title: Forecasting Epidemiological and Evolutionary Dynamics of Infectious Diseases.
abstract: 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.
authors: CEFE UMR 5175, CNRS-Université de Montpellier-Université Paul-Valéry Montpellier-EPHE, 1919 route de Mende, 34293 Montpellier cedex 5, France. Electronic address: sylvain.gandon@cefe.cnrs.fr.
authors: Sylvain
authors: S
authors: Gandon
authors: Department of Biology, Queen's University, Kingston, Canada.
authors: Troy
authors: T
authors: Day
authors: Department of Ecology and Evolutionary Biology and Woodrow Wilson School of Public and International Affairs, Princeton University, Princeton, NJ, USA.
authors: C Jessica E
authors: CJE
authors: Metcalf
authors: Department of Ecology and Evolutionary Biology and Woodrow Wilson School of Public and International Affairs, Princeton University, Princeton, NJ, USA.
authors: Bryan T
authors: BT
authors: Grenfell
mesh_terms: D003141:Communicable Diseases; D004196:Disease Outbreaks; D005544:Forecasting; D006801:Humans; D008954:Models, Biological