Forecasting Zika Incidence in the 2016 Latin America Outbreak Combining Traditional Disease Surveillance with Search, Social Media, and News Report Data.
BACKGROUND:Over 400,000 people across the Americas are thought to have been infected with Zika virus as a consequence of the 2015-2016 Latin American outbreak. Official government-led case count data in Latin America are typically delayed by several weeks, making it difficult to track the disease in...
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ftdoajarticles:oai:doaj.org/article:6cee6f45233948dea90d86008259107d 2023-05-15T15:10:25+02:00 Forecasting Zika Incidence in the 2016 Latin America Outbreak Combining Traditional Disease Surveillance with Search, Social Media, and News Report Data. Sarah F McGough John S Brownstein Jared B Hawkins Mauricio Santillana 2017-01-01T00:00:00Z https://doi.org/10.1371/journal.pntd.0005295 https://doaj.org/article/6cee6f45233948dea90d86008259107d EN eng Public Library of Science (PLoS) http://europepmc.org/articles/PMC5268704?pdf=render https://doaj.org/toc/1935-2727 https://doaj.org/toc/1935-2735 1935-2727 1935-2735 doi:10.1371/journal.pntd.0005295 https://doaj.org/article/6cee6f45233948dea90d86008259107d PLoS Neglected Tropical Diseases, Vol 11, Iss 1, p e0005295 (2017) Arctic medicine. Tropical medicine RC955-962 Public aspects of medicine RA1-1270 article 2017 ftdoajarticles https://doi.org/10.1371/journal.pntd.0005295 2022-12-31T04:15:06Z BACKGROUND:Over 400,000 people across the Americas are thought to have been infected with Zika virus as a consequence of the 2015-2016 Latin American outbreak. Official government-led case count data in Latin America are typically delayed by several weeks, making it difficult to track the disease in a timely manner. Thus, timely disease tracking systems are needed to design and assess interventions to mitigate disease transmission. METHODOLOGY/PRINCIPAL FINDINGS:We combined information from Zika-related Google searches, Twitter microblogs, and the HealthMap digital surveillance system with historical Zika suspected case counts to track and predict estimates of suspected weekly Zika cases during the 2015-2016 Latin American outbreak, up to three weeks ahead of the publication of official case data. We evaluated the predictive power of these data and used a dynamic multivariable approach to retrospectively produce predictions of weekly suspected cases for five countries: Colombia, El Salvador, Honduras, Venezuela, and Martinique. Models that combined Google (and Twitter data where available) with autoregressive information showed the best out-of-sample predictive accuracy for 1-week ahead predictions, whereas models that used only Google and Twitter typically performed best for 2- and 3-week ahead predictions. SIGNIFICANCE:Given the significant delay in the release of official government-reported Zika case counts, we show that these Internet-based data streams can be used as timely and complementary ways to assess the dynamics of the outbreak. Article in Journal/Newspaper Arctic Directory of Open Access Journals: DOAJ Articles Arctic PLOS Neglected Tropical Diseases 11 1 e0005295 |
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Directory of Open Access Journals: DOAJ Articles |
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English |
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Arctic medicine. Tropical medicine RC955-962 Public aspects of medicine RA1-1270 |
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Arctic medicine. Tropical medicine RC955-962 Public aspects of medicine RA1-1270 Sarah F McGough John S Brownstein Jared B Hawkins Mauricio Santillana Forecasting Zika Incidence in the 2016 Latin America Outbreak Combining Traditional Disease Surveillance with Search, Social Media, and News Report Data. |
topic_facet |
Arctic medicine. Tropical medicine RC955-962 Public aspects of medicine RA1-1270 |
description |
BACKGROUND:Over 400,000 people across the Americas are thought to have been infected with Zika virus as a consequence of the 2015-2016 Latin American outbreak. Official government-led case count data in Latin America are typically delayed by several weeks, making it difficult to track the disease in a timely manner. Thus, timely disease tracking systems are needed to design and assess interventions to mitigate disease transmission. METHODOLOGY/PRINCIPAL FINDINGS:We combined information from Zika-related Google searches, Twitter microblogs, and the HealthMap digital surveillance system with historical Zika suspected case counts to track and predict estimates of suspected weekly Zika cases during the 2015-2016 Latin American outbreak, up to three weeks ahead of the publication of official case data. We evaluated the predictive power of these data and used a dynamic multivariable approach to retrospectively produce predictions of weekly suspected cases for five countries: Colombia, El Salvador, Honduras, Venezuela, and Martinique. Models that combined Google (and Twitter data where available) with autoregressive information showed the best out-of-sample predictive accuracy for 1-week ahead predictions, whereas models that used only Google and Twitter typically performed best for 2- and 3-week ahead predictions. SIGNIFICANCE:Given the significant delay in the release of official government-reported Zika case counts, we show that these Internet-based data streams can be used as timely and complementary ways to assess the dynamics of the outbreak. |
format |
Article in Journal/Newspaper |
author |
Sarah F McGough John S Brownstein Jared B Hawkins Mauricio Santillana |
author_facet |
Sarah F McGough John S Brownstein Jared B Hawkins Mauricio Santillana |
author_sort |
Sarah F McGough |
title |
Forecasting Zika Incidence in the 2016 Latin America Outbreak Combining Traditional Disease Surveillance with Search, Social Media, and News Report Data. |
title_short |
Forecasting Zika Incidence in the 2016 Latin America Outbreak Combining Traditional Disease Surveillance with Search, Social Media, and News Report Data. |
title_full |
Forecasting Zika Incidence in the 2016 Latin America Outbreak Combining Traditional Disease Surveillance with Search, Social Media, and News Report Data. |
title_fullStr |
Forecasting Zika Incidence in the 2016 Latin America Outbreak Combining Traditional Disease Surveillance with Search, Social Media, and News Report Data. |
title_full_unstemmed |
Forecasting Zika Incidence in the 2016 Latin America Outbreak Combining Traditional Disease Surveillance with Search, Social Media, and News Report Data. |
title_sort |
forecasting zika incidence in the 2016 latin america outbreak combining traditional disease surveillance with search, social media, and news report data. |
publisher |
Public Library of Science (PLoS) |
publishDate |
2017 |
url |
https://doi.org/10.1371/journal.pntd.0005295 https://doaj.org/article/6cee6f45233948dea90d86008259107d |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic |
genre_facet |
Arctic |
op_source |
PLoS Neglected Tropical Diseases, Vol 11, Iss 1, p e0005295 (2017) |
op_relation |
http://europepmc.org/articles/PMC5268704?pdf=render https://doaj.org/toc/1935-2727 https://doaj.org/toc/1935-2735 1935-2727 1935-2735 doi:10.1371/journal.pntd.0005295 https://doaj.org/article/6cee6f45233948dea90d86008259107d |
op_doi |
https://doi.org/10.1371/journal.pntd.0005295 |
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PLOS Neglected Tropical Diseases |
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11 |
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1 |
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e0005295 |
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