OBJECTIVE: The effectiveness of social distancing during the coronavirus disease 2019 (COVID-19) pandemic has been evaluated using the magnitude of changes in population mobility. This study aimed to investigate a direct indicator-namely, the number of close contacts per patient with confirmed COVID-19.
METHODS: From week 7, 2020 to week 43, 2021, population movement changes were calculated from the data of two Korean telecommunication companies and Google in accordance with social distancing stringency levels. Data on confirmed cases and their close contacts among residents of Gyeonggi Province, Korea were combined at each stage. Pearson correlation analysis was conducted to compare the movement data with the change in the number of contacts for each confirmed case calculated by stratification according to age group. The reference value of the population movement data was set using the value before mid-February 2020, considering each data\'s characteristics.
RESULTS: In the age group of 18 or younger, the number of close contacts per confirmed case decreased or increased when the stringency level was strengthened or relaxed, respectively. In adults, the correlation was relatively low, with no correlation between the change in the number of close contacts per confirmed case and the change in population movement after the commencement of vaccination for adults.
CONCLUSIONS: The effectiveness of governmental social distancing policies against COVID-19 can be evaluated using the number of close contacts per confirmed case as a direct indicator, especially for each age group. Such an analysis can facilitate policy changes for specific groups.

After COVID-19 began spreading through fecal-oral routes, crowded cities introduced social distancing policies. Mobility patterns in urban also changed because of the pandemic and the policies to reduce the infection of it. This study investigates the impact of COVID-19 and related policies such as social-distancing by comparing bike-share demand in Daejeon, Korea. By using big data analytics and data visualization, the study measures differences in bike-sharing demand between 2018-19, before the pandemic, and 2020-21, during the pandemic. According to results, (1) bike-share users tend to travel long distances and cycle more than before the pandemic, (2) bike users choose cycling not for commuting but for transportation during the pandemic, and (3) the pandemic has broadened the spatial borders bike-usages. These results provide meaningful implications for urban planners and policymakers by identifying differences in the ways people use public bikes during the pandemic era.

COVID-19 pandemic has changed several aspects in human behaviour, whereas population mobility patterns have been strongly affected by social distancing. In parallel, changes in solid waste generation patterns have been reported worldwide. This work assessed the influence of the COVID-19 pandemic on waste generation and collection in São Paulo city, Brazil, the largest city of Latin America. Data on nine types of wastes collected between 2013 and 2021 were obtained, and the quantities of wastes collected before and during the pandemic were compared. These data were also discussed in light of data on COVID-19 cases and rates of social distancing and mobility. An increase in the amount of recyclables collected during the first wave of COVID-19 (March to September 2020) was observed. Decreases in the quantities of construction, demolition and bulky wastes (first wave of COVID-19) and farmers market wastes (second wave - October 2020 to February 2021) were also evidenced. The quantities of medical wastes collected during the pandemic significantly increased. The amount of residential wastes was lower than the mean for the pre-pandemic period during the first months of COVID-19. Thus, changes in lifestyle and consumption patterns of the population of São Paulo city seem to have affected solid waste generation during the pandemic, which reinforces the need of implementing solid waste management policies based on a diagnostic that characterise and consider these changes.

The COVID-19 pandemic impacts on eating habits among adolescents may be more relevant in pediatric patients with immunocompromised chronic diseases. This case-control study conducted between June and October 2020 aimed to: (i) describe dietary patterns of adolescents with chronic conditions compared to healthy controls and (ii) determine associations between food consumption, health-related quality of life (HRQL) and sleep quality during the COVID-19 pandemic. Participants (184 immunocompromised and 58 healthy adolescents, aged 14.3 [SD 2.5]) responded to HRQL and sleep validated instruments (PedsQL and PSQI) and three 24 h food recalls via online software. Adjusted linear and logistic regressions were used to assess differences in dietary patterns and associations between food consumption (according to Nova classification) and HRQL and sleep quality. Adolescents with gastrohepatic, rheumatic, and kidney diseases had an improved dietary pattern vs. their healthy peers, showing greater consumption of unprocessed and minimally processed foods (unstandardized coefficient (b) = 7.35%[95%CI 1.59; 13.1]; b = 15.10%[95%CI 7.00; 23.1]; and b = 11.2%[95%CI 5.68; 16.8]), and lower consumption of ultraprocessed foods (b = -7.53%[95%CI-12.90; -2.18]; b = -11.4%[95%CI-18.90; -3.94]; b = -10.8%[95%CI-16.00; -5.68]). Consumption of culinary ingredients was associated with reduced psychological HRQL in controls (standardized coefficient (β) = -0.26[95%CI-0.52; -0.004]), and processed food consumption was associated with improved sleep latency in immunocompromised participants (β = 0.16[95%CI 0.01; 0.31]). These findings suggest diet quality may play a role in HRQL and sleep quality in this population, and may be relevant for clinical practitioners and policy makers when considering the importance of dietary quality in immunocompromised youths.

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.

UNASSIGNED: This study aimed to determine whether there is a correlation between COVID-19 cases and deaths because of COVID-19 and community movements in Turkey and to develop a strategy for future outbreaks.
UNASSIGNED: The study\'s data covers COVID-19 cases and deaths between March 11, 2020, and December 16, 2021, and Turkey\'s Google community movements between these dates. The COVID-19 cases and deaths were obtained from Turkey\'s Ministry of Health COVID-19 Information Platform. Community mobility collated by Google is retail and recreation, supermarket and pharmacy, parks, public transport, workplaces visits, and residential. The data were transferred via \"SPSS (Statistical Package for Social Sciences) for Windows 25.0 (SPSS Inc, Chicago, IL\" and statistical analysis was performed. The Spearman correlation test was used as a statistical method. In the Kruskal-Wallis Test, categorical variables were created using increases and decreases in community movements based on the baseline.
UNASSIGNED: A weak positive correlation between daily COVID-19 deaths and supermarket and pharmacy activity (r = 0.28 p < 0.01). A weak negative correlation with park activity (r = -0.23 p < 0.01). A weakly positive and significant relationship with workplace visits mobility (r = 0.10 p < 0.05). There was a weak positive significant relationship with public transport mobility (r = 0.10 p < 0.01), including a weak positive significant relationship with residential (r = 0.12 p < 0.01).
UNASSIGNED: Social distancing measures (such as reducing community mobility) and educating people on viral transmission in possible epidemics will save us time developing new diagnostic tests and vaccine studies.

[This corrects the article DOI: 10.3389/fpubh.2022.913096.].

Vietnam has been one of a few countries that successfully contained the COVID-19 pandemic. However, aggressive measurements against the pandemic were at the expense of economic activities and companies\' financial performances. This cross-sectional study uses a survey of 672 companies in Vietnam and the logistic regression model to explore companies\' coping strategy choices based on their degree of financial distress, companies\' profiles, entrepreneurial factors, and the interactions between them. The results suggest that companies predominantly selected cost-cutting strategies to deal with the economic shutdown. However, the interactions between financial and entrepreneurial factors could significantly increase the likelihood of selecting growth-focused strategies. Besides, when facing a global pandemic such as COVID-19, managers\' perceptions about the spillover effects of global risks were much more impactful than local risks on companies\' coping strategy selections. This paper can help to inform managers to better deal with the aftermath of the COVID-19 outbreak.

Due to its remarkable learning ability and benefits in several areas of real-life, deep learning-based applications have recovered to be a research topic of great importance in the last few years. This article presents a method devoted to guaranteeing safety conditions in public transportation systems (PTS) during the COVID-19 pandemic and post-pandemic era. The paper describes a viable real-time model based on deep learning for monitoring social distance between users and detecting face masks in stop areas and inside vehicles of public transportation systems. Detections are made using the deep learning approach and YOLOv3 algorithm. The safety rule violations are represented by red bounding boxes and red circles in a bird\'s eye view as output of the video surveillance analysis. The datasets used to train the neural network are the \"Caltech Pedestrian Dataset\" and the \"COVID-19 Medical Face Mask Detection Dataset\". Metrics, such Loss Accuracy, and Precision, obtained in the testing process of the neural network were used to evaluate the performance of the model in detecting users and face masks. The proposed method was recently tested in the Public Transportation System of the Municipality of Piazza Armerina (Italy). The results show a significant reliability of the method in detecting real-time interactions between users of the PTS in terms of over-time variations in their mutual distancing, as well as in recognising cases of violation of the imposed social distancing and FFP2 face mask use.

Individual social responsibility is essential to achieving the sustainable development goals of the society, yet there has been very little research on whether and how social and cultural factors influence individual social responsibility. Using the Covid-19 pandemic as our empirical context, this research examines the relationship between social capital and individual social distancing behaviors during the pandemic. Social distancing is a form of socially responsible behavior because it is critical in mitigating the spread of the Covid-19 virus. By exploiting daily mobile GPS location data, we provide strong evidence for the divergent relationships between the two constituents of social capital-civic norms and social networks-and social distancing behaviors. While civic norms are positively associated with social distancing, social networks have a negative association with social distancing. These results are consistent with a nuanced view of social capital: civic norms facilitate cooperation and self-sacrifice for the common good, whereas social networks increase individual embeddedness and hence inertia in maintaining social interactions, resulting in opposite effects on social distancing. Our results contribute to the research at the intersection of social capital and individual social responsibility by highlighting the nuanced effects of social capital on individual responses to the pandemic and provide valuable insights for policymakers and businesses in disaster management.