Age, Gender, and Disease as Determinants of Social Distancing: Germany as a Case Study.

A mix of guidance and mandated regulations during the coronavirus disease (COVID-19) pandemic served to reduce the number of social contacts, to ensure distancing in public spaces, and to maintain the isolation of infected individuals. Individual variation in compliance to social distancing in Germany, relating to age, gender, or the presence of pre-existing health conditions, was examined using results from a total of 39 375 respondents to a web-based behavioral survey.Older people and females were more willing to engage in social distancing. Those with chronic conditions showed overall higher levels of compliance, but those with cystic fibrosis, human immunodeficiency virus/acquired immunodeficiency syndrome (HIV/AIDS), and epilepsy showed less adherence to general social distancing measures but were significantly more likely to isolate in their homes. Behavioral differences partly lie in the nature of each condition, especially with those conditions likely to be exacerbated by COVID-19. Compliance differences for age and gender are largely in line with previous studies.

Spatiotemporal variable selection and air quality impact assessment of COVID-19 lockdown.

During the first wave of the COVID-19 pandemics in 2020, lockdown policies reduced human mobility in many countries globally. This significantly reduces car traffic-related emissions. In this paper, we consider the impact of the Italian restrictions (lockdown) on the air quality in the Lombardy Region. In particular, we consider public data on concentrations of particulate matters (PM10 and PM2.5) and nitrogen dioxide, pre/during/after lockdown. To reduce the effect of confounders, we use detailed regression function based on meteorological, land and calendar information. Spatial and temporal correlations are handled using a multivariate spatiotemporal model in the class of hidden dynamic geostatistical models (HDGM). Due to the large size of the design matrix, variable selection is made using a hybrid approach coupling the well known LASSO algorithm with the cross-validation performance of HDGM. The impact of COVID-19 lockdown is heterogeneous in the region. Indeed, there is high statistical evidence of nitrogen dioxide concentration reductions in metropolitan areas and near trafficked roads where also PM10 concentration is reduced. However, rural, industrial, and mountain areas do not show significant reductions. Also, PM2.5 concentrations lack significant reductions irrespective of zone. The post-lockdown restart shows unclear results.