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Background: “Lockdowns” to control serious respiratory virus pandemics were widely used during the coronavirus disease 2019 (COVID-19) pandemic. However, there is limited information to understand the settings in which most transmission occurs during lockdowns, to support refinement of similar policies for future pandemics. Methods: Among Virus Watch household cohort participants we identified those infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) outside the household. Using survey activity data, we undertook multivariable logistic regressions assessing the contribution of activities on non-household infection risk. We calculated adjusted population attributable fractions (APAF) to estimate which activity accounted for the greatest proportion of non-household infections during the pandemic’s second wave. Results: Among 10,858 adults, 18% of cases were likely due to household transmission. Among 10,475 participants (household-acquired cases excluded), including 874 non-household-acquired infections, infection was associated with: leaving home for work or education (AOR 1.20 (1.02 – 1.42), APAF 6.9%); public transport (more than once per week AOR 1.82 (1.49 – 2.23), public transport APAF 12.42%); and shopping (more than once per week AOR 1.69 (1.29 – 2.21), shopping APAF 34.56%). Other non-household activities were rare and not significantly associated with infection. Conclusions: During lockdown, going to work and using public or shared transport independently increased infection risk, however only a minority did these activities. Most participants visited shops, accounting for one-third of non-household transmission. Transmission in restricted hospitality and leisure settings was minimal suggesting these restrictions were effective. If future respiratory infection pandemics emerge these findings highlight the value of working from home, using forms of transport that minimise exposure to others, minimising exposure to shops and restricting non-essential activities.

Background: Understanding symptomatology and accuracy of clinical case definitions for community COVID-19 cases is important for Test, Trace and Isolate (TTI) and future targeting of early antiviral treatment. Methods: Community cohort participants prospectively recorded daily symptoms and swab results (mainly undertaken through the UK TTI system). We compared symptom frequency, severity, timing, and duration in test positive and negative illnesses. We compared the test performance of the current UK TTI case definition (cough, high temperature, or loss of or altered sense of smell or taste) with a wider definition adding muscle aches, chills, headache, or loss of appetite. Results: Among 9706 swabbed illnesses, including 973 SARS-CoV-2 positives, symptoms were more common, severe and longer lasting in swab positive than negative illnesses. Cough, headache, fatigue, and muscle aches were the most common symptoms in positive illnesses but also common in negative illnesses. Conversely, high temperature, loss or altered sense of smell or taste and loss of appetite were less frequent in positive illnesses, but comparatively even less frequent in negative illnesses. The current UK definition had 81% sensitivity and 47% specificity versus 93% and 27% respectively for the broader definition. 1.7-fold more illnesses met the broader case definition than the current definition. Conclusions: Symptoms alone cannot reliably distinguish COVID-19 from other respiratory illnesses. Adding additional symptoms to case definitions could identify more infections, but with a large increase in the number needing testing and the number of unwell individuals and contacts self-isolating whilst awaiting results.

Background: Household overcrowding is associated with increased risk of infectious diseases across contexts and countries. Limited data exist linking household overcrowding and risk of COVID-19. We used data collected from the Virus Watch cohort to examine the association between overcrowded households and SARS-CoV-2. Methods: The Virus Watch study is a household community cohort of acute respiratory infections in England and Wales. We calculated overcrowding using the measure of persons per room for each household. We considered two primary outcomes: PCR-confirmed positive SARS-CoV-2 antigen tests and laboratory-confirmed SARS-CoV-2 antibodies. We used mixed-effects logistic regression models that accounted for household structure to estimate the association between household overcrowding and SARS-CoV-2 infection. Results: 26,367 participants were included in our analyses. The proportion of participants with a positive SARS-CoV-2 PCR result was highest in the overcrowded group (9.0%; 99/1,100) and lowest in the under-occupied group (4.2%; 980/23,196). In a mixed-effects logistic regression model, we found strong evidence of an increased odds of a positive PCR SARS-CoV-2 antigen result (odds ratio 2.45; 95% CI:1.43–4.19; p-value=0.001) and increased odds of a positive SARS-CoV-2 antibody result in individuals living in overcrowded houses (3.32; 95% CI:1.54–7.15; p-value

Introduction: Increased transmissibility of B.1.1.7 variant of concern (VOC) in the UK may explain its rapid emergence and global spread. We analysed data from putative household infector - infectee pairs in the Virus Watch Community cohort study to assess the serial interval of COVID-19 and whether this was affected by emergence of the B.1.1.7 variant. Methods: The Virus Watch study is an online, prospective, community cohort study following up entire households in England and Wales during the COVID-19 pandemic. Putative household infector-infectee pairs were identified where more than one person in the household had a positive swab matched to an illness episode. Data on whether or not individual infections were caused by the B.1.1.7 variant were not available. We therefore developed a classification system based on the percentage of cases estimated to be due to B.1.1.7 in national surveillance data for different English regions and study weeks. Results: Out of 24,887 illnesses reported, 915 tested positive for SARS-CoV-2 and 186 likely ‘infector-infectee’ pairs in 186 households amongst 372 individuals were identified. The mean COVID-19 serial interval was 3.18 (95%CI: 2.55 - 3.81) days. There was no significant difference (p=0.267) between the mean serial interval for VOC hotspots (mean = 3.64 days, (95%CI: 2.55 – 4.73)) days and non-VOC hotspots, (mean = 2.72 days, (95%CI: 1.48 – 3.96)). Conclusions: Our estimates of the average serial interval of COVID-19 are broadly similar to estimates from previous studies and we find no evidence that B.1.1.7 is associated with a change in serial intervals. Alternative explanations such as increased viral load, longer period of viral shedding or improved receptor binding may instead explain the increased transmissibility and rapid spread and should undergo further investigation.