Project description:This article describes patterns of compliance with social distancing measures among the Spanish population during the coronavirus disease-2019 (COVID-19) pandemic. It identifies several factors associated with higher or lower compliance with recommended measures of social distancing. This research is part of a 67-country study, titled the International COVID-19 study on Social & Moral Psychology, in which we use a Spanish dataset. Participants were residents in Spain aged 18 or above. The sample comprises 1,090 respondents, weighted to be representative of the Spanish population. Frequencies, correlations, bivariate analysis, and six models based on hierarchical multiple regressions were applied. The main finding is that most Spaniards are compliant with established guidelines of social distance during the pandemic (State of Alarm, before May 2020). Variables associated more with lower levels of compliance with these standards were explored. Six hierarchical multiple regression models found that compliance with social distance measures has a multifactorial explanation (R 2 between 20.4 and 49.1%). Sociodemographic factors, personal hygiene patterns, and the interaction between personal hygiene patterns and the support for political measures related to the coronavirus brought significant effects on the regression models. Less compliance was also associated with beliefs in some specific conspiracy theories with regard to COVID-19 or general conspiracy mentality (Conspiracy Mentality Questionnaire, CMQ), consumption patterns of traditional mass media (television, paper newspapers, magazines, and radio) and modern means to get informed (online digital newspapers, blogs, and social networks), political ideology, vote, trust in institutions, and political identification. Among the future lines of action in preventing the possible outbreak of the virus, we suggest measures to reinforce trust in official information, mainly linked to reducing the influence of disinformation and conspiracy theories parallel to the pandemic.
Project description:As COVID-19 spreads across the globe, new technologies are being leveraged to enforce social distancing requirements. I explore social distancing through the theoretical lens of Michel Foucault’s biopolitics, with an emphasis on recognizing unauthorized movement and controlling circulation. Although reporting and widely shared data visualizations about COVID-19 have made many people newly aware that their movements are being tracked and surveilled, governments are already implementing new measures such as geofencing and artificial intelligence (AI)–based facial recognition to facilitate the enforcement of social distancing. The tracking of COVID-19 spread and social distancing behaviors of the public has made more visible the practices of biopolitics but also generated new opportunities for even greater surveillance and control. The current moment offers an opportunity to shift public perceptions about data surveillance, technological control, and the racial disparities of biopower, much in the same way that public perceptions around social media shifted during and after the Arab Spring. How we collectively respond to these biopolitical processes will, in part, determine how such power relations are articulated in the future.
Project description:We find that social distancing is affected by the policies set in neighboring counties, even after controlling for confirmed COVID cases and weather. A stay-at-home order in a neighboring county reduces social distancing by more than half as much as implementing an order in that county. This implies that, to increase social distancing in hard-hit counties, stay-at-home orders need to be implemented in a regionally or federally coordinated response. We also find that estimates of the efficacy of stay-at-home orders that do not control for policies in neighboring counties overstate the effect of these orders by about 50%.
Project description:Social distancing interventions can be effective against epidemics but are potentially detrimental for the economy. Businesses that rely heavily on face-to-face communication or close physical proximity when producing a product or providing a service are particularly vulnerable. There is, however, no systematic evidence about the role of human interactions across different lines of business and about which will be the most limited by social distancing. Here we provide theory-based measures of the reliance of U.S. businesses on human interaction, detailed by industry and geographic location. We find that, before the pandemic hit, 43 million workers worked in occupations that rely heavily on face-to-face communication or require close physical proximity to other workers. Many of these workers lost their jobs since. Consistently with our model, employment losses have been largest in sectors that rely heavily on customer contact and where these contacts dropped the most: retail, hotels and restaurants, arts and entertainment and schools. Our results can help quantify the economic costs of social distancing.
Project description:BackgroundImplementing and lifting social distancing (LSD) is an urgent global issue during the COVID-19 pandemic, particularly when the travel ban is lifted to revive international businesses and economies. However, when and whether LSD can be considered is subject to the spread of SARS-CoV-2, the recovery rate, and the case-fatality rate. It is imperative to provide real-time assessment of three factors to guide LSD.ObjectiveA simple LSD index was developed for health decision makers to do real-time assessment of COVID-19 at the global, country, region, and community level.MethodsData on the retrospective cohort of 186 countries with three factors were retrieved from a publicly available repository from January to early July. A simple index for guiding LSD was measured by the cumulative number of COVID-19 cases and recoveries, and the case-fatality rate was envisaged. If the LSD index was less than 1, LSD can be considered. The dynamic changes of the COVID-19 pandemic were evaluated to assess whether and when health decision makers allowed for LSD and when to reimplement social distancing after resurgences of the epidemic.ResultsAfter large-scale outbreaks in a few countries before mid-March (prepandemic phase), the global weekly LSD index peaked at 4.27 in March and lasted until mid-June (pandemic phase), during which most countries were affected and needed to take various social distancing measures. Since, the value of LSD has gradually declined to 0.99 on July 5 (postpandemic phase), at which 64.7% (120/186) of countries and regions had an LSD<1 with the decile between 0 and 1 to refine risk stratification by countries. The LSD index decreased to 1 in about 115 days. In addition, we present the results of dynamic changes of the LSD index for the world and for each country and region with different time windows from January to July 5. The results of the LSD index on the resurgence of the COVID-19 epidemic in certain regions and validation by other emerging infectious diseases are presented.ConclusionsThis simple LSD index provides a quantitative assessment of whether and when to ease or implement social distancing to provide advice for health decision makers and travelers.
Project description:This paper addresses the airplane passengers' seat assignment problem while practicing social distancing among passengers. We proposed a mixed integer programming model to assign passengers to seats on an airplane in a manner that will respect two types of social distancing. One type of social distancing refers to passengers being seated far enough away from each other. The metric for this type of social distancing is how many passengers are seated so close to each other as to increase the risk of infection. The other type of social distancing refers to the distance between seat assignments and the aisle. That distance influences the health risk involved in passengers and crew members walking down the aisle. Corresponding metrics for both health risks are included in the objective function. To conduct simulation experiments, we define different scenarios distinguishing between the relative level of significance of each type of social distancing. The results suggest the seating assignments that best serve the intention of the scenarios. We also reformulate the initial model to determine seat assignments that maximize the number of passengers boarding an airplane while practicing social distancing among passengers. In the last part of this study, we compare the proposed scenarios with the recommended middle-seat blocking policy presently used by some airlines to keep social distancing among passengers. The results show that the proposed scenarios can provide social distancing among seated passengers similar to the middle-seat blocking policy, while reducing the number of passengers seated close to the aisle of an airplane.
Project description:Public policies intended to induce behavioral change, specifically incentives to reduce interpersonal contacts or to "social distance," increasingly play a prominent role in public disease response strategies as governments plan for and respond to major epidemics. I compare social distancing incentives and outcomes under decentralized, full control social planner, and constrained social planner, without health class specific control, decision making scenarios. Constrained social planner decision making, based on non-health class specific controls, can in some instances make society worse off than decentralized decision making (i.e. no intervention). The oft neglected behavior of recovered and immune individuals is important for welfare and health outcomes.
Project description:The "Aging Science Talks: Science for the Community" daily online seminar series was established in reaction to the cancellation of a myriad of regional, national, and international meetings focused on the biology of aging due to the COVID-19 pandemic. The inability to attend scientific meetings has far-reaching implications for our field, as we lose the ability to 1) disseminate both published and non-published data through talks and posters; 2) network and establish new collaborations to produce innovative science in the aging field; and 3) continue the career development of early career researchers (ECRs). Through these virtual seminars, we hope to offset the negative effects of these canceled meetings. We established the program rapidly using a "lean" approach, making use of existing technologies broadly available at academic institutions. Here, we provide an initial description of how this program was developed and implemented. We discuss advantages and limitations of this approach, including "real-time" participation and the creation of an on/off-line community of inquiry (CoI). In the future, we hope to formally evaluate the success of this program in building engagement, creating a community, and enhancing the development of ECRs, and to capture metrics associated with the continued progress of science. Our approach to building a CoI may be applied across multiple scientific disciplines during this time of uncertainty, and may offer a valuable example of how to continue to advance science during pandemics or similar events.
Project description:As the first wave of COVID-19 recedes, policymakers are contemplating the relaxation of shelter-in-place orders. Using a model capturing high-risk populations and transmission rates estimated from hospitalization data, we find that postponing relaxation will only delay a second wave and cocooning vulnerable populations is needed to prevent overwhelming medical surges.
Project description:The COVID-19 pandemic has proved to be one of the most disruptive public health emergencies in recent memory. Among non-pharmaceutical interventions, social distancing and lockdown measures are some of the most common tools employed by governments around the world to combat the disease. While mathematical models of COVID-19 are ubiquitous, few have leveraged network theory in a general way to explain the mechanics of social distancing. In this paper, we build on existing network models for heterogeneous, clustered networks with random link activation/deletion dynamics to put forth realistic mechanisms of social distancing using piecewise constant activation/deletion rates. We find our models are capable of rich qualitative behavior, and offer meaningful insight with relatively few intervention parameters. In particular, we find that the severity of social distancing interventions and when they begin have more impact than how long it takes for the interventions to take full effect.