Fitting dynamic models to epidemic outbreaks with quantified uncertainty: A primer for parameter uncertainty, identifiability, and forecasts

Chowell, Gerardo

Emerging and re-emerging infectious diseases are undoubtedly one of humankind's most important health and security risks (Fauci & Morens, 2016) . As epidemic threats increase so is the potential impact of mathematical and statistical inference and simulation approaches to guide prevention and mitigation plans. As the recent 2013e2016 Ebola epidemic exemplified, an infectious disease outbreak often forces public health officials to make key decisions to mitigate the outbreak in a changing environment where multiple factors positively or negatively impact local disease transmission . Hence, public health officials are often interested in practical yet mathematically rigorous and computationally efficient approaches that comprehensively assimilate data and model uncertainty to 1) generate estimates of key transmission parameters, 2) assess the impact of control interventions (vaccination campaigns, behavior changes), 3) test hypotheses, 4) evaluate how behavior changes affect transmission dynamics, 5) gain insight to the contribution of different transmission pathways, 6) optimize the impact of control strategies, and 7) generate short and long-term forecasts, just to name a few.