Individual social contact data and population mobility data as early markers of SARS-CoV-2 transmission dynamics during the first wave in Germany—an analysis based on the COVIMOD study

Abstract

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.

Estimating RSV seasonality from pandemic disruptions: a modelling study

“…To our knowledge, there are no data on the magnitude of contact reduction after July 2020 for Australia. In Germany, Google mobility data were found to correlate well with the contact data collected in 2020 when weighted for home/non-home contacts [65]. A similar 1:1 correlation of the reduction of mobility at workplaces with the reduction of work contacts was also found in the UK with the CoMix study (see Fig.…”

Resuming social contact after months of contact restrictions: Social traits moderate associations between changes in social contact and well-being

“…The "lockdown" to manage the COVID-19 pandemic in Germany in early 2020 initially created strong situational constraints severely restricting everyday mobility in all age groups and regions (Becher et al, 2021;Bönisch et al, 2020). Compared to pre-pandemic levels, social contact frequency was estimated to have decreased by 61-90%, reaching a nadir in April 2020 (Del Fava et al, 2021;Tomori et al, 2021). This time period, during which our longitudinal study started, also represents the maximum extent of governmental contact restrictions in all German federal states during the first COVID-19 wave (Aravindakshan et al, 2020).…”

Nowcasting and Forecasting COVID-19 Waves: The Recursive and Stochastic Nature of Transmission

“…More sophisticated models are harder to calibrate and to forecast scenarios within longer periods, it is necessary to predict the evolution of other quantities, like mobility information or the disease severity dependency on age. It is also difficult to estimate the causality relations between transmission and mobility [33, 34]. Moreover, as Figure 3 shows, the performance of more sophisticated models may not be considerably better.…”