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Background The effect of contact reduction measures on infectious disease transmission can only be assessed indirectly and with considerable delay. However, individual social contact data and population mobility data can offer near real-time proxy information. The aim of this study is to compare social contact data and population mobility data with respect to their ability to reflect transmission dynamics during the first wave of the SARS-CoV-2 pandemic in Germany. Methods We quantified the change in social contact patterns derived from self-reported contact survey data collected by the German COVIMOD study from 04/2020 to 06/2020 (compared to the pre-pandemic period from previous studies) and estimated the percentage mean reduction over time. We compared these results as well as the percentage mean reduction in population mobility data (corrected for pre-pandemic mobility) with and without the introduction of scaling factors and specific weights for different types of contacts and mobility to the relative reduction in transmission dynamics measured by changes in R values provided by the German Public Health Institute. Results We observed the largest reduction in social contacts (90%, compared to pre-pandemic data) in late April corresponding to the strictest contact reduction measures. Thereafter, the reduction in contacts dropped continuously to a minimum of 73% in late June. Relative reduction of infection dynamics derived from contact survey data underestimated the one based on reported R values in the time of strictest contact reduction measures but reflected it well thereafter. Relative reduction of infection dynamics derived from mobility data overestimated the one based on reported R values considerably throughout the study. After the introduction of a scaling factor, specific weights for different types of contacts and mobility reduced the mean absolute percentage error considerably; in all analyses, estimates based on contact data reflected measured R values better than those based on mobility. Conclusions Contact survey data reflected infection dynamics better than population mobility data, indicating that both data sources cover different dimensions of infection dynamics. The use of contact type-specific weights reduced the mean absolute percentage errors to less than 1%. Measuring the changes in mobility alone is not sufficient for understanding the changes in transmission dynamics triggered by public health measures.

Background One of the primary aims of contact restriction measures during the SARS-CoV-2 pandemic has been to protect people at increased risk of severe disease from the virus. Knowledge about the uptake of contact restriction measures in this group is critical for public health decision-making. We analysed data from the German contact survey COVIMOD to assess differences in contact patterns based on risk status, and compared this to pre-pandemic data to establish whether there was a differential response to contact reduction measures. Methods We quantified differences in contact patterns according to risk status by fitting a generalised linear model accounting for within-participant clustering to contact data from 31 COVIMOD survey waves (April 2020-December 2021), and estimated the population-averaged ratio of mean contacts of persons with high risk for a severe COVID-19 outcome due to age or underlying health conditions, to those without. We then compared the results to pre-pandemic data from the contact surveys HaBIDS and POLYMOD. Results Averaged across all analysed waves, COVIMOD participants reported a mean of 3.21 (95% confidence interval (95%CI) 3.14,3.28) daily contacts (truncated at 100), compared to 18.10 (95%CI 17.12,19.06) in POLYMOD and 28.27 (95%CI 26.49,30.15) in HaBIDS. After adjusting for confounders, COVIMOD participants aged 65 or above had 0.83 times (95%CI 0.79,0.87) the number of contacts as younger age groups. In POLYMOD, this ratio was 0.36 (95%CI 0.30,0.43). There was no clear difference in contact patterns due to increased risk from underlying health conditions in either HaBIDS or COVIMOD. We also found that persons in COVIMOD at high risk due to old age increased their non-household contacts less than those not at such risk after strict restriction measures were lifted. Conclusions Over the course of the SARS-CoV-2 pandemic, there was a general reduction in contact numbers in the German population and also a differential response to contact restriction measures based on risk status for severe COVID-19. This differential response needs to be taken into account for parametrisations of mathematical models in a pandemic setting.

Background Contact behaviour is crucial to assess and predict transmission of respiratory pathogens like SARS-CoV-2. Contact behaviour has traditionally been assessed in cross-sectional surveys and not as part of longitudinal population-based studies which simultaneously measure infection frequency and vaccination coverage. During the COVID-19 pandemic, several studies assessed contact behaviour over longer periods and correlated this to data on immunity. This can inform future dynamic modelling. Here, we assess how contact behaviour varied based on SARS-CoV-2 infection or vaccination status in two large population-based studies in Germany during 2021. Methods We assessed direct encounters, separated into household and non-household contacts, in participants of MuSPAD ( n  = 12,641), a population-based cohort study, and COVIMOD ( n  = 31,260), a longitudinal contact survey. We calculated mean numbers of reported contacts and fitted negative binomial mixed-effects models to estimate the impact of immunity status, defined by vaccination or previous infection, on contact numbers; logistic mixed-effects models were used to examine the relationship between contact behaviour and seropositivity due to infection. Results Contact numbers varied over the course of the pandemic from 7.6 to 10.8 per 24 h in MuSPAD and 2.1 to 3.1 per 24 h in COVIMOD. The number of non-household contacts was higher in participants who reported previous infections and vaccinations (contact ratio (CR) MuSPAD: 1.22 (95%CI 0.94–1.60); COVIMOD: 1.35 (CI 1.12–1.62)) compared to unvaccinated and uninfected individuals. Non-household contact numbers were also higher in fully vaccinated participants (MUSPAD: CR 1.15 (CI 1.05–1.26); COVIMOD: 1.43 (CI 1.32–1.56)) compared to unvaccinated individuals. Compared to individuals without household contacts, the odds for seropositivity due to infection were higher among MuSPAD individuals with three or more household contacts (odds ratio (OR) 1.54 (CI 1.12–2.13)) and eleven or more non-household contacts (OR 1.29 (CI 1.01–1.65)). Conclusions Different contact behaviours based on infection and/or vaccination status suggest that public health policies targeting immunity status may influence the contact behaviour of those affected. A combined assessment of self-reported contacts, infections, and vaccinations as well as laboratory-confirmed serostatus in the population can support modelling of the spread of infections. This could help target containment policies and evaluate the impact of public health measures.