Proc Biol Sci. 2015 May 7;282(1806). pii: 20150347.

Avoidable errors in the modelling of outbreaks of emerging pathogens, with special reference to Ebola.

King AA1, Domenech de Cellès M2, Magpantay FM2, Rohani P3.

Author information

• 1Department of Ecology & Evolutionary Biology, University of Michigan, Ann Arbor, MI 48109, USA Center for the Study of Complex Systems, University of Michigan, Ann Arbor, MI 48109, USA Department of Mathematics, University of Michigan, Ann Arbor, MI 48109, USA Fogarty International Center, National Institutes of Health, Bethesda, MD 20892, USA kingaa@umich.edu.
• 2Department of Ecology & Evolutionary Biology, University of Michigan, Ann Arbor, MI 48109, USA.
• 3Department of Ecology & Evolutionary Biology, University of Michigan, Ann Arbor, MI 48109, USA Center for the Study of Complex Systems, University of Michigan, Ann Arbor, MI 48109, USA Fogarty International Center, National Institutes of Health, Bethesda, MD 20892, USA.

Abstract

As an emergent infectious disease outbreak unfolds, public health response is reliant on information on key epidemiological quantities, such as transmission potential and serial interval. Increasingly, transmission models fit to incidence data are used to estimate these parameters and guidepolicy. Some widely used modelling practices lead to potentially large errors in parameter estimates and, consequently, errors in model-based forecasts. Even more worryingly, in such situations, confidence in parameter estimates and forecasts can itself be far overestimated, leading to the potential for large errors that mask their own presence. Fortunately, straightforward and computationally inexpensive alternatives exist that avoid these problems. Here, we first use a simulation study to demonstrate potential pitfalls of the standard practice of fitting deterministic models to cumulative incidence data. Next, we demonstrate an alternative based on stochastic models fit to raw data from an early phase of 2014 West Africa Ebola virus disease outbreak. We show not only that bias is thereby reduced, but that uncertainty in estimates and forecasts is better quantified and that, critically, lack of model fit is more readily diagnosed. We conclude with a short list of principles to guide the modelling response to future infectious disease outbreaks.