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Faster indicators of chikungunya incidence using Google searches

Author

Listed:
  • Sam Miller
  • Tobias Preis
  • Giovanni Mizzi
  • Leonardo Soares Bastos
  • Marcelo Ferreira da Costa Gomes
  • Flávio Codeço Coelho
  • Claudia Torres Codeço
  • Helen Susannah Moat

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.Author summary: To respond quickly to disease outbreaks, policymakers need rapid data on the number of new infections. However, for many diseases, such data is very delayed, due to the administrative work required to record each case in a disease surveillance system. This is a problem for data on chikungunya, a mosquito-borne disease which is a growing threat in Brazil. In Rio de Janeiro, delays in chikungunya cases being recorded average four weeks. These delays are sometimes longer and sometimes shorter. In stark contrast to chikungunya data, data on what people are searching for on Google is available almost immediately. People suffering from chikungunya might search on Google for information about the disease. Here, we investigate whether rapidly available Google data can help generate quick estimates of the number of chikungunya cases in Rio de Janeiro in the previous week. Our model uses a Bayesian methodology to help account for the varying delays in the chikungunya data. We show that including Google search data in the model reduces both the error and uncertainty of the chikungunya case count estimates, in particular during epidemics. Our method could be used to help policymakers to respond more quickly to future chikungunya epidemics.

Suggested Citation

  • Sam Miller & Tobias Preis & Giovanni Mizzi & Leonardo Soares Bastos & Marcelo Ferreira da Costa Gomes & Flávio Codeço Coelho & Claudia Torres Codeço & Helen Susannah Moat, 2022. "Faster indicators of chikungunya incidence using Google searches," PLOS Neglected Tropical Diseases, Public Library of Science, vol. 16(6), pages 1-16, June.
    • Handle: RePEc:plo:pntd00:0010441
    DOI: 10.1371/journal.pntd.0010441
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    References listed on IDEAS

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    1. Hyunyoung Choi & Hal Varian, 2012. "Predicting the Present with Google Trends," The Economic Record, The Economic Society of Australia, vol. 88(s1), pages 2-9, June.
    2. Håvard Rue & Sara Martino & Nicolas Chopin, 2009. "Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 71(2), pages 319-392, April.
    3. Cecilia de Almeida Marques-Toledo & Carolin Marlen Degener & Livia Vinhal & Giovanini Coelho & Wagner Meira & Claudia Torres Codeço & Mauro Martins Teixeira, 2017. "Dengue prediction by the web: Tweets are a useful tool for estimating and forecasting Dengue at country and city level," PLOS Neglected Tropical Diseases, Public Library of Science, vol. 11(7), pages 1-20, July.
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