Faster indicators of chikungunya incidence using Google searches.

• Published in: PLOS Neglected Tropical Diseases
• Main Authors: Sam Miller, Tobias Preis, Giovanni Mizzi, Leonardo Soares Bastos, Marcelo Ferreira da Costa Gomes, Flávio Codeço Coelho, Claudia Torres Codeço, Helen Susannah Moat
• Format: Article in Journal/Newspaper
• Language: English
• Published: Public Library of Science (PLoS) 2022
• Subjects: Arctic medicine. Tropical medicine; RC955-962; Public aspects of medicine; RA1-1270; Arctic
• Online Access: https://doi.org/10.1371/journal.pntd.0010441; https://doaj.org/article/7bed86ce73274b7995b03aa7b38e5c47

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.