Disease control is a complex problem, requiring knowledge not only of the biology of the disease, but of many other factors, from immunity of the population, to living conditions, public policies, or human behaviour. Integrating this information in a meaningful manner is not trivial and we develop new methods and approaches, at the interface between theory, computational, and experimental sciences.

For example, if before asking the doctor people ask Dr. Google, we can use changes in collective search patterns to now-cast outbreaks and improve surveillance systems.
We use different data sources (from traditional surveys to “big data”) and combine methods (mathematical modelling, machine learning, and others), to extract consistent behavioural patterns. Despite the complex nature of such research, we strive to make predictions that can be informative to decision-makers and the society.