Tracking COVID-19 using online search

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 unsupe...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Vasileios Lampos, Maimuna S. Majumder, Elad Yom-Tov, Michael Edelstein, Simon Moura, Yohhei Hamada, Molebogeng X. Rangaka, Rachel A. McKendry, Ingemar J. Cox
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Computer applications to medicine. Medical informatics R858-859.7
Acceso en línea:	https://doaj.org/article/2aa635b5cb99459ca53411cd1aea79db
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:2aa635b5cb99459ca53411cd1aea79db
record_format	dspace
spelling	oai:doaj.org-article:2aa635b5cb99459ca53411cd1aea79db2021-12-02T13:50:57ZTracking COVID-19 using online search10.1038/s41746-021-00384-w2398-6352https://doaj.org/article/2aa635b5cb99459ca53411cd1aea79db2021-02-01T00:00:00Zhttps://doi.org/10.1038/s41746-021-00384-whttps://doaj.org/toc/2398-6352Abstract 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.Vasileios LamposMaimuna S. MajumderElad Yom-TovMichael EdelsteinSimon MouraYohhei HamadaMolebogeng X. RangakaRachel A. McKendryIngemar J. CoxNature PortfolioarticleComputer applications to medicine. Medical informaticsR858-859.7ENnpj Digital Medicine, Vol 4, Iss 1, Pp 1-11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Computer applications to medicine. Medical informatics R858-859.7
spellingShingle	Computer applications to medicine. Medical informatics R858-859.7 Vasileios Lampos Maimuna S. Majumder Elad Yom-Tov Michael Edelstein Simon Moura Yohhei Hamada Molebogeng X. Rangaka Rachel A. McKendry Ingemar J. Cox Tracking COVID-19 using online search
description	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.
format	article
author	Vasileios Lampos Maimuna S. Majumder Elad Yom-Tov Michael Edelstein Simon Moura Yohhei Hamada Molebogeng X. Rangaka Rachel A. McKendry Ingemar J. Cox
author_facet	Vasileios Lampos Maimuna S. Majumder Elad Yom-Tov Michael Edelstein Simon Moura Yohhei Hamada Molebogeng X. Rangaka Rachel A. McKendry Ingemar J. Cox
author_sort	Vasileios Lampos
title	Tracking COVID-19 using online search
title_short	Tracking COVID-19 using online search
title_full	Tracking COVID-19 using online search
title_fullStr	Tracking COVID-19 using online search
title_full_unstemmed	Tracking COVID-19 using online search
title_sort	tracking covid-19 using online search
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/2aa635b5cb99459ca53411cd1aea79db
work_keys_str_mv	AT vasileioslampos trackingcovid19usingonlinesearch AT maimunasmajumder trackingcovid19usingonlinesearch AT eladyomtov trackingcovid19usingonlinesearch AT michaeledelstein trackingcovid19usingonlinesearch AT simonmoura trackingcovid19usingonlinesearch AT yohheihamada trackingcovid19usingonlinesearch AT molebogengxrangaka trackingcovid19usingonlinesearch AT rachelamckendry trackingcovid19usingonlinesearch AT ingemarjcox trackingcovid19usingonlinesearch
_version_	1718392428696698880

Tracking COVID-19 using online search

Ejemplares similares