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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...

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Detalles Bibliográficos
Autores principales: Pei, Sen, Cane, Mark A., Shaman, Jeffrey
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2019
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.
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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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