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Forecasting the 2001 Foot-and-Mouth Disease Epidemic in the UK
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 fore...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer US
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6132414/ https://www.ncbi.nlm.nih.gov/pubmed/29238900 http://dx.doi.org/10.1007/s10393-017-1293-2 |
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author | Shanafelt, David W. Jones, Glyn Lima, Mauricio Perrings, Charles Chowell, Gerardo |
author_facet | Shanafelt, David W. Jones, Glyn Lima, Mauricio Perrings, Charles Chowell, Gerardo |
author_sort | Shanafelt, David W. |
collection | PubMed |
description | 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 [Formula: see text] 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. |
format | Online Article Text |
id | pubmed-6132414 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Springer US |
record_format | MEDLINE/PubMed |
spelling | pubmed-61324142018-09-14 Forecasting the 2001 Foot-and-Mouth Disease Epidemic in the UK Shanafelt, David W. Jones, Glyn Lima, Mauricio Perrings, Charles Chowell, Gerardo Ecohealth Original Contribution 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 [Formula: see text] 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. Springer US 2017-12-13 2018 /pmc/articles/PMC6132414/ /pubmed/29238900 http://dx.doi.org/10.1007/s10393-017-1293-2 Text en © The Author(s) 2017 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. |
spellingShingle | Original Contribution Shanafelt, David W. Jones, Glyn Lima, Mauricio Perrings, Charles Chowell, Gerardo Forecasting the 2001 Foot-and-Mouth Disease Epidemic in the UK |
title | Forecasting the 2001 Foot-and-Mouth Disease Epidemic in the UK |
title_full | Forecasting the 2001 Foot-and-Mouth Disease Epidemic in the UK |
title_fullStr | Forecasting the 2001 Foot-and-Mouth Disease Epidemic in the UK |
title_full_unstemmed | Forecasting the 2001 Foot-and-Mouth Disease Epidemic in the UK |
title_short | Forecasting the 2001 Foot-and-Mouth Disease Epidemic in the UK |
title_sort | forecasting the 2001 foot-and-mouth disease epidemic in the uk |
topic | Original Contribution |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6132414/ https://www.ncbi.nlm.nih.gov/pubmed/29238900 http://dx.doi.org/10.1007/s10393-017-1293-2 |
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