Four key challenges in infectious disease modelling using data from multiple sources

• Health decision making increasingly uses models and data from multiple sources.
• Inference on model parameters using a multiplicity of data sources is challenging.
• Key challenges include more thoughtful model specification and criticism.
• Addressing these problems rigorously will require better use of existing tools.
• Challenges in epidemic models may motivate new statistical methods.

Public health-related decision-making on policies aimed at controlling epidemics is increasingly evidence-based, exploiting multiple sources of data. Policy makers rely on complex models that are required to be robust, realistically approximating epidemics and consistent with all relevant data. Meeting these requirements in a statistically rigorous and defendable manner poses a number of challenging problems. How to weight evidence from different datasets and handle dependence between them, efficiently estimate and critically assess complex models are key challenges that we expound in this paper, using examples from influenza modelling.