Influenza forecasting with Google Flu Trends.

<h4>Background</h4>We developed a practical influenza forecast model based on real-time, geographically focused, and easy to access data, designed to provide individual medical centers with advanced warning of the expected number of influenza cases, thus allowing for sufficient time to i...

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Autores principales: Andrea Freyer Dugas, Mehdi Jalalpour, Yulia Gel, Scott Levin, Fred Torcaso, Takeru Igusa, Richard E Rothman
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Publicado: Public Library of Science (PLoS) 2013
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spelling oai:doaj.org-article:60e802b3002640dbb3cac9ec1b6328222021-11-18T07:57:28ZInfluenza forecasting with Google Flu Trends.1932-620310.1371/journal.pone.0056176https://doaj.org/article/60e802b3002640dbb3cac9ec1b6328222013-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/23457520/pdf/?tool=EBIhttps://doaj.org/toc/1932-6203<h4>Background</h4>We developed a practical influenza forecast model based on real-time, geographically focused, and easy to access data, designed to provide individual medical centers with advanced warning of the expected number of influenza cases, thus allowing for sufficient time to implement interventions. Secondly, we evaluated the effects of incorporating a real-time influenza surveillance system, Google Flu Trends, and meteorological and temporal information on forecast accuracy.<h4>Methods</h4>Forecast models designed to predict one week in advance were developed from weekly counts of confirmed influenza cases over seven seasons (2004-2011) divided into seven training and out-of-sample verification sets. Forecasting procedures using classical Box-Jenkins, generalized linear models (GLM), and generalized linear autoregressive moving average (GARMA) methods were employed to develop the final model and assess the relative contribution of external variables such as, Google Flu Trends, meteorological data, and temporal information.<h4>Results</h4>A GARMA(3,0) forecast model with Negative Binomial distribution integrating Google Flu Trends information provided the most accurate influenza case predictions. The model, on the average, predicts weekly influenza cases during 7 out-of-sample outbreaks within 7 cases for 83% of estimates. Google Flu Trend data was the only source of external information to provide statistically significant forecast improvements over the base model in four of the seven out-of-sample verification sets. Overall, the p-value of adding this external information to the model is 0.0005. The other exogenous variables did not yield a statistically significant improvement in any of the verification sets.<h4>Conclusions</h4>Integer-valued autoregression of influenza cases provides a strong base forecast model, which is enhanced by the addition of Google Flu Trends confirming the predictive capabilities of search query based syndromic surveillance. This accessible and flexible forecast model can be used by individual medical centers to provide advanced warning of future influenza cases.Andrea Freyer DugasMehdi JalalpourYulia GelScott LevinFred TorcasoTakeru IgusaRichard E RothmanPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 8, Iss 2, p e56176 (2013)
institution DOAJ
collection DOAJ
language EN
topic Medicine
R
Science
Q
spellingShingle Medicine
R
Science
Q
Andrea Freyer Dugas
Mehdi Jalalpour
Yulia Gel
Scott Levin
Fred Torcaso
Takeru Igusa
Richard E Rothman
Influenza forecasting with Google Flu Trends.
description <h4>Background</h4>We developed a practical influenza forecast model based on real-time, geographically focused, and easy to access data, designed to provide individual medical centers with advanced warning of the expected number of influenza cases, thus allowing for sufficient time to implement interventions. Secondly, we evaluated the effects of incorporating a real-time influenza surveillance system, Google Flu Trends, and meteorological and temporal information on forecast accuracy.<h4>Methods</h4>Forecast models designed to predict one week in advance were developed from weekly counts of confirmed influenza cases over seven seasons (2004-2011) divided into seven training and out-of-sample verification sets. Forecasting procedures using classical Box-Jenkins, generalized linear models (GLM), and generalized linear autoregressive moving average (GARMA) methods were employed to develop the final model and assess the relative contribution of external variables such as, Google Flu Trends, meteorological data, and temporal information.<h4>Results</h4>A GARMA(3,0) forecast model with Negative Binomial distribution integrating Google Flu Trends information provided the most accurate influenza case predictions. The model, on the average, predicts weekly influenza cases during 7 out-of-sample outbreaks within 7 cases for 83% of estimates. Google Flu Trend data was the only source of external information to provide statistically significant forecast improvements over the base model in four of the seven out-of-sample verification sets. Overall, the p-value of adding this external information to the model is 0.0005. The other exogenous variables did not yield a statistically significant improvement in any of the verification sets.<h4>Conclusions</h4>Integer-valued autoregression of influenza cases provides a strong base forecast model, which is enhanced by the addition of Google Flu Trends confirming the predictive capabilities of search query based syndromic surveillance. This accessible and flexible forecast model can be used by individual medical centers to provide advanced warning of future influenza cases.
format article
author Andrea Freyer Dugas
Mehdi Jalalpour
Yulia Gel
Scott Levin
Fred Torcaso
Takeru Igusa
Richard E Rothman
author_facet Andrea Freyer Dugas
Mehdi Jalalpour
Yulia Gel
Scott Levin
Fred Torcaso
Takeru Igusa
Richard E Rothman
author_sort Andrea Freyer Dugas
title Influenza forecasting with Google Flu Trends.
title_short Influenza forecasting with Google Flu Trends.
title_full Influenza forecasting with Google Flu Trends.
title_fullStr Influenza forecasting with Google Flu Trends.
title_full_unstemmed Influenza forecasting with Google Flu Trends.
title_sort influenza forecasting with google flu trends.
publisher Public Library of Science (PLoS)
publishDate 2013
url https://doaj.org/article/60e802b3002640dbb3cac9ec1b632822
work_keys_str_mv AT andreafreyerdugas influenzaforecastingwithgoogleflutrends
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AT scottlevin influenzaforecastingwithgoogleflutrends
AT fredtorcaso influenzaforecastingwithgoogleflutrends
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