Social Distancing Index Research Articles

Forecasting COVID-19 Cases Based on Social Distancing in Maryland, USA: A Time-Series Approach.

Our objective is to forecast the number of coronavirus disease 2019 (COVID-19) cases in the state of Maryland, United States, using transfer function time series (TS) models based on a Social Distancing Index (SDI) and determine how their parameters relate to the pandemic mechanics. A moving window of 2 mo was used to train the transfer function TS model that was then tested on the next week data. After accounting for a secular trend and weekly cycle of the SDI, a high correlation was documented between it and the daily caseload 9 days later. Similar patterns were also observed on the daily COVID-19 cases and incorporated in our models. In most cases, the proposed models provide a reasonable performance that was, on average, moderately better than that delivered by TS models based only on previous observations. The model coefficients associated with the SDI were statistically significant for most of the training/test sets. Our proposed models that incorporate SDI can forecast the number of COVID-19 cases in a region. Their parameters have real-life interpretations and, hence, can help understand the inner workings of the epidemic. The methods detailed here can help local health governments and other agencies adjust their response to the epidemic.

Vaccine-mediated exit strategies from England’s Covid-19 lockdown

An assessment is made of vaccine-mediated exit strategies from England’s Covid-19 lockdown. Two linked methods are considered. The first, termed the gradualist approach, assumes that the R-rate is controlled to a value close to 1.0 and that the social distancing index (SDI), which measures the nation’s level of interpersonal contact, is allowed to increase continuously to the point where it can grow no further and society and the economy are fully open. The second method, termed the two-step strategy, adopts the gradualist approach initially, but then, after a certain time, the SDI is stepped to its maximum value, which implies the immediate removal of all restrictions. It is found that, while vaccination-generated immunity makes a very valuable contribution to overall immunity, the other components—prior T-cell immunity and immunity generated by infection—are just as important. Infection-generated immunity needs to be the largest component if all restrictions are to be removed, under both strategies. Leaving lockdown to the point where all restrictions can be fully eased requires a narrow path to be followed during the spring and early summer of 2021, keeping the R-rate in a central band around 1.0. Close control of the R-rate is needed and this will require it to be measured accurately, continuously and rapidly. The gradualist approach might allow all restrictions to be lifted by the end of the summer 2021, while the two-step strategy might offer the prospect of full derestriction by the end of May 2021. © 2021 Collegium Basilea.

Public Compliance With Social Distancing Measures and SARS-CoV-2 Spread : A Quantitative Analysis of 5 States.

We quantified the association between public compliance with social distancing measures and the spread of SARS-CoV-2 during the first wave of the epidemic (March-May 2020) in 5 states that accounted for half of the total number of COVID-19 cases in the United States. We used data on mobility and number of COVID-19 cases to longitudinally estimate associations between public compliance, as measured by human mobility, and the daily reproduction number and daily growth rate during the first wave of the COVID-19 epidemic in California, Illinois, Massachusetts, New Jersey, and New York. The 5 states mandated social distancing directives during March 19-24, 2020, and public compliance with mandates started to decrease in mid-April 2020. As of May 31, 2020, the daily reproduction number decreased from 2.41-5.21 to 0.72-1.19, and the daily growth rate decreased from 0.22-0.77 to -0.04 to 0.05 in the 5 states. The level of public compliance, as measured by the social distancing index (SDI) and daily encounter-density change, was high at the early stage of implementation but decreased in the 5 states. The SDI was negatively associated with the daily reproduction number (regression coefficients range, -0.04 to -0.01) and the daily growth rate (from -0.009 to -0.01). The daily encounter-density change was positively associated with the daily reproduction number (regression coefficients range, 0.24 to 1.02) and the daily growth rate (from 0.05 to 0.26). Social distancing is an effective strategy to reduce the incidence of COVID-19 and illustrates the role of public compliance with social distancing measures to achieve public health benefits.

Can social media data be used to evaluate the risk of human interactions during the COVID-19 pandemic?

The U.S. has taken multiple measures to contain the spread of COVID-19, including the implementation of lockdown orders and social distancing practices. Evaluating social distancing is critical since it reflects the risk of close human interactions. While questionnaire surveys or mobility data-based systems have provided valuable insights, social media data can contribute as an additional instrument to help monitor the risk of human interactions during the pandemic. For this reason, this study introduced a social media-based approach that quantifies the pro/anti-lockdown ratio as an indicator of the risk of human interactions. With the aid of natural language processing and machine learning techniques, this study classified the lockdown-related tweets and quantified the pro/anti-lockdown ratio for each state over time. The anti-lockdown ratio showed a moderate and negative correlation with the state-level social distancing index on a weekly basis, suggesting that people are more likely to travel out of the state where the higher anti-lockdown level is observed. The study further showed that the perception expressed on social media could reflect people's behaviors. The findings of the study are of significance for government agencies to assess the risk of close human interactions and to evaluate their policy effectiveness in the context of social distancing and lockdown.

Insufficient social distancing may contribute to COVID-19 outbreak: The case of Ijuí city in Brazil.

The coronavirus disease that emerged in 2019 (COVID-19) is highly contagious and has given way to a global pandemic. A present COVID-19 has high transmission rates worldwide, including in small Brazilian cities such as Ijuí. Located in the northwest part of the state of Rio Grande do Sul (RS) and with a population of 83,475, Ijuí was selected as the site of a population-based survey involving 2,222 subjects, from April to June 2020. Subjects were tested for the presence of antibodies against coronavirus (SARS-CoV-2) and answered questions regarding social distance adherence (SDA), daily preventive routines (DPR), comorbidities, and sociodemographic characteristics. In parallel, the local government registered the official COVID-19 cases in Ijuí, as well as the mobile social distancing index (MSDI). In this study, we demonstrate that there was a decrease in the levels of SDA, DPR and MSDI before the beginning of COVID-19 community transmission in Ijuí. Furthermore, we provide predictions for the number of COVID-19 cases, hospitalizations, and deaths in the city. We conclude that insufficient social distancing, as evidenced by different methods, may be related to the rapid increase of COVID-19 cases in Ijuí. Our study predicts an approaching outbreak of COVID-19 in Ijuí through community spread, which could be avoided or attenuated with increased levels of social distancing among the population.

Quantifying human mobility behaviour changes during the COVID-19 outbreak in the United States

Since the first case of the novel coronavirus disease (COVID-19) was confirmed in Wuhan, China, social distancing has been promoted worldwide, including in the United States, as a major community mitigation strategy. However, our understanding remains limited in how people would react to such control measures, as well as how people would resume their normal behaviours when those orders were relaxed. We utilize an integrated dataset of real-time mobile device location data involving 100 million devices in the contiguous United States (plus Alaska and Hawaii) from February 2, 2020 to May 30, 2020. Built upon the common human mobility metrics, we construct a Social Distancing Index (SDI) to evaluate people’s mobility pattern changes along with the spread of COVID-19 at different geographic levels. We find that both government orders and local outbreak severity significantly contribute to the strength of social distancing. As people tend to practice less social distancing immediately after they observe a sign of local mitigation, we identify several states and counties with higher risks of continuous community transmission and a second outbreak. Our proposed index could help policymakers and researchers monitor people’s real-time mobility behaviours, understand the influence of government orders, and evaluate the risk of local outbreaks.

The impact of social stratification on morbidity during the COVID-19 pandemic

PurposeSocioeconomic status (SES) has been known to be associated with many aspects of social life such as health. We argue that social stratification remains relevant in understanding differentials in health during a pandemic, as countries globally have encouraged or enforced social distancing and remote work measures.Design/methodology/approachBy examining data sources and news reports on the COVID-19 pandemic, we aim to highlight the relationship between SES and morbidity, through the ability to adopt social distancing measures and work remotely. Utilizing publicly available data from the Maryland Transport Institute and the US Census, we performed linear regressions on median income, social distancing index and percentage of individuals working from home.FindingsIndividuals with higher SES are more likely to have jobs that provide opportunities for remote work to be performed, which allows for social distancing. Comparatively, individuals with lower SES are more likely to be involved in jobs that cannot be performed remotely. The linear regression models suggest a positive moderate and significant correlation between median income and social distancing index (R2 = 0.4981, p-value < 0.001), and a positive weak and significant correlation between median income and remote work (R2 = 0.2460, p-value < 0.001).Research limitations/implicationsGovernments need to account for SES in policymaking to reduce inequalities in health.Originality/valueThe paper aims to improve the understanding of social stratification and morbidity through examining data on the COVID-19 pandemic.

Análise dos indicadores da COVID-19 no Nordeste brasileiro em quatro meses de pandemia

Introduction: The novel coronavirus or SARS-CoV-2 came up in the end of 2019 in China and quickly spread, resulting in a global outbreak. The COVID-19 (name given to the disease caused by the virus) pandemic was declared by WHO in March and surpassed the mark of one million cases and fifty thousand deaths in Brazil after four months since the first case was confirmed. Objective: The paper aimed to analyze the evolution of COVID-19 indicators in the Brazilian Northeast and correlate it to the social distancing levels in each northeastern state. Method: It is a traversal study with data collected from epidemiological panels of the Health Ministry of Brazil and the University of São Paulo about cases and deaths numbers and from the geolocation’s company Inloco that calculated the social distancing levels. The analyzed variables were incidence rate, mortality rate, lethality coefficient and social distancing index. Results: The indicators are interdependent and there is certain correlation between the social distancing levels and incidence and mortality rates – they were weak or moderate. Conclusions: Differences between northeastern states, mainly social ones, reflect in their indicators and in the pandemic impact in each of them. It is a challenge proposed for health gestors.

The effect of social distance measures on COVID-19 epidemics in Europe: an interrupted time series analysis

Following the introduction of unprecedented “stay-at-home” national policies, the COVID-19 pandemic recently started declining in Europe. Our research aims were to characterize the changepoint in the flow of the COVID-19 epidemic in each European country and to evaluate the association of the level of social distancing with the observed decline in the national epidemics. Interrupted time series analyses were conducted in 28 European countries. Social distance index was calculated based on Google Community Mobility Reports. Changepoints were estimated by threshold regression, national findings were analyzed by Poisson regression, and the effect of social distancing in mixed effects Poisson regression model. Our findings identified the most probable changepoints in 28 European countries. Before changepoint, incidence of new COVID-19 cases grew by 24% per day on average. From the changepoint, this growth rate was reduced to 0.9%, 0.3% increase, and to 0.7% and 1.7% decrease by increasing social distancing quartiles. The beneficial effect of higher social distance quartiles (i.e., turning the increase into decline) was statistically significant for the fourth quartile. Notably, many countries in lower quartiles also achieved a flat epidemic curve. In these countries, other plausible COVID-19 containment measures could contribute to controlling the first wave of the disease. The association of social distance quartiles with viral spread could also be hindered by local bottlenecks in infection control. Our results allow for moderate optimism related to the gradual lifting of social distance measures in the general population, and call for specific attention to the protection of focal micro-societies enriching high-risk elderly subjects, including nursing homes and chronic care facilities.

Mobility and Engagement Following the SARS-Cov-2 Outbreak

We develop a Social Distancing Index (SDI) based on a range of mobility metrics from Safegraph geolocation data, and validate the index with mobility data from Google and Unacast. We construct SDIs at the county, MSA, state and nationwide level, and link these measures to indicators of economic activity. According to our measures, the bulk of social distancing occurred during the week of March 15 and simultaneously across the U.S. At the national peak of social distancing in early April, localities that engaged in 10% more social distancing than average saw an additional 0.6% of their populations claiming unemployment insurance, an additional 2.8 pp reduction in small businesses employment, an additional 2.6 pp increase in small business closures, and an additional 3.2 pp reduction in new-business applications. A gradual and broad-based reduction in social distancing started in the third week of April.

Monitoring Social Distancing and SARS-CoV-2 Transmission in Brazil Using Cell Phone Mobility Data

BACKGROUND: In the absence of vaccines and effective pharmacological interventions to reduce with the COVID-19 transmission, social distancing measures have been implemented to mitigate the impact on healthcare systems and secure time to prepare the public health response. METHODS: We assessed the relationship between mobility data collected by mobile phone and time-dependent reproduction number R(t) using severe acute respiratory illness cases reported by 102 Brazilian cities with COVID-19 confirmed cases until April 15, grouped by demographic density (low, intermediate and high). FINDINGS: The mean social distancing index from February 1 to April 15 was 43.6% (27.2% to 63.7%), and no significant difference observed comparing the groups of demographic density (p-value = 0.809). The social distancing index measure obtained from mobility data was able to predict future values of R(t) in all groups of demographic density. Furthermore, using SARI cases, cross-correlation analyses showed that isolation was highly correlated with R(t) (ccfINTERPRETATION: The early implementation of social distancing measures greatly reduced the COVID-19 spread. A major advantage to our approach is that the social distancing index data is available on a daily basis, in contrast with R(t) measurement, which is subject to significant delays. This index metric can be monitored in real time to assess adherence to social distancing measures and help guide, with real time data, the public health policy decision making process.FUNDING: FADQ and JC were granted a fellowship for research productivity from the Brazilian National Council for Scientific and Technological Development – CNPq, process/contract identification: 312656/2019-0 and 310551/2018-8, respectively.DECLARATION OF INTERESTS: Authors declare no competing interests.ETHICS APPROVAL STATEMENT: This study followed Brazilian and International legislation for conducting human research. This research project was approved by the National Research Ethics Committee (Comissão Nacional de Ética em Pesquisa, CONEP) in Brazil, Register number (CAAE): 11946619.5.0000.5421.

An internet-based survey in Japan concerning social distance and stigmatization toward the mentally ill among doctors, nurses, pharmacists, and the general public.

Stigma associated with psychiatric disorders tends to be manifested as negative attitudes or behavior toward the mentally ill. It has negative influences, such as leading to difficulty in establishing trust-based relationships and interfering with medical treatment. In order to reduce such stigma, it is necessary to clarify its extent and characteristics in healthcare professionals. Considering this, an Internet-based questionnaire survey was conducted, involving doctors (n = 186), nurses (n = 161), and pharmacists (n = 192) in comparison with the general public (n = 331), and using the Whatley Social Distance Scale (WSDS) and Index of Attitudes toward the Mentally Ill (IATM) as stigma-related indices. Median total WSDS scores and interquartile range were as follows: doctors: 15.5(12.0-18.0), nurses: 14.0(12.0-16.5), pharmacists: 15.0(13.0-17.0), and the general public: 16.0(13.0-18.0). Similarly, median IATM scores were as follows: doctors: 39.0(36.0-42.0), nurses: 39.0(37.0-43.0), pharmacists: 40.0(36.0-42.0), and the general public: 37.0(33.0-41.0). IATM scores were significantly higher in the professional groups than the general public group. Both healthcare professionals and the general public with prior exposure to mental illness were more favorable attitudes toward the mentally ill. Especially among healthcare professionals, they working in psychiatric departments were more favorable attitudes. These results suggest that the stigma of healthcare professionals toward the mentally ill was shown to have a smaller and relatively favorable attitude than that of the general public. In order to correct the stigma it was suggested that a good contact experience with the patient such as work and training in psychiatry is effective.

Multidimensional Polarization, Social Classes, and Societal Conflict: Evidence from Medieval Towns

&lt;p&gt;The paper focuses on the nature of a population distribution (polarized or not) and its possible influence on societal conflict. Despite theoretical and empirical studies on the link between population’s polarization and social conflict, the relationship remains in question. Up to now, the role of a multidimensional polarization has been neglected and the determination of social classes by their roles and functions (and not by their resource level) has been ignored. To extend the research, we first define a multidimensional polarization index and approach it empirically through quantitative and qualitative data (often textual data) over a very long period in accordance with the historiographical method. First, this paper refutes the stereotype of a medieval French urban population polarized between rich and poor. Second, over the same period, we build a database of the intensity and occurrence of societal conflict on a sample of twenty-four French towns. The paper finds that over time the low initial degree of the population’s polarization continued to decline while societal violence was increasing. Third, whereas polarization is excluded as a determinant of societal conflict, the inter-group heterogeneity measure (or social distance) highlights some relationships. The results show that societal upheavals may be quite connected with the social distance index defined between the high and middle classes; moreover, this social unrest may be greatly related with the index defined between the high and the low classes. By contrast, the results find an outbreak of societal conflicts when social distances between the middle and low classes decrease.&lt;/p&gt;

The role of social distance in sharecropping efficiency

PurposeThe purpose of this paper is to gain a better understanding of the role of culture in general, and social distance in particular, in influencing the choice and efficiency of various contractual modes in developing country agriculture. It aims to focus on sharecropping, but the model of social distance can be applied to any contract, mainly those in close‐knit village societies.Design/methodology/approachPrincipal components analysis (PCA) is used in the study to develop a social distance index for all sharecroppers, which is included as an independent variable in land productivity ordinary least squares (OLS) regressions.FindingsFindings indicate social distance is a key determinant in sharecropping efficiency for marginal tenant farmers in rural Nepal. Specifically, social distance is found to be a significant factor in explaining land productivity differentials between owned land and sharecropped land.Research limitations/implicationsFuture research hopes to see whether social distance is also a significant factor in the efficiency and choice of bonded labor contracts. It intends to use simple OLS regressions for sharecroppers and bonded laborers separately in which: input use and land productivity are separate dependent variables, and the various factors or proxies of social distance are independent variables to test for their particular impact; each type of contract is the dependent variable to see the extent to which social distance affects the choice of tenancy; and social distance is the dependent variable so one can see the specific impact of different proxies. Given the small sample (although representative), the strong results in this paper are limited.Practical implicationsFrom a policy standpoint, the results suggest that a relatively egalitarian agrarian structure, insofar as it results in lower social distances among parties to land and labor contracts, would have a positive impact on productivity. Therefore, the object of agrarian reforms should not be to alter or constrain the form of contracts (for example by banning sharecropping) but rather to improve the social relations among contracting parties.Originality/valueThis paper is original and provides value in three ways: a conceptually and theoretically innovative model that explains sharecropping efficiency independent of standard explanations of market imperfections, transaction costs, and risk; in developing a new measure of social distance that allows the data to determine the weights of the independent variables in constructing social distance; and to see the need to more importantly study the changing social relations on which contracts are based and are often only one element of.