Abstract
We propose a method to estimate the household secondary attack rate (hSAR) of COVID-19 in the United Kingdom based on activity on the social media platform X, formerly known as Twitter. Conventional methods of hSAR estimation are resource intensive, requiring regular contact tracing of COVID-19 cases. Our proposed framework provides a complementary method that does not rely on conventional contact tracing or laboratory involvement, including the collection, processing, and analysis of biological samples. We use a text classifier to identify reports of people tweeting about themselves and/or members of their household having COVID-19 infections. A probabilistic analysis is then performed to estimate the hSAR based on the number of self or household, and self and household tweets of COVID-19 infection. The analysis includes adjustments for a reluctance of Twitter users to tweet about household members, and the possibility that the secondary infection was not acquired within the household. Experimental results for the UK, both monthly and weekly, are reported for the period from January 2020 to February 2022. Our results agree with previously reported hSAR estimates, varying with the primary variants of concern, e.g. delta and omicron. The serial interval (SI) is based on the time between the two tweets that indicate a primary and secondary infection. Experimental results, though larger than the consensus, are qualitatively similar. The estimation of hSAR and SI using social media data constitutes a new tool that may help in characterizing, forecasting and managing outbreaks and pandemics in a faster, affordable, and more efficient manner.
Originalsprog | Engelsk |
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Artikelnummer | 194 |
Tidsskrift | npj Digital Medicine |
Vol/bind | 7 |
Udgave nummer | 1 |
Antal sider | 10 |
DOI | |
Status | Udgivet - dec. 2024 |
Bibliografisk note
Funding Information:We would like to acknowledge all levels of support from the EPSRC projects \u201CEPSRC IRC in Early-Warning Sensing Systems for Infectious Diseases\u201D (EP/K031953/1), \u201Ci-sense: EPSRC IRC in Agile Early Warning Sensing Systems for Infectious Diseases and Antimicrobial Resistance\u201D and its COVID-19 plus award \u201CEPSRC i-sense COVID-19: Harnessing digital and diagnostic technologies for COVID-19\u201D (EP/R00529X/1). We also acknowledge a financial gift from Google in support of our work on COVID-19. We would also like to acknowledge the support from the MRC/NIHR project \u201CVirus Watch: Understanding community incidence, symptom profiles, and transmission of COVID-19 in relation to population movement and behaviour\u201D (MC_PC_19070). We also acknowledge the assistance of Tomasz Czernuszenko. L.P. gratefully acknowledges the Wellcome Trust and Royal Society Sir Henry Dale Fellowship (202562/Z/16/Z). L.P. and T.H. are also supported by Wellcome Trust Discovery Award \u201CHarnessing epidemiological and genomic data for understanding of respiratory virus transmission at multiple scales\u201D (227438/Z/23/Z), the UKRI Impact Acceleration Award (IAA 386) and the JUNIPER modelling consortium (MR/V038613/1).
Publisher Copyright:
© Crown 2024.