Avoidable errors in the modelling of outbreaks of emerging pathogens,
with special reference to Ebola |
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Authors: | Aaron A King Matthieu Domenech de Cellès Felicia M G Magpantay Pejman Rohani |
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Institution: | 1.Department of Ecology & Evolutionary
Biology, University of Michigan, Ann Arbor, MI 48109, USA;2.Center for the Study of Complex
Systems, University of Michigan, Ann Arbor, MI 48109, USA;3.Department of Mathematics, University of Michigan, Ann Arbor, MI
48109, USA;4.Fogarty International Center, National Institutes of Health, Bethesda,
MD 20892, USA |
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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 guide policy. 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. |
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Keywords: | Ebola virus disease forecast emerging infectious disease |
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