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Tracking COVID-19 using online search.
Lampos, Vasileios; Majumder, Maimuna S; Yom-Tov, Elad; Edelstein, Michael; Moura, Simon; Hamada, Yohhei; Rangaka, Molebogeng X; McKendry, Rachel A; Cox, Ingemar J.
  • Lampos V; Department of Computer Science, University College London, London, UK. v.lampos@ucl.ac.uk.
  • Majumder MS; Computational Health Informatics Program, Boston Children's Hospital, Boston, MA, USA.
  • Yom-Tov E; Department of Pediatrics, Harvard Medical School, Boston, MA, USA.
  • Edelstein M; Microsoft Research, Herzeliya, Israel.
  • Moura S; National Infection Service, Public Health England, London, UK.
  • Hamada Y; Department of Population Health, Faculty of Medicine, Bar-Ilan University, Safed, Israel.
  • Rangaka MX; Department of Computer Science, University College London, London, UK.
  • McKendry RA; Institute for Global Health, University College London, London, UK.
  • Cox IJ; Institute for Global Health, University College London, London, UK.
NPJ Digit Med ; 4(1): 17, 2021 Feb 08.
Article in English | MEDLINE | ID: covidwho-1072176
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.

Full text: Available Collection: International databases Database: MEDLINE Type of study: Prognostic study / Risk factors Language: English Journal: NPJ Digit Med Year: 2021 Document Type: Article Affiliation country: S41746-021-00384-w

Full text: Available Collection: International databases Database: MEDLINE Type of study: Prognostic study / Risk factors Language: English Journal: NPJ Digit Med Year: 2021 Document Type: Article Affiliation country: S41746-021-00384-w