Faster indicators of chikungunya incidence using Google searches.
Chikungunya, a mosquito-borne disease, is a growing threat in Brazil, where over 640,000 cases have been reported since 2017. However, there are often long delays between diagnoses of chikungunya cases and their entry in the national monitoring system, leaving policymakers without the up-to-date cas...
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ftdoajarticles:oai:doaj.org/article:7bed86ce73274b7995b03aa7b38e5c47 2023-05-15T15:03:14+02:00 Faster indicators of chikungunya incidence using Google searches. Sam Miller Tobias Preis Giovanni Mizzi Leonardo Soares Bastos Marcelo Ferreira da Costa Gomes Flávio Codeço Coelho Claudia Torres Codeço Helen Susannah Moat 2022-06-01T00:00:00Z https://doi.org/10.1371/journal.pntd.0010441 https://doaj.org/article/7bed86ce73274b7995b03aa7b38e5c47 EN eng Public Library of Science (PLoS) https://doi.org/10.1371/journal.pntd.0010441 https://doaj.org/toc/1935-2727 https://doaj.org/toc/1935-2735 1935-2727 1935-2735 doi:10.1371/journal.pntd.0010441 https://doaj.org/article/7bed86ce73274b7995b03aa7b38e5c47 PLoS Neglected Tropical Diseases, Vol 16, Iss 6, p e0010441 (2022) Arctic medicine. Tropical medicine RC955-962 Public aspects of medicine RA1-1270 article 2022 ftdoajarticles https://doi.org/10.1371/journal.pntd.0010441 2022-12-31T00:24:31Z Chikungunya, a mosquito-borne disease, is a growing threat in Brazil, where over 640,000 cases have been reported since 2017. However, there are often long delays between diagnoses of chikungunya cases and their entry in the national monitoring system, leaving policymakers without the up-to-date case count statistics they need. In contrast, weekly data on Google searches for chikungunya is available with no delay. Here, we analyse whether Google search data can help improve rapid estimates of chikungunya case counts in Rio de Janeiro, Brazil. We build on a Bayesian approach suitable for data that is subject to long and varied delays, and find that including Google search data reduces both model error and uncertainty. These improvements are largest during epidemics, which are particularly important periods for policymakers. Including Google search data in chikungunya surveillance systems may therefore help policymakers respond to future epidemics more quickly. Article in Journal/Newspaper Arctic Directory of Open Access Journals: DOAJ Articles Arctic PLOS Neglected Tropical Diseases 16 6 e0010441 |
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Open Polar |
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Directory of Open Access Journals: DOAJ Articles |
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ftdoajarticles |
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English |
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Arctic medicine. Tropical medicine RC955-962 Public aspects of medicine RA1-1270 |
spellingShingle |
Arctic medicine. Tropical medicine RC955-962 Public aspects of medicine RA1-1270 Sam Miller Tobias Preis Giovanni Mizzi Leonardo Soares Bastos Marcelo Ferreira da Costa Gomes Flávio Codeço Coelho Claudia Torres Codeço Helen Susannah Moat Faster indicators of chikungunya incidence using Google searches. |
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Arctic medicine. Tropical medicine RC955-962 Public aspects of medicine RA1-1270 |
description |
Chikungunya, a mosquito-borne disease, is a growing threat in Brazil, where over 640,000 cases have been reported since 2017. However, there are often long delays between diagnoses of chikungunya cases and their entry in the national monitoring system, leaving policymakers without the up-to-date case count statistics they need. In contrast, weekly data on Google searches for chikungunya is available with no delay. Here, we analyse whether Google search data can help improve rapid estimates of chikungunya case counts in Rio de Janeiro, Brazil. We build on a Bayesian approach suitable for data that is subject to long and varied delays, and find that including Google search data reduces both model error and uncertainty. These improvements are largest during epidemics, which are particularly important periods for policymakers. Including Google search data in chikungunya surveillance systems may therefore help policymakers respond to future epidemics more quickly. |
format |
Article in Journal/Newspaper |
author |
Sam Miller Tobias Preis Giovanni Mizzi Leonardo Soares Bastos Marcelo Ferreira da Costa Gomes Flávio Codeço Coelho Claudia Torres Codeço Helen Susannah Moat |
author_facet |
Sam Miller Tobias Preis Giovanni Mizzi Leonardo Soares Bastos Marcelo Ferreira da Costa Gomes Flávio Codeço Coelho Claudia Torres Codeço Helen Susannah Moat |
author_sort |
Sam Miller |
title |
Faster indicators of chikungunya incidence using Google searches. |
title_short |
Faster indicators of chikungunya incidence using Google searches. |
title_full |
Faster indicators of chikungunya incidence using Google searches. |
title_fullStr |
Faster indicators of chikungunya incidence using Google searches. |
title_full_unstemmed |
Faster indicators of chikungunya incidence using Google searches. |
title_sort |
faster indicators of chikungunya incidence using google searches. |
publisher |
Public Library of Science (PLoS) |
publishDate |
2022 |
url |
https://doi.org/10.1371/journal.pntd.0010441 https://doaj.org/article/7bed86ce73274b7995b03aa7b38e5c47 |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic |
genre_facet |
Arctic |
op_source |
PLoS Neglected Tropical Diseases, Vol 16, Iss 6, p e0010441 (2022) |
op_relation |
https://doi.org/10.1371/journal.pntd.0010441 https://doaj.org/toc/1935-2727 https://doaj.org/toc/1935-2735 1935-2727 1935-2735 doi:10.1371/journal.pntd.0010441 https://doaj.org/article/7bed86ce73274b7995b03aa7b38e5c47 |
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https://doi.org/10.1371/journal.pntd.0010441 |
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PLOS Neglected Tropical Diseases |
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e0010441 |
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