Knut Rand: Bayesian Time Series Modelling of Climate-Health Data

Climate and weather can affect disease prevalence in different ways. For instance, humidity and temperature affect the life cycles of mosquitos which can greatly influence the prevalence of vector-borne diseases like malaria and dengue. Modelling this relationship is very important, both in the short term for outbreak preparedness, and in the long term, for health systems to adapt to the changing climate. However, this modelling is difficult because of low amounts of quality health data, complexities in spatial-temporal modelling, and the many different domains (vector biology, climate, epidemiology).

In this talk I will present our work on building a framework both for developing modularized and adaptable climate-health models, and for rigorously evaluating the utility of these models.