However, for most locations, if not all, no sufficient data-in terms of validity, representativeness, and time span-currently exists to apply supervised learning approaches to COVID-19. Typically, this training data spans multiple years and flu seasons 13, 14, 15. the frequency of web searches over time, to a noisy representation of a disease rate time series, indirectly minimising the impact of online activity that is not caused by infection.
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These models learn a function that maps time series of online user-generated data, e.g. Previous research, which has used online search to predominantly model influenza-like illness (ILI) rates, has focused on supervised learning solutions, where “ground truth” information, in the form of historical syndromic surveillance reports, can be used to train machine learning models 2, 5, 13, 14, 22. This search behaviour could signal the presence of actual infections, but may also 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. Notably, 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. Furthermore, during emerging epidemics they may offer community level insights that current monitoring systems are not equipped to obtain given a limited testing capacity 18, and the physical distancing measures that discourage or prohibit people from interacting with health services 19.
Compared to conventional health surveillance systems, online user trails exhibit certain advantages, including low latency, continuous operational capacity, denser spatial coverage, and broader demographic inclusion 8, 17. After an extensive scientific debate about their usefulness and accuracy 11, 12, and with the manifestation of new findings that improved upon past methodological shortcomings 13, 14, 15, these techniques are now becoming part of public health systems, serving as complementary endpoints for monitoring the prevalence of infectious diseases, such as seasonal influenza 8, 16. Seasonal influenza and the H1N1 pandemic were used as case studies for the development and evaluation of machine learning models that produce disease rate estimates in a non-traditional way, using online search or social media as their input information 1, 2, 5, 9, 10. 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. 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. Finally, we show that web searches improve the short-term forecasting accuracy of autoregressive models for COVID-19 deaths. 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. 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. 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 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. We first develop unsupervised modelling techniques based on associated symptom categories identified by the United Kingdom’s National Health Service and Public Health England. In this work we use time series of online search query frequencies to gain insights about the prevalence of COVID-19 in multiple countries. Previous research has demonstrated that various properties of infectious diseases can be inferred from online search behaviour.
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