Rapid review of social contact patterns during the COVID-19 pandemic

BackgroundPhysical distancing measures aim to reduce person-to-person contact, a key driver of transmission of respiratory infections such as SARS-CoV-2. In response to unprecedented restrictions on human contact during the COVID-19 pandemic, a number of studies measured social contact patterns under the implementation of physical distancing measures. This rapid review aims to synthesize empirical data on the changing social contact patterns during the COVID-19 pandemic. MethodWe conducted a systematic review using PubMed, Medline, Embase and Google Scholar following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. We descriptively compared the distribution of contacts observed during the pandemic to pre-COVID data across countries to explore changes in contact patterns during physical distancing measures. ResultsWe identified 12 studies that reported social contact patterns during the COVID-19 pandemic. The majority of studies (11/12) collected data during the initial mitigation period in the spring of 2020 marked by government-declared lockdowns and the most stringent physical distancing measures. Some studies collected additional data after relaxation of initial mitigation. Most study settings reported a mean of between 2-5 contacts per person per day, a substantial reduction compared to pre-COVID rates which ranged from 7-26 contacts per day in similar settings. This reduction was particularly pronounced for contacts outside of the home. Consequently, levels of assortative mixing by age substantially declined. After relaxation of initial mitigation, mean contact rates subsequently increased but did not return to pre-COVID levels. Increases in contacts post-relaxation were driven by working-age adults. ConclusionInformation on changes in contact patterns during physical distancing measures can guide more realistic representations of contact patterns in mathematical models for SARS-CoV-2 transmission.

Forecasting intensive care unit demand during the COVID-19 pandemic: A spatial age-structured microsimulation model

BackgroundThe COVID-19 pandemic poses the risk of overburdening health care systems, and in particular intensive care units (ICUs). Non-pharmaceutical interventions (NPIs), ranging from wearing masks to (partial) lockdowns have been implemented as mitigation measures around the globe. However, especially severe NPIs are used with great caution due to their negative effects on the economy, social life and mental well-being. Thus, understanding the impact of the pandemic on ICU demand under alternative scenarios reflecting different levels of NPIs is vital for political decision-making on NPIs. ObjectiveThe aim is to support political decision-making by forecasting COVID-19-related ICU demand under alternative scenarios of COVID-19 progression reflecting different levels of NPIs. Substantial sub-national variation in COVID-19-related ICU demand requires a spatially disaggregated approach. This should not only take sub-national variation in ICU-relevant disease dynamics into account, but also variation in the population at risk including COVID-19-relevant risk characteristics (e.g. age), and factors mitigating the pandemic. The forecast provides indications for policy makers and health care stakeholders as to whether mitigation measures have to be maintained or even strengthened to prevent ICU demand from exceeding supply, or whether there is leeway to relax them. MethodsWe implement a spatial age-structured microsimulation model of the COVID-19 pandemic by extending the Susceptible-Exposed-Infectious-Recovered (SEIR) framework. The model accounts for regional variation in population age structure and in spatial diffusion pathways. In a first step, we calibrate the model by applying a genetic optimization algorithm against hospital data on ICU patients with COVID-19. In a second step, we forecast COVID-19-related ICU demand under alternative scenarios of COVID 19 progression reflecting different levels of NPIs. We apply the model to Germany and provide state-level forecasts over a 2-month period, which can be updated daily based on latest data on the progression of the pandemic. ResultsTo illustrate the merits of our model, we present here "forecasts" of ICU demand for different stages of the pandemic during 2020. Our forecasts for a quiet summer phase with low infection rates identified quite some variation in potential for relaxing NPIs across the federal states. By contrast, our forecasts during a phase of quickly rising infection numbers in autumn (second wave) suggested that all federal states should implement additional NPIs. However, the identified needs for additional NPIs varied again across federal states. In addition, our model suggests that during large infection waves ICU demand would quickly exceed supply, if there were no NPIs in place to contain the virus. ConclusionOur results provide evidence for substantial spatial variation in (1) the effect of the pandemic on ICU demand, and (2) the potential and need for NPI adjustments at different stages of the pandemic. Forecasts with our spatial age-structured microsimulation model allow to take this spatial variation into account. The model is programmed in R and can be applied to other countries, provided that reliable data on the number of ICU patients infected with COVID-19 are available at sub-national level.

COVeAGE-DB: A database of age-structured COVID-19 cases and deaths.

COVerAGE-DB is an open-access database including cumulative counts of confirmed COVID-19 cases, deaths, and tests by age and sex. The main goal of COVerAGE-DB is to provide a centralized, standardized, age-harmonized, and fully reproducible database of COVID-19 data. Original data and sources are provided alongside data and measures in age-harmonized formats. An international team, composed of more than 60 researchers, contributed to the collection of data and metadata in COVerAGE-DB from governmental institutions, as well as to the design and implementation of the data processing and validation pipeline. The database is still in development, and at this writing, it includes 89 countries, and 237 subnational areas. Cumulative counts of COVID-19 cases, deaths, and tests are recorded daily (when possible) since January 2020. Many time series thus fully capture the first pandemic wave and the beginning of later waves. Since collection efforts began for COVerAGE-DB several studies have used the data.

The differential impact of physical distancing strategies on social contacts relevant for the spread of COVID-19

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, Rt, 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 bring Rt to 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.

Behaviors and attitudes in response to the COVID-19 pandemic: Insights from a cross-national Facebook survey

In the absence of medical treatment and vaccination, individual behaviours are key to controlling the spread of COVID-19. We developed a rapid response monitoring system through an online survey (the "COVID-19 Health Behavior Survey"). Participant recruitment takes places continuously via Facebook in eight countries (Belgium, France, Germany, Italy, the Netherlands, Spain, the United Kingdom, the United States). The survey collects key information on peoples health, attitudes, behaviours, and social contacts. In this paper, we present results based on a total of 71,612 completed questionnaires, collected between March 13-April 19, 2020. We find sex-specific patterns, as women show higher threat perceptions, lower confidence in the healthcare system, and a higher likelihood of adopting preventive behaviours. Our findings also show higher awareness and concern among older respondents. Finally, we find spatio-temporal heterogeneity in threat perception, confidence in organisations, and adoption of preventive behaviours.