{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,8]],"date-time":"2026-10-08T01:27:28Z","timestamp":1791422848785,"version":"4.3.3"},"reference-count":0,"publisher":"Cambridge University Press (CUP)","license":[{"start":{"date-parts":[[2025,11,28]],"date-time":"2025-11-28T00:00:00Z","timestamp":1764288000000},"content-version":"unspecified","delay-in-days":331,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["cambridge.org"],"crossmark-restriction":true},"short-container-title":["Epidemiol. Infect."],"published-print":{"date-parts":[[2025]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Google Trends is used in research and surveillance as a proxy for community infection incidence. Signals are difficult to validate, as most surveillance biases towards severe outcomes and certain demographics.<\/jats:p>\n                  <jats:p>Using Winter COVID-19 Infection Study (WCIS) data in England, symptom prevalence is estimated via generalized additive model with multilevel-regression and poststratification. Symptom duration was estimated using interval censored time delay modelling, converting prevalence to incidence. Google Trends and WCIS incidence and growth rates were compared using cross-correlation.<\/jats:p>\n                  <jats:p>Google Trends and WCIS agreement varied by symptom and age group. The national maximum growth rate cross-correlation for sore throat was 0.81, with 90% prediction intervals of [0.69, 0.90]. Google Trends growth rates generally lagged the WCIS growth rates across symptoms (cough: \u22125.0\u00a0days [\u22128.0, 0.0], fever: \u22123.0\u00a0days [\u22126.0, 1.0], loss of smell: \u22129.0\u00a0days [\u221213, \u22123.0], shortness of breath: \u221212\u00a0days [\u221216, \u22125.0], and sore throat: \u22124.0\u00a0days [\u22125.0, \u22122.0]).<\/jats:p>\n                  <jats:p>This work shows Google Trends and community symptom incidence can align, although substantial variation between symptoms and age groups exists, underscoring utility in predicting other surveillance indicators.<\/jats:p>","DOI":"10.1017\/s0950268825100794","type":"journal-article","created":{"date-parts":[[2025,11,28]],"date-time":"2025-11-28T08:17:56Z","timestamp":1764317876000},"update-policy":"https:\/\/doi.org\/10.1017\/policypage","source":"Crossref","is-referenced-by-count":2,"title":["Evaluating Google Trends as a proxy for symptom incidence: insights from the winter COVID-19 infection study in England 2023\/24"],"prefix":"10.1017","volume":"153","author":[{"given":"Phoebe","family":"Asplin","sequence":"first","affiliation":[{"id":[{"id":"https:\/\/ror.org\/00vbvha87","id-type":"ROR","asserted-by":"publisher"}],"name":"UK Health Security Agency"},{"id":[{"id":"https:\/\/ror.org\/01a77tt86","id-type":"ROR","asserted-by":"publisher"}],"name":"University of Warwick"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Martyn","family":"Fyles","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/00vbvha87","id-type":"ROR","asserted-by":"publisher"}],"name":"UK Health Security Agency"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jack","family":"Kennedy","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/00vbvha87","id-type":"ROR","asserted-by":"publisher"}],"name":"UK Health Security Agency"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8801-747X","authenticated-orcid":false,"given":"Thomas","family":"Ward","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/00vbvha87","id-type":"ROR","asserted-by":"publisher"}],"name":"UK Health Security Agency"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8743-0817","authenticated-orcid":false,"given":"Jonathon","family":"Mellor","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/00vbvha87","id-type":"ROR","asserted-by":"publisher"}],"name":"UK Health Security Agency"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"56","published-online":{"date-parts":[[2025,11,28]]},"container-title":["Epidemiology and Infection"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.cambridge.org\/core\/services\/aop-cambridge-core\/content\/view\/S0950268825100794","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,12]],"date-time":"2025-12-12T07:03:56Z","timestamp":1765523036000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.cambridge.org\/core\/product\/identifier\/S0950268825100794\/type\/journal_article"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"references-count":0,"alternative-id":["S0950268825100794"],"URL":"https:\/\/doi.org\/10.1017\/s0950268825100794","relation":{},"ISSN":["0950-2688","1469-4409"],"issn-type":[{"value":"0950-2688","type":"print"},{"value":"1469-4409","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"\u00a9 The Author(s), 2025. Published by Cambridge University Press","name":"copyright","label":"Copyright","group":{"name":"copyright_and_licensing","label":"Copyright and Licensing"}},{"value":"This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http:\/\/creativecommons.org\/licenses\/by\/4.0), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited.","name":"license","label":"License","group":{"name":"copyright_and_licensing","label":"Copyright and Licensing"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}],"article-number":"e136"}}