A project to predict the effects of climate change on health in Africa
Everybody knows that climate change particularly affects developing countries, but its effects on health are still very hard to predict. For that reason, the QWECI project set out to assist medical practitioners and public health decision-makers in allocating resources and implementing preventative measures ahead of disease epidemics. The project was coordinated by the University of Liverpool in the United Kingdom.
The QWECI project brought together researchers from 13 European and African research institutes to integrate data from climate-modelling and disease-forecasting systems. The project focused on climate and disease in Senegal, Ghana and Malawi and aimed to give decision-makers the necessary time to deploy intervention methods and help prevent large-scale spread of diseases such as malaria and Rift Valley fever. It is expected to help predict the likelihood of a malaria epidemic four to six months in advance.
The overall objective of QWECI was to combine state-of-the-art climate models, weather-dependent infection-control data for key African diseases, and local knowledge about population behaviour, disease, vectors and transmission patterns. The outputs could thus generate maps of infection risk appropriate to the decision-making of health professionals on the ground and the policy-making of governments in susceptible countries.
QWECI’s value-added resides in the integration of the most reliable climate-based prediction models with models of climate controls on disease risk variables for ‘vector-borne diseases’ (VBDs) on medium and long timescales. This results in unique and meaningful information which can be rapidly conveyed to end-users and allows for the quantification and prediction of the impact of climate and weather on health in Africa.
The project team has taken malaria modelling driven by seasonal-scale ensemble prediction systems to the operational cusp. The region’s capacity to use and interact with malaria-modelling technologies was also developed through local parameter settings from field studies in the region, and methods including long-range WiFi to communicate the results to local users.
For more information you can visit: http://www.liv.ac.uk/qweci/