Dr Aarzoo Dhiman A.Dhiman@hull.ac.uk
Teaching Fellow
Estimating the household secondary attack rate and serial interval of COVID-19 using social media
Dhiman, Aarzoo; Yom-Tov, Elad; Pellis, Lorenzo; Edelstein, Michael; Pebody, Richard; Hayward, Andrew; House, Thomas; Finnie, Thomas; Guzman, David; Lampos, Vasileios; Virus Watch Consortium; Cox, Ingemar J.
Authors
Elad Yom-Tov
Lorenzo Pellis
Michael Edelstein
Richard Pebody
Andrew Hayward
Thomas House
Thomas Finnie
David Guzman
Vasileios Lampos
Virus Watch Consortium
Ingemar J. Cox
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.
Citation
Dhiman, A., Yom-Tov, E., Pellis, L., Edelstein, M., Pebody, R., Hayward, A., House, T., Finnie, T., Guzman, D., Lampos, V., Virus Watch Consortium, & Cox, I. J. (2024). Estimating the household secondary attack rate and serial interval of COVID-19 using social media. npj Digital Medicine, 7(1), Article 194. https://doi.org/10.1038/s41746-024-01160-2
Journal Article Type | Article |
---|---|
Acceptance Date | Jun 10, 2024 |
Online Publication Date | Jul 20, 2024 |
Publication Date | Jul 20, 2024 |
Deposit Date | Aug 7, 2024 |
Publicly Available Date | Aug 8, 2024 |
Journal | npj Digital Medicine |
Print ISSN | 2398-6352 |
Electronic ISSN | 2398-6352 |
Publisher | Nature Research |
Peer Reviewed | Peer Reviewed |
Volume | 7 |
Issue | 1 |
Article Number | 194 |
DOI | https://doi.org/10.1038/s41746-024-01160-2 |
Keywords | Risk factors; Viral infection |
Public URL | https://hull-repository.worktribe.com/output/4785390 |
Files
Published article
(1.2 Mb)
PDF
Publisher Licence URL
http://creativecommons.org/licenses/by/4.0
Copyright Statement
© Crown 2024.
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
You might also like
An Approximate Model for Event Detection from Twitter Data
(2020)
Journal Article
Downloadable Citations
About Repository@Hull
Administrator e-mail: repository@hull.ac.uk
This application uses the following open-source libraries:
SheetJS Community Edition
Apache License Version 2.0 (http://www.apache.org/licenses/)
PDF.js
Apache License Version 2.0 (http://www.apache.org/licenses/)
Font Awesome
SIL OFL 1.1 (http://scripts.sil.org/OFL)
MIT License (http://opensource.org/licenses/mit-license.html)
CC BY 3.0 ( http://creativecommons.org/licenses/by/3.0/)
Powered by Worktribe © 2024
Advanced Search