Résumé
Mathematical models have been powerful tools in developing mechanistic understanding of infectious diseases. Furthermore, they have allowed detailed forecasting of epidemiological phenomena such as outbreak size, which is of considerable public-health relevance. The short generation time of pathogens and the strong selection they are subjected to (by host immunity, vaccines, chemotherapy, etc.) mean that evolution is also a key driver of infectious disease dynamics. Accurate forecasting of pathogen dynamics therefore calls for the integration of epidemiological and evolutionary processes, yet this integration remains relatively rare. We review previous attempts to model and predict infectious disease dynamics with or without evolution and discuss major challenges facing the development of the emerging science of epidemic forecasting.
Long-term monitoring of infectious disease dynamics allows the estimation of key parameters of epidemiological models. These models can be used to forecast future epidemics and to implement effective public-health control measures.
Many pathogens exhibit extensive genetic variation and so can readily adapt to control measures like drugs and vaccines. We review recent attempts to combine epidemiology and evolution to predict the evolutionary trajectories of pathogens.
Inspired by the success of weather forecasting we discuss the current limits of the predictive power of evolutionary epidemiology. The development of the emerging science of epidemic forecasting requires better integration of mathematical epidemiology, population genetics, statistics, and numerical computation.