Tracking COVID-19 using online search
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- Tracking COVID-19 using online search
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Previous research has demonstrated that various properties of infectious diseases can be inferred from online search behaviour. In this work we use time series of online search query frequencies to gain insights about the prevalence of COVID-19 in multiple countries. We first develop unsupervised modelling techniques based on associated symptom categories identified by the United Kingdom’s National Health Service and Public Health England. We then attempt to minimise an expected bias in these signals caused by public interest—as opposed to infections—using the proportion of news media coverage devoted to COVID-19 as a proxy indicator. Our analysis indicates that models based on online searches precede the reported confirmed cases and deaths by 16.7 (10.2–23.2) and 22.1 (17.4–26.9) days, respectively. We also investigate transfer learning techniques for mapping supervised models from countries where the spread of the disease has progressed extensively to countries that are in earlier phases of their respective epidemic curves. Furthermore, we compare time series of online search activity against confirmed COVID-19 cases or deaths jointly across multiple countries, uncovering interesting querying patterns, including the finding that rarer symptoms are better predictors than common ones. Finally, we show that web searches improve the short-term forecasting accuracy of autoregressive models for COVID-19 deaths. Our work provides evidence that online search data can be used to develop complementary public health surveillance methods to help inform the COVID-19 response in conjunction with more established approaches.
|Tidsskrift||npj Digital Medicine|
|Status||Udgivet - 2021|
V.L., S.M., R.A.M., and I.J.C would like to acknowledge all levels of support from the EPSRC projects “EPSRC IRC in Early-Warning Sensing Systems for Infectious Diseases” (EP/K031953/1), “i-sense: EPSRC IRC in Agile Early Warning Sensing Systems for Infectious Diseases and Antimicrobial Resistance” and its COVID-19 plus award "EPSRC i-sense COVID-19: Harnessing digital and diagnostic technologies for COVID-19” (EP/R00529X/1). V.L. and I.J.C. would also like to acknowledge the support from the MRC/NIHR project “COVID-19 Virus Watch: Understanding community incidence, symptom profiles, and transmission of COVID-19 in relation to population movement and behaviour” (MC_PC_19070) as well as from a Google donation funding the project “Modelling the prevalence and understanding the impact of COVID-19 using web search data”. The authors would like to thank the NHS/PHE FF100 team for sharing early results, and the RCGP for sharing swabbing data for COVID-19. We also appreciate the contribution of Ettore Severi, Anna Odone, and Daniela Paolotti in the translation of search queries from English to Italian. Finally, V.L. would like to thank Sam J. Gilbert for interesting discussions and pointers during the development of this work.
© 2021, The Author(s).