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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. [ABSTRACT FROM AUTHOR]

Background: Workers across different occupations vary in their risk of SARS-CoV-2 infection, but the direct contribution of occupation to this relationship is unclear. This study aimed to investigate how infection risk differed across occupational groups in England and Wales up to April 2022, after adjustment for potential confounding and stratification by pandemic phase. Methods: Data from 15,190 employed/self-employed participants in the Virus Watch prospective cohort study were used to generate risk ratios for virologically- or serologically-confirmed SARS-CoV-2 infection using robust Poisson regression, adjusting for socio-demographic and health-related factors and non-work public activities. We calculated attributable fractions (AF) amongst the exposed for belonging to each occupational group based on adjusted risk ratios (aRR). Results: Increased risk was seen in nurses (aRR = 1.44, 1.25–1.65; AF = 30%, 20–39%), doctors (aRR = 1.33, 1.08–1.65; AF = 25%, 7–39%), carers (1.45, 1.19–1.76; AF = 31%, 16–43%), primary school teachers (aRR = 1.67, 1.42- 1.96; AF = 40%, 30–49%), secondary school teachers (aRR = 1.48, 1.26–1.72; AF = 32%, 21–42%), and teaching support occupations (aRR = 1.42, 1.23–1.64; AF = 29%, 18–39%) compared to office-based professional occupations. Differential risk was apparent in the earlier phases (Feb 2020—May 2021) and attenuated later (June—October 2021) for most groups, although teachers and teaching support workers demonstrated persistently elevated risk across waves. Conclusions: Occupational differences in SARS-CoV-2 infection risk vary over time and are robust to adjustment for socio-demographic, health-related, and non-workplace activity-related potential confounders. Direct investigation into workplace factors underlying elevated risk and how these change over time is needed to inform occupational health interventions. [ABSTRACT FROM AUTHOR]