Eurosurveillance - Volume 26, Issue 40, 07 October 2021

Annual seasonal influenza activity in the northern hemisphere causes a high burden of disease during the winter months, peaking in the first weeks of the year.

We describe the 2019/20 influenza season and the impact of the COVID-19 pandemic on sentinel surveillance in the World Health Organization (WHO) European Region.

We analysed weekly epidemiological and virological influenza data from sentinel primary care and hospital sources reported by countries, territories and areas (hereafter countries) in the European Region.

We observed co-circulation of influenza B/Victoria-lineage, A(H1)pdm09 and A(H3) viruses during the 2019/20 season, with different dominance patterns observed across the Region. A higher proportion of patients with influenza A virus infection than type B were observed. The influenza activity started in week 47/2019, and influenza positivity rate was ≥ 50% for 2 weeks (05–06/2020) rather than 5–8 weeks in the previous five seasons. In many countries a rapid reduction in sentinel reports and the highest influenza activity was observed in weeks 09–13/2020. Reporting was reduced from week 14/2020 across the Region coincident with the onset of widespread circulation of SARS-CoV-2.

Overall, influenza type A viruses dominated; however, there were varying patterns across the Region, with dominance of B/Victoria-lineage viruses in a few countries. The COVID-19 pandemic contributed to an earlier end of the influenza season and reduced influenza virus circulation probably owing to restricted healthcare access and public health measures.

Many countries have implemented population-wide interventions to control COVID-19, with varying extent and success. Many jurisdictions have moved to relax measures, while others have intensified efforts to reduce transmission.

We aimed to determine the time frame between a population-level change in COVID-19 measures and its impact on the number of cases.

We examined how long it takes for there to be a substantial difference between the number of cases that occur following a change in COVID-19 physical distancing measures and those that would have occurred at baseline. We then examined how long it takes to observe this difference, given delays and noise in reported cases. We used a susceptible-exposed-infectious-removed (SEIR)-type model and publicly available data from British Columbia, Canada, collected between March and July 2020.

It takes 10 days or more before we expect a substantial difference in the number of cases following a change in COVID-19 control measures, but 20–26 days to detect the impact of the change in reported data. The time frames are longer for smaller changes in control measures and are impacted by testing and reporting processes, with delays reaching ≥ 30 days.

The time until a change in control measures has an observed impact is longer than the mean incubation period of COVID-19 and the commonly used 14-day time period. Policymakers and practitioners should consider this when assessing the impact of policy changes. Rapid, consistent and real-time COVID-19 surveillance is important to minimise these time frames.

The occupational risk of COVID-19 may be different in the first versus second epidemic wave.

To study whether employees in occupations that typically entail close contact with others were at higher risk of SARS-CoV-2 infection and COVID-19-related hospitalisation during the first and second epidemic wave before and after 18 July 2020, in Norway.

We included individuals in occupations working with patients, children, students, or customers using Standard Classification of Occupations (ISCO-08) codes. We compared residents (3,559,694 on 1 January 2020) in such occupations aged 20–70 years (mean: 44.1; standard deviation: 14.3 years; 51% men) to age-matched individuals in other professions using logistic regression adjusted for age, sex, birth country and marital status.

Nurses, physicians, dentists and physiotherapists had 2–3.5 times the odds of COVID-19 during the first wave when compared with others of working age. In the second wave, bartenders, waiters, food counter attendants, transport conductors, travel stewards, childcare workers, preschool and primary school teachers had ca 1.25–2 times the odds of infection. Bus, tram and taxi drivers had an increased odds of infection in both waves (odds ratio: 1.2–2.1). Occupation was of limited relevance for the odds of severe infection, here studied as hospitalisation with the disease.

Our findings from the entire Norwegian population may be of relevance to national and regional authorities in handling the epidemic. Also, we provide a knowledge foundation for more targeted future studies of lockdowns and disease control measures.