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Faster indicators of chikungunya incidence using Google searches
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Miller, Sam, Preis, Tobias, Mizzi, Giovanni, Bastos, Leonardo Soares, Gomes, Marcelo Ferreira da Costa, Coelho, Flávio Codeço, Codeço, Claudia Torres and Moat, Helen Susannah (2022) Faster indicators of chikungunya incidence using Google searches. PLOS Neglected Tropical Diseases, 16 (6). e0010441. doi:10.1371/journal.pntd.0010441 ISSN 1935-2735.
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WRAP-Faster-indicators-chikungunya-incidence-Google-searches-2022.pdf - Published Version - Requires a PDF viewer. Available under License Creative Commons Attribution 4.0. Download (1677Kb) | Preview |
Official URL: https://doi.org/10.1371/journal.pntd.0010441
Abstract
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.
| Item Type: | Journal Article | |||||||||||||||||||||||||||||||||
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| Subjects: | Q Science > QA Mathematics R Medicine > RC Internal medicine T Technology > TK Electrical engineering. Electronics Nuclear engineering |
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| Divisions: | Faculty of Social Sciences > Warwick Business School | |||||||||||||||||||||||||||||||||
| SWORD Depositor: | Library Publications Router | |||||||||||||||||||||||||||||||||
| Library of Congress Subject Headings (LCSH): | Chikungunya, Google, Web search engines, Internet searching, Data mining -- Health aspects, Bayesian statistical decision theory | |||||||||||||||||||||||||||||||||
| Journal or Publication Title: | PLOS Neglected Tropical Diseases | |||||||||||||||||||||||||||||||||
| Publisher: | Public Library of Science | |||||||||||||||||||||||||||||||||
| ISSN: | 1935-2735 | |||||||||||||||||||||||||||||||||
| Official Date: | 9 June 2022 | |||||||||||||||||||||||||||||||||
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| Volume: | 16 | |||||||||||||||||||||||||||||||||
| Number: | 6 | |||||||||||||||||||||||||||||||||
| Article Number: | e0010441 | |||||||||||||||||||||||||||||||||
| DOI: | 10.1371/journal.pntd.0010441 | |||||||||||||||||||||||||||||||||
| Status: | Peer Reviewed | |||||||||||||||||||||||||||||||||
| Publication Status: | Published | |||||||||||||||||||||||||||||||||
| Access rights to Published version: | Open Access (Creative Commons) | |||||||||||||||||||||||||||||||||
| Date of first compliant deposit: | 11 July 2022 | |||||||||||||||||||||||||||||||||
| Date of first compliant Open Access: | 11 July 2022 | |||||||||||||||||||||||||||||||||
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