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Epidemics. 2019 Dec;29:100371. doi: 10.1016/j.epidem.2019.100371. Epub 2019 Sep 11.

Accurate forecasts of the effectiveness of interventions against Ebola may require models that account for variations in symptoms during infection.

Author information

1
Mathematical Institute, University of Oxford, Andrew Wiles Building, Radcliffe Observatory Quarter, Woodstock Road, Oxford, OX2 6GG, UK.
2
Department of Plant Sciences, University of Cambridge, Downing Street, Cambridge, CB2 3EA, UK.
3
Graduate School of Medicine, Hokkaido University, Hokkaido, Japan.
4
Mathematical Institute, University of Oxford, Andrew Wiles Building, Radcliffe Observatory Quarter, Woodstock Road, Oxford, OX2 6GG, UK; Department of Zoology, University of Oxford, South Parks Road, Oxford, OX1 3PS, UK; Christ Church, University of Oxford, St Aldates, Oxford, OX1 1DP, UK. Electronic address: robin.thompson@chch.ox.ac.uk.

Abstract

Epidemiological models are routinely used to predict the effects of interventions aimed at reducing the impacts of Ebola epidemics. Most models of interventions targeting symptomatic hosts, such as isolation or treatment, assume that all symptomatic hosts are equally likely to be detected. In other words, following an incubation period, the level of symptoms displayed by an individual host is assumed to remain constant throughout an infection. In reality, however, symptoms vary between different stages of infection. During an Ebola infection, individuals progress from initial non-specific symptoms through to more severe phases of infection. Here we compare predictions of a model in which a constant symptoms level is assumed to those generated by a more epidemiologically realistic model that accounts for varying symptoms during infection. Both models can reproduce observed epidemic data, as we show by fitting the models to data from the ongoing epidemic in the Democratic Republic of the Congo and the 2014-16 epidemic in Liberia. However, for both of these epidemics, when interventions are altered identically in the models with and without levels of symptoms that depend on the time since first infection, predictions from the models differ. Our work highlights the need to consider whether or not varying symptoms should be accounted for in models used by decision makers to assess the likely efficacy of Ebola interventions.

KEYWORDS:

Disease control; Ebola virus disease; Epidemic forecasting; Infectious disease management; Interventions; Mathematical modelling

PMID:
31784341
DOI:
10.1016/j.epidem.2019.100371
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