Increased Malaria Risk Due to Greenhouse Forcing and Land-Use Changes in Tropical Africa

Increased temperatures and precipitation due to climate change will impact malaria spread and transmission throughout Africa. Ermert at al. (2012) assessed potential changes in the malaria transmission using an integrated weather-disease model. The model incorporated high-resolution regional data along with the impacts of land use changes. Projections were based upon moderate and optimistic climate change scenarios. The results showed both an increase in transmission in areas with increased temperature and rainfall, but extreme increases resulted in a decrease in transmission. In addition, projections showed that epidemic risk will increase in high altitude regions, whereas in lower-altitude regions epidemic risk will decline. —Simone Berkovitz
Ermert, V., Fink, A., Morse, A., Paeth, H., 2012. The Impact of Regional Climate Change on Malaria Risk due to Greenhouse Forcing and Land-Use Changes in Tropical Africa. Environmental Health Perspectives 120, 77-84.

            According to previous studies, it has been established that climate change will alter the distribution and transmission of malaria worldwide. In some areas of the world, the disease will contract, while in other areas it will expand, as malaria seasonality is altered. Previous research has also shown that in addition to weather and climate variability, there are many socioeconomic factors, which alter transmission and prevalence levels. In the East African Highlands it has been debated whether increased temperatures or other factors have caused the recent increase in malaria. Therefore, Ermert at al. used a disease-weather integrated model in order to assess the potential changes in malaria transmission due to climate change and land-use changes in tropical Africa. Most studies use general circulation models (GCMs) to model and predict transmission, but according to the authors these models have several limitations. For example, the grid resolution of a GCM is too coarse and does not adequately capture the effects of local terrain on temperature and rainfall. In addition, model biases are not corrected and changes in land surface characteristics are never taken into account. In order to improve upon modeling techniques, Ermert at al. used high-resolution regional climate model (RCM) and land-cover changes (LUC), which were incorporated into a regional model (REMO).
            The study simulated mosquito-biting rates using the Liver Pool Malaria Model (LMM). The LMM used a REMO, which is a limited area climate model with a horizontal grid resolution of 0.5°. Three REMO integrations were created using A1B and B1 IPCC greenhouse gas emissions scenarios. The A1B scenario predicts moderate climate change due to economic growth combined with the development of energy efficient technologies, while the B1 scenario is more ecologically optimistic. LUC changes, surface temperature changes and rainfall changes were also incorporated into the model. Malaria projections for 2001 to 2050 were modeled using the integrated-disease model in order to predict epidemics under the two climate change scenarios.
            The authors found that the model accurately predicted malaria distribution in Africa under the current climate conditions for 1960–2000. From the models it was found that areas with high, but not excessive precipitation, resulted in year-round transmission. According to model predictions, there will be a decreased spread of malaria over many parts of tropical Africa, due to an increase in surface temperatures and a decrease in rainfall. However, in the Southern part of Sahel, an increase in epidemics is predicted. In East Africa, malaria transmission is predicted to increase due to higher temperatures, and slightly higher rainfall. Most notably, it was found that formerly unsuitable areas in the highland could become epidemic areas, while epidemics will decrease in the lower-altitude regions. As predicted, Ermert at al.found the changes to be stronger under the A1B scenario than in the B1 scenario.
            By using models based upon high-resolution data that takes both greenhouse gas and LUC changes into account, Ermert at al. were able to model specific, terrain based malaria projections. With high-resolution data, the authors were able to analyze the effect of altitude on epidemic risk, which has not been analyzed before. In addition, the inclusion of land use changes accelerates precipitation decline, which significantly altered projections. This new type of modeling is going to be crucial in future studies for malaria and other vector born disease predictions. 

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