Developing a population data science approach to assess increased risk of COVID-19 associated with attending large events.

Introduction: In summer 2021, as rates of COVID-19 decreased and social restrictions were relaxed, live entertainment and sporting events were resumed. In order to inform policy on the safe re-introduction of spectator events, a number of test events were organised in Wales, ranging in setting, size and audience. Objectives: To design and test a method to assess whether test events were associated with an increase in risk of confirmed COVID-19, in order to inform policy. Methods: We designed a cohort study with fixed follow-up time and measured relative risk of confirmed COVID-19 in those attending two large sporting events. First, we linked ticketing information to individual records on the Welsh Demographic Service (WDS), a register of all people living in Wales and registered with a GP, and identified NHS numbers for attendees. Where NHS numbers were not found we used combinations of other identifiers such as email, name, postcode and/or mobile number. We then linked attendees to routine SARS-CoV-2 test data to calculate positivity rates in people attending each event for the period one to fourteen days following the event. We selected a comparison cohort from WDS for each event, individually matched by age band, gender and locality of residence. As many people attended events in family groups we explored the possibility of also matching on household clusters within the comparison group. Risk ratios were then computed for the two events. Results: We successfully assigned NHS numbers to 91% and 84% of people attending the two events respectively. Other identifiers were available for the remainder. Only a small number of attendees (<10) had a record of confirmed COVID-19 following attendance at each event (14 day cumulative incidence: 36 and 26 per 100,000, respectively). There was no evidence of significantly increased risk of COVID-19 at either event. However, the event that didn't include pre-event testing in their mitigations, had a higher risk ratio (3.0 compared to 0.3). Conclusions: We demonstrate the potential for using population data science methods to inform policy. We conclude that, at that point in the epidemic, and with the mitigations that were in place, attending large outdoor sporting events did not significantly increase risk of COVID-19. However, these analyses were carried out between epidemic waves when background incidence and testing rate was low, and need to be repeated during periods of greater transmission. Having a mechanism to identify attendees at events is necessary to calculate risk and feasibility and acceptability of data sharing should be considered.

Developing a population data science approach to assess increased risk of COVID-19 associated with attending large events

Introduction In summer 2021, as rates of COVID-19 decreased and social restrictions were relaxed, live entertainment and sporting events were resumed. In order to inform policy on the safe re-introduction of spectator events, a number of test events were organised in Wales, ranging in setting, size and audience. Objectives To design and test a method to assess whether test events were associated with an increase in risk of confirmed COVID-19, in order to inform policy. Methods We designed a cohort study with fixed follow-up time and measured relative risk of confirmed COVID-19 in those attending two large sporting events. First, we linked ticketing information to individual records on the Welsh Demographic Service (WDS), a register of all people living in Wales and registered with a GP, and identified NHS numbers for attendees. Where NHS numbers were not found we used combinations of other identifiers such as email, name, postcode and/or mobile number. We then linked attendees to routine SARS-CoV-2 test data to calculate positivity rates in people attending each event for the period one to fourteen days following the event. We selected a comparison cohort from WDS for each event, individually matched by age band, gender and locality of residence. As many people attended events in family groups we explored the possibility of also matching on household clusters within the comparison group. Risk ratios were then computed for the two events. Results We successfully assigned NHS numbers to 91% and 84% of people attending the two events respectively. Other identifiers were available for the remainder. Only a small number of attendees (<10) had a record of confirmed COVID-19 following attendance at each event (14 day cumulative incidence: 36 and 26 per 100,000, respectively). There was no evidence of significantly increased risk of COVID-19 at either event. However, the event that didn’t include pre-event testing in their mitigations, had a higher risk ratio (3.0 compared to 0.3). Conclusions We demonstrate the potential for using population data science methods to inform policy. We conclude that, at that point in the epidemic, and with the mitigations that were in place, attending large outdoor sporting events did not significantly increase risk of COVID-19. However, these analyses were carried out between epidemic waves when background incidence and testing rate was low, and need to be repeated during periods of greater transmission. Having a mechanism to identify attendees at events is necessary to calculate risk and feasibility and acceptability of data sharing should be considered.

Family cluster of three cases of monkeypox imported from Nigeria to the United Kingdom, May 2021.

Most reported cases of human monkeypox occur in Central and West Africa, where the causing virus is endemic. We describe the identification and public health response to an imported case of West African monkeypox from Nigeria to the United Kingdom (UK) in May 2021. Secondary transmission from the index case occurred within the family to another adult and a toddler. Concurrent COVID-19-related control measures upon arrival and at the hospital, facilitated detection and limited the number of potential contacts.