The potential effects of climate change on malaria transmission in Africa using bias-corrected regionalised climate projections and a simple malaria seasonality model

Abstract

Climatic conditions such as relatively cold temperatures and dryness are able to limit malaria transmission. Climate change is therefore expected to alter malaria spread. A previous assessment of the potential impacts of climate change on the seasonality of malaria in Africa is revisited. Bias-corrected regional climate projections with a horizontal resolution of 0.5 • are used from the Regional Model (REMO), which include land use and land cover changes. The malaria model employed is the climate-driven seasonality model (MSM) from the Mapping Malaria Risk in Africa project for which a comparison with data from the Malaria Atlas Project (MAP) and the Liverpool Malaria Model (LMM), and a novel validation procedure lends more credence to results. For climate scenarios A1B and B1 and for 2001-2050, REMO projects an overall drying and warming trend in the African malaria belt, that is largely imposed by the man-made degradation of vegetation. As a result, the malaria projections of the MSM show a decreased length of the malaria season in West Africa. The northern Sahel is no more longer suitable for malaria in the projections and shorter malaria seasons are expected for various areas farther south. In East Africa, higher temperatures and nearly unchanged precipitation patterns lead to longer transmission seasons and an increase in highland malaria. Assuming constant population numbers, an overall increase in person-months of exposure of up to 6 % is found. The results of this simple seasonality model are similar to previous projections from the more complex LMM. However, a different response to the warming of highlands is found for the two models. It is concluded that the MSM is an efficient tool to assess the climate-driven malaria seasonality and that an uncertainty analysis of future malaria spread would benefit from a multi-model approach.

Bibliometric analysis of peer-reviewed literature on climate change and human health with an emphasis on infectious diseases

“…The model aims to apply the statistical equations to future climate scenarios in order to predict the actual distribution of the disease. These models have been applied to malaria and dengue fever [27,76,[103][104][105][106][107][108]. The case incidence of dengue fever has multiplied 30fold since the 1960s [109].…”

Spatiotemporal epidemic characteristics and risk factor analysis of malaria in Yunnan Province, China

“…Rice fields and their surrounding area, such as irrigation canals, were the common breeding grounds except for Anopheles dirus , and all of these Anopheles species have a strong human-biting habit. Climate warming has caused an extension of the malaria transmission season, and has triggered research into the relationship between malaria and meteorological factors [15–17]. …”

Variability and Predictability of West African Droughts: A Review on the Role of Sea Surface Temperature Anomalies

“…The Coordinated Regional Climate Downscaling Experiment in Africa (CORDEX-Africa) has led to a coordinated evaluation of RCM skill, spread and uncertainties for simulating the West African monsoon mean climate and, to a lesser extent, its simulated onset and variability (Nikulin et al 2012;Hernández-Díaz et al 2013). The European Commission Seventh Framework Programme (EC FP7) Quantifying Weather and Climate Impacts on Health in Developing Countries (QWECI) project has aimed to understand, on a more fundamental level, the climate drivers of the vector-borne diseases of malaria, Rift Valley fever, and certain tickborne diseases, all of which have major human and livestock health and economic implications in Africa (Cash et al 2013;Tompkins and Ermert 2013;Ermert et al 2013;Caminade et al 2014). Some of these international projects have focused on the impact of SST anomalies on the WAM at interannual and decadal time scales.…”