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Google Health Trends performance reflecting dengue incidence for the Brazilian states

BACKGROUND: Dengue fever is a mosquito-borne infection transmitted by Aedes aegypti and mainly found in tropical and subtropical regions worldwide. Since its re-introduction in 1986, Brazil has become a hotspot for dengue and has experienced yearly epidemics. As a notifiable infectious disease, Braz...

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Autores principales: Romero-Alvarez, Daniel, Parikh, Nidhi, Osthus, Dave, Martinez, Kaitlyn, Generous, Nicholas, del Valle, Sara, Manore, Carrie A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7104526/
https://www.ncbi.nlm.nih.gov/pubmed/32228508
http://dx.doi.org/10.1186/s12879-020-04957-0
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author Romero-Alvarez, Daniel
Parikh, Nidhi
Osthus, Dave
Martinez, Kaitlyn
Generous, Nicholas
del Valle, Sara
Manore, Carrie A.
author_facet Romero-Alvarez, Daniel
Parikh, Nidhi
Osthus, Dave
Martinez, Kaitlyn
Generous, Nicholas
del Valle, Sara
Manore, Carrie A.
author_sort Romero-Alvarez, Daniel
collection PubMed
description BACKGROUND: Dengue fever is a mosquito-borne infection transmitted by Aedes aegypti and mainly found in tropical and subtropical regions worldwide. Since its re-introduction in 1986, Brazil has become a hotspot for dengue and has experienced yearly epidemics. As a notifiable infectious disease, Brazil uses a passive epidemiological surveillance system to collect and report cases; however, dengue burden is underestimated. Thus, Internet data streams may complement surveillance activities by providing real-time information in the face of reporting lags. METHODS: We analyzed 19 terms related to dengue using Google Health Trends (GHT), a free-Internet data-source, and compared it with weekly dengue incidence between 2011 to 2016. We correlated GHT data with dengue incidence at the national and state-level for Brazil while using the adjusted R squared statistic as primary outcome measure (0/1). We used survey data on Internet access and variables from the official census of 2010 to identify where GHT could be useful in tracking dengue dynamics. Finally, we used a standardized volatility index on dengue incidence and developed models with different variables with the same objective. RESULTS: From the 19 terms explored with GHT, only seven were able to consistently track dengue. From the 27 states, only 12 reported an adjusted R squared higher than 0.8; these states were distributed mainly in the Northeast, Southeast, and South of Brazil. The usefulness of GHT was explained by the logarithm of the number of Internet users in the last 3 months, the total population per state, and the standardized volatility index. CONCLUSIONS: The potential contribution of GHT in complementing traditional established surveillance strategies should be analyzed in the context of geographical resolutions smaller than countries. For Brazil, GHT implementation should be analyzed in a case-by-case basis. State variables including total population, Internet usage in the last 3 months, and the standardized volatility index could serve as indicators determining when GHT could complement dengue state level surveillance in other countries.
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spelling pubmed-71045262020-03-31 Google Health Trends performance reflecting dengue incidence for the Brazilian states Romero-Alvarez, Daniel Parikh, Nidhi Osthus, Dave Martinez, Kaitlyn Generous, Nicholas del Valle, Sara Manore, Carrie A. BMC Infect Dis Research Article BACKGROUND: Dengue fever is a mosquito-borne infection transmitted by Aedes aegypti and mainly found in tropical and subtropical regions worldwide. Since its re-introduction in 1986, Brazil has become a hotspot for dengue and has experienced yearly epidemics. As a notifiable infectious disease, Brazil uses a passive epidemiological surveillance system to collect and report cases; however, dengue burden is underestimated. Thus, Internet data streams may complement surveillance activities by providing real-time information in the face of reporting lags. METHODS: We analyzed 19 terms related to dengue using Google Health Trends (GHT), a free-Internet data-source, and compared it with weekly dengue incidence between 2011 to 2016. We correlated GHT data with dengue incidence at the national and state-level for Brazil while using the adjusted R squared statistic as primary outcome measure (0/1). We used survey data on Internet access and variables from the official census of 2010 to identify where GHT could be useful in tracking dengue dynamics. Finally, we used a standardized volatility index on dengue incidence and developed models with different variables with the same objective. RESULTS: From the 19 terms explored with GHT, only seven were able to consistently track dengue. From the 27 states, only 12 reported an adjusted R squared higher than 0.8; these states were distributed mainly in the Northeast, Southeast, and South of Brazil. The usefulness of GHT was explained by the logarithm of the number of Internet users in the last 3 months, the total population per state, and the standardized volatility index. CONCLUSIONS: The potential contribution of GHT in complementing traditional established surveillance strategies should be analyzed in the context of geographical resolutions smaller than countries. For Brazil, GHT implementation should be analyzed in a case-by-case basis. State variables including total population, Internet usage in the last 3 months, and the standardized volatility index could serve as indicators determining when GHT could complement dengue state level surveillance in other countries. BioMed Central 2020-03-26 /pmc/articles/PMC7104526/ /pubmed/32228508 http://dx.doi.org/10.1186/s12879-020-04957-0 Text en © The Author(s) 2020 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research Article
Romero-Alvarez, Daniel
Parikh, Nidhi
Osthus, Dave
Martinez, Kaitlyn
Generous, Nicholas
del Valle, Sara
Manore, Carrie A.
Google Health Trends performance reflecting dengue incidence for the Brazilian states
title Google Health Trends performance reflecting dengue incidence for the Brazilian states
title_full Google Health Trends performance reflecting dengue incidence for the Brazilian states
title_fullStr Google Health Trends performance reflecting dengue incidence for the Brazilian states
title_full_unstemmed Google Health Trends performance reflecting dengue incidence for the Brazilian states
title_short Google Health Trends performance reflecting dengue incidence for the Brazilian states
title_sort google health trends performance reflecting dengue incidence for the brazilian states
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7104526/
https://www.ncbi.nlm.nih.gov/pubmed/32228508
http://dx.doi.org/10.1186/s12879-020-04957-0
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