Modeling infectious diseases (2008)

See all research themes

Epidemics of infectious diseases such as malaria and Rift Valley Fever (RVF) exhibit great variation from year to year. There is huge controversy within the scientific community regarding the role that climate variability plays in driving these outbreaks. While some authors claim that climate change is not driving the spread of those diseases, others show that in epidemic fringes, local temperature or rainfall play a key role in predicting outbreak size, as well as interannual variability. For instance, in northwest India it had already been known for a long time that rainfall was a good predictor for malaria cases, but the extent to which this external forcing is influencing disease dynamics was quantified only recently. This was done using a mechanistic approach based on a system of non-linear stochastic differential equations, along with a statistical method that allows for comparison of epidemiological vs. climate hypotheses. This approach is based on a large number of computer simulations. One advantage is that for northwest India, these models show remarkable prediction skill, four months in advance, when rainfall is explicitly included into the model.

We are now using a similar approach to study the influence of local meteorological variables on malaria and RVF in Senegal, Malawi and Ghana. In particular, for Senegal, we are calibrating and adapting various malaria models to fit an extensive monthly record for malaria incidence that spans almost two decades. We are working on these models through collaboration with the Pasteur Institute in two Senegalese villages presenting endemic and epidemic dynamics. The models being considered include age structure, drug resistance, bed net and insecticide control, as well as other socio-economic factors. Our methodology allows external forcing such as rainfall or temperature to be explicitly included.

During 2012, after these models have been calibrated, we will apply them to shorter malaria and RVF incidence series from Senegal, Malawi and Ghana, available through collaborations with our partners from the European project “Quantifying Weather and Climate Impacts on Health in Developing Countries” (QWeCI).