Abstract
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.
Highlights
Over the past several years, numerous scientific studies have shown that user interactions with web applications generate latent health-related signals, reflective of individual as well as community level trends[1,2,3,4,5,6,7,8]
The first part of the analysis presents unsupervised models of COVID-19 based on weighted search query frequencies
Queries and their weightings are determined using the first few hundred (FF100) survey on COVID-19 conducted by the National Health Service (NHS)/Public Health England (PHE) in the UK23
Summary
Over the past several years, numerous scientific studies have shown that user interactions with web applications generate latent health-related signals, reflective of individual as well as community level trends[1,2,3,4,5,6,7,8]. The ongoing coronavirus disease (COVID-19) pandemic, caused by a novel coronavirus (SARS-CoV-2), has generated an unprecedented relative volume of online searches (Supplementary Fig. 1). This search behaviour could signal the presence of actual infections, but may be due to general concern that is intensified by news media coverage, reported figures of disease incidence and mortality across the world, and imposed physical distancing measures[20,21]
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