The differential impact of physical distancing strategies on social contacts relevant for the spread of COVID-19: Evidence from a multi-country survey

Physical distancing measures are intended to mitigate the spread of COVID-19, even though their impact on social contacts and disease transmission remains unclear. Obtaining timely data on social contact patterns can help to assess the impact of such protective measures. We conducted an online opt-in survey based on targeted Facebook advertising campaigns across seven European countries (Belgium, France, Germany, Italy, Netherlands, Spain, United Kingdom (UK)) and the United States (US), achieving a sample of 53,708 questionnaires in the period March 13–April 13, 2020. Post-stratification weights were produced to correct for biases. Data on social contact numbers, as well as on protective behaviour and perceived level of threat were collected and used to the expected net reproduction number by week,R_t, with respect to pre-pandemic data. Compared to social contacts reported prior to COVID-19, in mid-April daily social contact numbers had decreased between 49% in Germany and 83% in Italy, ranging from below three contacts per day in France, Spain, and the UK up to four in Germany and the Netherlands. Such reductions were sufficient to bringR_tto one or even below in all countries, except Germany. Evidence from the US and the UK showed that the number of daily social contacts mainly decreased after governments issued the first physical distancing guidelines. Finally, although contact numbers decreased uniformly across age groups, older adults reported the lowest numbers of contacts, indicating higher levels of protection. We provided a comparable set of statistics on social contact patterns during the COVID-19 pandemic for eight high-income countries, disaggregated by week. As these estimates offer a more grounded alternative to the theoretical assumptions often used in epidemiological models, the scientific community could draw on this information for developing more realistic epidemic models of COVID-19.