How effective are social distancing policies? Evidence on the fight against COVID-19.
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ABSTRACT:
SUBMITTER: Glogowsky U
PROVIDER: S-EPMC8457454 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Non-compliance with social distancing (SD) measures clearly has negative effects on both public health and post-pandemic economic recovery. However, little is as yet known about people's views on and factors influencing their behavioral intentions toward SD measures. This study draws on moral disengagement theory and the norm-activation model to investigate mechanisms that promote or hinder compliance with SD measures. A longitudinal research approach was adopted to compare changes in the main factors over three periods of the COVID-19 pandemic in England (UK). The results reveal significant differences between the three periods regarding intentions to comply with SD measures, altruistic value, moral obligation and moral disengagement, with no significant change in ascription of responsibility. Residents showed the strongest intentions to comply with SD measures during the first national lockdown, with the highest moral obligation and lowest moral disengagement levels, compared with the lowest intention to comply during the first re-opening period. Altruistic value is important in promoting moral obligation and compliance with SD measures, whereas the predictive powers of ascription of responsibility and moral disengagement were weaker than expected. These findings offer guidance to policymakers and researchers in developing more effective policies and public communication strategies. The results suggest that communication is key to normalizing SD compliance, which can be achieved most effectively by fostering residents' altruistic value and moral considerations. Particular attention must be paid to re-opening periods between lockdowns, with clear messages to remind residents of prosocial aspects of SD compliance and public health. In addition to appropriate communication and education, technologies such as apps, QR codes and contactless shopping settings may also be used to facilitate compliance with SD measures.
| S-EPMC8696958 | biostudies-literature
Project description:BackgroundThe novel coronavirus disease 2019 (COVID-19) sickened over 20 million residents in the United States (US) by January 2021. Our objective was to describe state variation in the effect of initial social distancing policies and non-essential business (NEB) closure on infection rates early in 2020.MethodsWe used an interrupted time series study design to estimate the total effect of all state social distancing orders, including NEB closure, shelter-in-place, and stay-at-home orders, on cumulative COVID-19 cases for each state. Data included the daily number of COVID-19 cases and deaths for all 50 states and Washington, DC from the New York Times database (January 21 to May 7, 2020). We predicted cumulative daily cases and deaths using a generalized linear model with a negative binomial distribution and a log link for two models.ResultsSocial distancing was associated with a 15.4% daily reduction (Relative Risk = 0.846; Confidence Interval [CI] = 0.832, 0.859) in COVID-19 cases. After 3 weeks, social distancing prevented nearly 33 million cases nationwide, with about half (16.5 million) of those prevented cases among residents of the Mid-Atlantic census division (New York, New Jersey, Pennsylvania). Eleven states prevented more than 10,000 cases per 100,000 residents within 3 weeks.ConclusionsThe effect of social distancing on the infection rate of COVID-19 in the US varied substantially across states, and effects were largest in states with highest community spread.
| S-EPMC8237534 | biostudies-literature
Project description:Social distancing policies (SDPs) implemented in response to the COVID-19 pandemic have led to temporal and spatial shifts in water demand across cities. Water utilities need to understand these demand shifts to respond to potential operational and water-quality issues. Aided by a fixed-effects model of citywide water demand in Austin, Texas, we explore the impacts of various SDPs (e.g., time after the stay home-work safe order, reopening phases) using daily demand data gathered between 2013 and 2020. Our approach uses socio-technical determinants (e.g., climate, water conservation policy) with SDPs to model water demand, while accounting for spatial and temporal effects (e.g., geographic variations, weekday patterns). Results indicate shifts in behavior of residential and nonresidential demands that offset the change at the system scale, demonstrating a spatial redistribution of water demand after the stay home-work safe order. Our results show that some phases of Texas's reopening phases had statistically significant relationships to water demand. While this yielded only marginal net effects on overall demand, it underscores behavioral changes in demand at sub-system spatial scales. Our discussions shed light on SDPs' impacts on water demand. Equipped with our empirical findings, utilities can respond to potential vulnerabilities in their systems, such as water-quality problems that may be related to changes in water pressure in response to demand variations.
| S-EPMC8519786 | biostudies-literature
Project description:We collected COVID-19 epidemiological and epidemic control measures-related data in mainland China during the period January 1 to February 19, 2020, and empirically tested the practical effects of the epidemic control measures implemented in China by applying the econometrics approach. The results show that nationally, both traffic control and social distancing have played an important role in controlling the outbreak of the epidemic, however, neither of the two measures have had a significant effect in low-risk areas. Moreover, the effect of traffic control is more successful than that of social distancing. Both measures complement each other, and their combined effect achieves even better results. These findings confirm the effectiveness of the measures currently in place in China, however, we would like to emphasize that control measures should be more tailored, which implemented according to each specific city's situation, in order to achieve a better epidemic prevention and control.
| S-EPMC8171991 | biostudies-literature
Project description:The COVID-19 pandemic has become a crucial public health problem in the world that disrupted the lives of millions in many countries including the United States. In this study, we present a decision analytic approach which is an efficient tool to assess the effectiveness of early social distancing measures in communities with different population characteristics. First, we empirically estimate the reproduction numbers for two different states. Then, we develop an age-structured compartmental simulation model for the disease spread to demonstrate the variation in the observed outbreak. Finally, we analyze the computational results and show that early trigger social distancing strategies result in smaller death tolls; however, there are relatively larger second waves. Conversely, late trigger social distancing strategies result in higher initial death tolls but relatively smaller second waves. This study shows that decision analytic tools can help policy makers simulate different social distancing scenarios at the early stages of a global outbreak. Policy makers should expect multiple waves of cases as a result of the social distancing policies implemented when there are no vaccines available for mass immunization and appropriate antiviral treatments.
| S-EPMC8233412 | biostudies-literature
Project description:Social distancing remains an important strategy to combat the COVID-19 pandemic in the United States. However, the impacts of specific state-level policies on mobility and subsequent COVID-19 case trajectories have not been completely quantified. Using anonymized and aggregated mobility data from opted-in Google users, we found that state-level emergency declarations resulted in a 9.9% reduction in time spent away from places of residence. Implementation of one or more social distancing policies resulted in an additional 24.5% reduction in mobility the following week, and subsequent shelter-in-place mandates yielded an additional 29.0% reduction. Decreases in mobility were associated with substantial reductions in case growth two to four weeks later. For example, a 10% reduction in mobility was associated with a 17.5% reduction in case growth two weeks later. Given the continued reliance on social distancing policies to limit the spread of COVID-19, these results may be helpful to public health officials trying to balance infection control with the economic and social consequences of these policies.
| S-EPMC8149701 | biostudies-literature
Project description:We study changes in social distancing and government policy in response to local outbreaks during the COVID-19 pandemic. Using aggregated county-level data from approximately 20 million smartphones in the United States, we show that social distancing behaviors have responded to local outbreaks: a 1% increase in new cases (deaths) is associated with a 3% (11%) increase in social distancing intensity. Responsiveness is reinforced by the presence of public measures restricting movements, but remains significant in their absence. Responsiveness is higher in high-income, more educated, or Democrat-leaning counties, and in counties with low health insurance coverage. By contrast, social capital and vulnerability to infection are strongly associated with more social distancing but not with more responsiveness. Our results point to the importance of politics, trust and reciprocity for compliance with social distancing, while material constraints are more critical for being responsive to new risks such as the emergence of variants.
| S-EPMC9119550 | biostudies-literature
Project description:ObjectivePredicting daily trends in the Coronavirus Disease 2019 (COVID-19) case number is important to support individual decisions in taking preventative measures. This study aims to use COVID-19 case number history, demographic characteristics, and social distancing policies both independently/interdependently to predict the daily trend in the rise or fall of county-level cases.Materials and methodsWe extracted 2093 features (5 from the US COVID-19 case number history, 1824 from the demographic characteristics independently/interdependently, and 264 from the social distancing policies independently/interdependently) for 3142 US counties. Using the top selected 200 features, we built 4 machine learning models: Logistic Regression, Naïve Bayes, Multi-Layer Perceptron, and Random Forest, along with 4 Ensemble methods: Average, Product, Minimum, and Maximum, and compared their performances.ResultsThe Ensemble Average method had the highest area-under the receiver operator characteristic curve (AUC) of 0.692. The top ranked features were all interdependent features.ConclusionThe findings of this study suggest the predictive power of diverse features, especially when combined, in predicting county-level trends of COVID-19 cases and can be helpful to individuals in making their daily decisions. Our results may guide future studies to consider more features interdependently from conventionally distinct data sources in county-level predictive models. Our code is available at: https://doi.org/10.5281/zenodo.6332944.
| S-EPMC9278037 | biostudies-literature
Project description:Social distancing is the most visible public health response to the COVID-19 pandemic, but its implications for mental health are unknown. In a nationwide online sample of 435 U.S. adults, conducted in March 2020 as the pandemic accelerated and states implemented stay-at-home orders, we examined whether stay-at-home orders and individuals' personal distancing behavior were associated with symptoms of depression, generalized anxiety disorder (GAD), intrusive thoughts, insomnia, and acute stress. Stay-at-home order status and personal distancing were independently associated with higher symptoms, beyond protective effects of available social resources (social support and social network size). A subsample of 118 participants who had completed symptom measures earlier in the outbreak (February 2020) showed increases in depression and GAD between February and March, and personal distancing behavior was associated with these increases. Findings suggest that there are negative mental health correlates of social distancing, which should be addressed in research, policy, and clinical approaches to the COVID-19 pandemic.
| S-EPMC7439968 | biostudies-literature
Project description:A stochastic compartmental network model of SARS-CoV-2 spread explores the simultaneous effects of policy choices in three domains: social distancing, hospital triaging, and testing. Considering policy domains together provides insight into how different policy decisions interact. The model incorporates important characteristics of COVID-19, the disease caused by SARS-CoV-2, such as heterogeneous risk factors and asymptomatic transmission, and enables a reliable qualitative comparison of policy choices despite the current uncertainty in key virus and disease parameters. Results suggest possible refinements to current policies, including emphasizing the need to reduce random encounters more than personal contacts, and testing low-risk symptomatic individuals before high-risk symptomatic individuals. The strength of social distancing of symptomatic individuals affects the degree to which asymptomatic cases drive the epidemic as well as the level of population-wide contact reduction needed to keep hospitals below capacity. The relative importance of testing and triaging also depends on the overall level of social distancing.
| S-EPMC7591016 | biostudies-literature