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Forecasting the 2001 Foot-and-Mouth Disease Epidemic in the UK

Lookup NU author(s): Dr Glyn Jones

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This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).


Abstract

Near real-time epidemic forecasting approaches are needed to respond to the increasing number of infectious disease outbreaks. In this paper, we retrospectively assess the performance of simple phenomenological models that incorporate early sub-exponential growth dynamics to generate short-term forecasts of the 2001 foot-and-mouth disease epidemic in the UK. For this purpose, we employed the generalized-growth model (GGM) for pre-peak predictions and the generalized-Richards model (GRM) for post-peak predictions. The epidemic exhibits a growth-decelerating pattern as the relative growth rate declines inversely with time. The uncertainty of the parameter estimates (r and p) narrows down and becomes more precise using an increasing amount of data of the epidemic growth phase. Indeed, using only the first 10–15 days of the epidemic, the scaling of growth parameter (p) displays wide uncertainty with the confidence interval for p ranging from values ~ 0.5 to 1.0, indicating that less than 15 epidemic days of data are not sufficient to discriminate between sub-exponential (i.e., p < 1) and exponential growth dynamics (i.e., p = 1). By contrast, using 20, 25, or 30 days of epidemic data, it is possible to recover estimates of p around 0.6 and the confidence interval is substantially below the exponential growth regime. Local and national bans on the movement of livestock and a nationwide cull of infected and contiguous premises likely contributed to the decelerating trajectory of the epidemic. The GGM and GRM provided useful 10-day forecasts of the epidemic before and after the peak of the epidemic, respectively. Short-term forecasts improved as the model was calibrated with an increasing length of the epidemic growth phase. Phenomenological models incorporating generalized-growth dynamics are useful tools to generate short-term forecasts of epidemic growth in near real time, particularly in the context of limited epidemiological data as well as information about transmission mechanisms and the effects of control interventions.


Publication metadata

Author(s): Shanafelt DW, Jones G, Lima M, Perrings C, Chowell G

Publication type: Article

Publication status: Published

Journal: EcoHealth

Year: 2018

Volume: 15

Issue: 2

Pages: 338-347

Print publication date: 01/06/2018

Online publication date: 13/12/2017

Acceptance date: 15/11/2017

Date deposited: 20/07/2018

ISSN (print): 1612-9202

ISSN (electronic): 1612-9210

Publisher: Springer US

URL: https://doi.org/10.1007/s10393-017-1293-2

DOI: 10.1007/s10393-017-1293-2


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