded regions represent the cone of influence and are not considered for the analyses.