Providing early indication of regional anomalies in COVID-19 case counts in England using search engine queries

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Prior work has shown the utility of using Internet searches to track the incidence of different respiratory illnesses. Similarly, people who suffer from COVID-19 may query for their symptoms prior to accessing the medical system (or in lieu of it). To assist in the UK government’s response to the COVID-19 pandemic we analyzed searches for relevant symptoms on the Bing web search engine from users in England to identify areas of the country where unexpected rises in relevant symptom searches occurred. These were reported weekly to the UK Health Security Agency to assist in their monitoring of the pandemic. Our analysis shows that searches for “fever” and “cough” were the most correlated with future case counts during the initial stages of the pandemic, with searches preceding case counts by up to 21 days. Unexpected rises in search patterns were predictive of anomalous rises in future case counts within a week, reaching an Area Under Curve of 0.82 during the initial phase of the pandemic, and later reducing due to changes in symptom presentation. Thus, analysis of regional searches for symptoms can provide an early indicator (of more than one week) of increases in COVID-19 case counts.

TidsskriftScientific Reports
Udgave nummer1
Sider (fra-til)1-10
StatusUdgivet - 2022

Bibliografisk note

Funding Information:
IJC and VL would like to acknowledge all levels of support from the EPSRC projects “EPSRC IRC in Early-Warning Sensing Systems for Infectious Diseases” (EP/K031953/1), “i-sense: EPSRC IRC in Agile Early Warning Sensing Systems for Infectious Diseases and Antimicrobial Resistance” and its COVID-19 plus award “EPSRC i-sense COVID-19: Harnessing digital and diagnostic technologies for COVID-19” (EP/R00529X/1). IJC and VL would also like to acknowledge the support from the MRC/NIHR project “COVID-19 Virus Watch: Understanding community incidence, symptom profiles, and transmission of COVID-19 in relation to population movement and behaviour” (MC_PC_19070) as well as from a Google donation funding the project “Modelling the prevalence and understanding the impact of COVID-19 using web search data”.

Publisher Copyright:
© 2022, The Author(s).

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