Climate warming may worsen development in Africa and may affect human health by bringing about changes in the ecology of infectious diseases. There is therefore a need to enhance the ability to adapt to future climate change. Malaria is a major public health problem in Ethiopia. Unfortunately, there are no practical tools to predict malaria epidemics based on climate forecasts. Such tools would be useful in making efficient use of the limited resources for malaria control.
In this project, scientists from Ethiopia and Norway collaborated to incorporate climate variability and forecast information for malaria epidemics. The collaboration also aimed to strengthen PhD and masters programmes in Ethiopia.
From 2007 – 2012, this project combined new population-based malaria transmission information with climate and land use variability data to develop an early warning to predict malaria epidemics in Ethiopia.
Summary of Ethiopian Malaria Prediction System (EMAPS) project
While the generation of precipitation depends on local ascent and cooling of the air, our research has provided new data on the transport of moisture into the country that may improve weather and climate change forecasting. We developed a new classification of climate zones, have mapped drought episodes in all parts of Ethiopia during the last decades, and have improved seasonal weather forecasting. Our hydrology studies show the effect of potential climate change differs among the Ethiopian river basins. Our analysis shows the annual river flows are sensitive to variations in rainfall, but only moderately sensitive to temperature changes.
We integrated hydrological, meteorological and malaria studies using a mathematical model. We use rainfall, temperature and other environmental data from a regional climate model (the Weather Research and Forecasting Model), and we also include human and cattle densities to describe the dynamics of the malaria mosquitoes, and this influences malaria transmission.
The computer model, Open Malaria Warning, incorporates hydrological, meteorological, mosquito-breeding and land-use data to find out when and where outbreaks are likely to occur. The model made direct use of the limited real-time information available in typical rural areas. To have confidence the model describes observed malaria epidemics, there is a need for several years of active monitoring of malaria cases and mosquito densities. Such data is rare in Africa. This model is not only a tool for predicting malaria, but can also be used to understand malaria transmission.
Our research aimed to improve our understanding of malaria in the Ethiopian Highlands. We selected the highland areas because global warming would make its impact here, and would increase malaria transmission. Both through epidemiological and entomological studies, we show that malaria transmission takes place above 2000 m altitude. In another study, we show the ideal temperature for malaria transmission is about 25 degrees, underlining that global warming may lead to more malaria in highland areas, and less in the lowlands with already high average temperatures.
We also compared malaria transmission in the highlands with that of the lowlands, characterising malaria transmission over some years in both highlands and lowlands. This provided us with new knowledge on how malaria is transmitted in Ethiopia, how intense the seasonal transmission is, and how malaria occurs in different populations.
A retrospective review of 10 years of malaria from south-east Ethiopia showed the association between whether and malaria is complex. Although our statistical model showed that we could predict malaria for large areas, malaria transmission varies from place to place, and depends on local environmental conditions. Thus, to make a good malaria prediction for specific locations, we need to have good and local knowledge about each area, and our computer model must adapt for local scale prediction. However we note that weather variability currently is the main driver of malaria in Ethiopia.