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Type: Journal article
Title: Characterising pandemic severity and transmissibility from data collected during first few hundred studies
Author: Black, A.
Geard, N.
McCaw, J.
McVernon, J.
Ross, J.
Citation: Epidemics: the journal of infectious disease dynamics, 2017; 19:61-73
Publisher: Elsevier
Issue Date: 2017
ISSN: 1755-4365
Statement of
Andrew J. Black, Nicholas Gear, James M. McCaw, Jodie McVernon, Joshua V. Ross
Abstract: Early estimation of the probable impact of a pandemic influenza outbreak can assist public health authorities to ensure that response measures are proportionate to the scale of the threat. Recently, frameworks based on transmissibility and severity have been proposed for initial characterization of pandemic impact. Data requirements to inform this assessment may be provided by "First Few Hundred" (FF100) studies, which involve surveillance-possibly in person, or via telephone-of household members of confirmed cases. This process of enhanced case finding enables detection of cases across the full spectrum of clinical severity, including the date of symptom onset. Such surveillance is continued until data for a few hundred cases, or satisfactory characterization of the pandemic strain, has been achieved. We present a method for analysing these data, at the household level, to provide a posterior distribution for the parameters of a model that can be interpreted in terms of severity and transmissibility of a pandemic strain. We account for imperfect case detection, where individuals are only observed with some probability that can increase after a first case is detected. Furthermore, we test this methodology using simulated data generated by an independent model, developed for a different purpose and incorporating more complex disease and social dynamics. Our method recovers transmissibility and severity parameters to a high degree of accuracy and provides a computationally efficient approach to estimating the impact of an outbreak in its early stages.
Keywords: Households
Markov chain
Parameter inference
Rights: © 2017 The Author(s). Published by Elsevier B.V. This is an open access article under the CCBY-NC-ND license (
DOI: 10.1016/j.epidem.2017.01.004
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