Risk Mapping Zoonotic Diseases throughout Africa

The transmission of infectious disease can be closely linked with different ecological, social, and economic factors. Our project aims to identify factors associated with a number of diseases, e.g. Rift Valley fever, Q fever, Lassa fever and Hantavirus disease. By recognising specific indicators of potential disease transmission we aim to create detailed risk maps which are able to predict transmission of these diseases, in nationwide study areas. We will initially focus upon Rift Valley Fever and Q fever in Kenya but as the project progresses we aim to apply our techniques to other countries and zoonotic diseases. Rift Valley fever and Q fever not only cause morbidity and mortality in humans, but lead to extremely high rates of abortion and stillbirth within herds of livestock. This reduces both the food and income available to poor farming communities.
In this project we will use regression models to identify covariates that correlate with presence of zoonotic diseases. Once we have formulated a theory on the distribution of risk in our study areas using regression analysis, prevalence data will be collected via farmer surveys to help validate the predictions made. These surveys will help to create more informed models, and help to further recognise covariates which should be included or excluded.
Having validated these models we will apply these techniques to other countries or regions with disease presence, and creating risk maps for these new areas. This will allow us to do three things; 1) identify regions which have similar patterns or transmission/risk, 2) identify covariates which are globally significant for the transmission of these diseases and 3) predict potential outbreaks in one country, using data from another. Further data collection can then be carried out to verify the predictions from the models in these new study sites.