£3M awarded for climate model to predict disease outbreak
Researchers across 13 European and African research institutes will work together to integrate data from climate modelling and disease forecasting systems to predict the likelihood of an epidemic up to six months in advance. The research, funded by the European Commission Seventh Framework programme, will focus on climate and disease in Senegal, Ghana and Malawi and aims to give decision makers the necessary time to deploy intervention methods to help prevent large scale spread of diseases such as Rift Valley Fever and malaria.
It is thought that climate change will change global disease distributions, and although scientists have significant knowledge of the climate triggers for particular diseases, more research is needed to understand how far into the future these events can be predicted. The work will bring together experts in science and health to investigate the link between climate and vector-borne diseases, including zoonotic diseases transferred from animals to humans.
Dr Andy Morse, from the School of Environmental Sciences, said: “We know that climate variability has a significant impact on the incidence of human and animal diseases. In Africa, where the relationship between climate change and health is becoming recognised, human and animal disease has a particular effect on economic development. It is vital, therefore, that we improve our understanding of the climate triggers for disease and the forecasting systems used to predict outcomes.”
Scientists already know that the risk of epidemics in tropical countries increases shortly after a season of good rainfall – when heat and humidity allow insects, such as mosquitoes, to thrive. These insects can cause the spread of disease such as malaria and Rift Valley Fever, but there are a number of factors to consider before reliable predictions can be made.
Professor Matthew Baylis, from the School of Veterinary Science, explains: “Rift Valley Fever can spread amongst the human and animal population during periods of heavy rain, when flood water mosquitoes flourish and lay their eggs. If this rainfall occurs unexpectedly during the dry season, when cattle are kept in the villages rather than out on the land, the mosquitoes can infect the animals at the drinking ponds. Humans can then contract the disease by eating infected animals. Working with partners in Africa, we can bring this information together to build a much more accurate picture of when to expect epidemics.”
Dr Morse continued: “We will look at historical and contemporary climate data and combine it with disease incidence information, as well as integrating monthly and seasonal forecasts into a single seamless forecast system that will allow disease risk projections to be made beyond the conventional predictable time-limit. We will also look at data for vector-borne diseases and integrate them into the forecasting model. All this information will be fed into a decision support system to be developed with decision makers on national health issues.”