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Forecasting Peaks of Seasonal Influenza Epidemics

We present a framework for near real-time forecast of influenza epidemics using a simulation optimization approach. The method combines an individual-based model and a simple root finding optimization method for parameter estimation and forecasting. In this study, retrospective forecasts were genera...

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Detalles Bibliográficos
Autores principales: Nsoesie, Elaine, Mararthe, Madhav, Brownstein, John
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3712489/
https://www.ncbi.nlm.nih.gov/pubmed/23873050
http://dx.doi.org/10.1371/currents.outbreaks.bb1e879a23137022ea79a8c508b030bc
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author Nsoesie, Elaine
Mararthe, Madhav
Brownstein, John
author_facet Nsoesie, Elaine
Mararthe, Madhav
Brownstein, John
author_sort Nsoesie, Elaine
collection PubMed
description We present a framework for near real-time forecast of influenza epidemics using a simulation optimization approach. The method combines an individual-based model and a simple root finding optimization method for parameter estimation and forecasting. In this study, retrospective forecasts were generated for seasonal influenza epidemics using web-based estimates of influenza activity from Google Flu Trends for 2004-2005, 2007-2008 and 2012-2013 flu seasons. In some cases, the peak could be forecasted 5-6 weeks ahead. This study adds to existing resources for influenza forecasting and the proposed method can be used in conjunction with other approaches in an ensemble framework.
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spelling pubmed-37124892013-07-19 Forecasting Peaks of Seasonal Influenza Epidemics Nsoesie, Elaine Mararthe, Madhav Brownstein, John PLoS Curr Research We present a framework for near real-time forecast of influenza epidemics using a simulation optimization approach. The method combines an individual-based model and a simple root finding optimization method for parameter estimation and forecasting. In this study, retrospective forecasts were generated for seasonal influenza epidemics using web-based estimates of influenza activity from Google Flu Trends for 2004-2005, 2007-2008 and 2012-2013 flu seasons. In some cases, the peak could be forecasted 5-6 weeks ahead. This study adds to existing resources for influenza forecasting and the proposed method can be used in conjunction with other approaches in an ensemble framework. Public Library of Science 2013-06-21 /pmc/articles/PMC3712489/ /pubmed/23873050 http://dx.doi.org/10.1371/currents.outbreaks.bb1e879a23137022ea79a8c508b030bc Text en http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research
Nsoesie, Elaine
Mararthe, Madhav
Brownstein, John
Forecasting Peaks of Seasonal Influenza Epidemics
title Forecasting Peaks of Seasonal Influenza Epidemics
title_full Forecasting Peaks of Seasonal Influenza Epidemics
title_fullStr Forecasting Peaks of Seasonal Influenza Epidemics
title_full_unstemmed Forecasting Peaks of Seasonal Influenza Epidemics
title_short Forecasting Peaks of Seasonal Influenza Epidemics
title_sort forecasting peaks of seasonal influenza epidemics
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3712489/
https://www.ncbi.nlm.nih.gov/pubmed/23873050
http://dx.doi.org/10.1371/currents.outbreaks.bb1e879a23137022ea79a8c508b030bc
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