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Predictability in process-based ensemble forecast of influenza
Process-based models have been used to simulate and forecast a number of nonlinear dynamical systems, including influenza and other infectious diseases. In this work, we evaluate the effects of model initial condition error and stochastic fluctuation on forecast accuracy in a compartmental model of...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6394909/ https://www.ncbi.nlm.nih.gov/pubmed/30817754 http://dx.doi.org/10.1371/journal.pcbi.1006783 |
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author | Pei, Sen Cane, Mark A. Shaman, Jeffrey |
author_facet | Pei, Sen Cane, Mark A. Shaman, Jeffrey |
author_sort | Pei, Sen |
collection | PubMed |
description | Process-based models have been used to simulate and forecast a number of nonlinear dynamical systems, including influenza and other infectious diseases. In this work, we evaluate the effects of model initial condition error and stochastic fluctuation on forecast accuracy in a compartmental model of influenza transmission. These two types of errors are found to have qualitatively similar growth patterns during model integration, indicating that dynamic error growth, regardless of source, is a dominant component of forecast inaccuracy. We therefore examine the nonlinear growth of model initial error and compute the fastest growing directions using singular vector analysis. Using this information, we generate perturbations in an ensemble forecast system of influenza to obtain more optimal ensemble spread. In retrospective forecasts of historical outbreaks for 95 US cities from 2003 to 2014, this approach improves short-term forecast of incidence over the next one to four weeks. |
format | Online Article Text |
id | pubmed-6394909 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-63949092019-03-08 Predictability in process-based ensemble forecast of influenza Pei, Sen Cane, Mark A. Shaman, Jeffrey PLoS Comput Biol Research Article Process-based models have been used to simulate and forecast a number of nonlinear dynamical systems, including influenza and other infectious diseases. In this work, we evaluate the effects of model initial condition error and stochastic fluctuation on forecast accuracy in a compartmental model of influenza transmission. These two types of errors are found to have qualitatively similar growth patterns during model integration, indicating that dynamic error growth, regardless of source, is a dominant component of forecast inaccuracy. We therefore examine the nonlinear growth of model initial error and compute the fastest growing directions using singular vector analysis. Using this information, we generate perturbations in an ensemble forecast system of influenza to obtain more optimal ensemble spread. In retrospective forecasts of historical outbreaks for 95 US cities from 2003 to 2014, this approach improves short-term forecast of incidence over the next one to four weeks. Public Library of Science 2019-02-28 /pmc/articles/PMC6394909/ /pubmed/30817754 http://dx.doi.org/10.1371/journal.pcbi.1006783 Text en © 2019 Pei et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Pei, Sen Cane, Mark A. Shaman, Jeffrey Predictability in process-based ensemble forecast of influenza |
title | Predictability in process-based ensemble forecast of influenza |
title_full | Predictability in process-based ensemble forecast of influenza |
title_fullStr | Predictability in process-based ensemble forecast of influenza |
title_full_unstemmed | Predictability in process-based ensemble forecast of influenza |
title_short | Predictability in process-based ensemble forecast of influenza |
title_sort | predictability in process-based ensemble forecast of influenza |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6394909/ https://www.ncbi.nlm.nih.gov/pubmed/30817754 http://dx.doi.org/10.1371/journal.pcbi.1006783 |
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