scholarly journals Social Distancing Associations with COVID-19 Infection and Mortality Are Modified by Crowding and Socioeconomic Status

2021 ◽  
Vol 18 (9) ◽  
pp. 4680
Author(s):  
Trang VoPham ◽  
Matthew D. Weaver ◽  
Gary Adamkiewicz ◽  
Jaime E. Hart
Keyword(s):  
Socioeconomic Status ◽  
Disease Transmission ◽  
Census Data ◽  
Minority Population ◽  
County Level ◽  
Social Distancing ◽  
Interaction Terms ◽  
Incidence And Mortality ◽  
Black Residents ◽  
Household Crowding

The SARS-CoV-2 virus is a public health emergency. Social distancing is a key approach to slowing disease transmission. However, more evidence is needed on its efficacy, and little is known on the types of areas where it is more or less effective. We obtained county-level data on COVID-19 incidence and mortality during the first wave, smartphone-based average social distancing (0–5, where higher numbers indicate more social distancing), and census data on demographics and socioeconomic status. Using generalized linear mixed models with a Poisson distribution, we modeled associations between social distancing and COVID-19 incidence and mortality, and multiplicative interaction terms to assess effect modification. In multivariable models, each unit increase in social distancing was associated with a 26% decrease (p < 0.0001) in COVID-19 incidence and a 31% decrease (p < 0.0001) in COVID-19 mortality. Percent crowding, minority population, and median household income were all statistically significant effect modifiers. County-level increases in social distancing led to reductions in COVID-19 incidence and mortality but were most effective in counties with lower percentages of black residents, higher median household incomes, and with lower levels of household crowding.

scholarly journals Differential Effects of Intervention Timing on COVID-19 Spread in the United States

2020 ◽  
Author(s):  
Sen Pei ◽  
Sasikiran Kandula ◽  
Jeffrey Shaman
Keyword(s):  
United States ◽  
Disease Transmission ◽  
Metropolitan Areas ◽  
Human Mobility ◽  
The United States ◽  
Aggressive Response ◽  
Control Measures ◽  
County Level ◽  
Social Distancing ◽  
Us Counties

Assessing the effects of early non-pharmaceutical interventions1-5 on COVID-19 spread in the United States is crucial for understanding and planning future control measures to combat the ongoing pandemic6-10. Here we use county-level observations of reported infections and deaths11, in conjunction with human mobility data12 and a metapopulation transmission model13,14, to quantify changes of disease transmission rates in US counties from March 15, 2020 to May 3, 2020. We find significant reductions of the basic reproductive numbers in major metropolitan areas in association with social distancing and other control measures. Counterfactual simulations indicate that, had these same control measures been implemented just 1-2 weeks earlier, a substantial number of cases and deaths could have been averted. Specifically, nationwide, 61.6% [95% CI: 54.6%-67.7%] of reported infections and 55.0% [95% CI: 46.1%-62.2%] of reported deaths as of May 3, 2020 could have been avoided if the same control measures had been implemented just one week earlier. We also examine the effects of delays in re-implementing social distancing following a relaxation of control measures. A longer response time results in a stronger rebound of infections and death. Our findings underscore the importance of early intervention and aggressive response in controlling the COVID-19 pandemic.

scholarly journals Interdependence and the cost of uncoordinated responses to COVID-19

2020 ◽  
Vol 117 (33) ◽  
pp. 19837-19843 ◽  
Author(s):  
David Holtz ◽  
Michael Zhao ◽  
Seth G. Benzell ◽  
Cathy Y. Cao ◽  
Mohammad Amin Rahimian ◽  
...  
Keyword(s):  
Social Network ◽  
Local Governments ◽  
Census Data ◽  
The United States ◽  
Federal State ◽  
County Level ◽  
Social Distancing ◽  
Quantitative Evidence ◽  
Regional Policies ◽  
State And Local

Social distancing is the core policy response to coronavirus disease 2019 (COVID-19). But, as federal, state and local governments begin opening businesses and relaxing shelter-in-place orders worldwide, we lack quantitative evidence on how policies in one region affect mobility and social distancing in other regions and the consequences of uncoordinated regional policies adopted in the presence of such spillovers. To investigate this concern, we combined daily, county-level data on shelter-in-place policies with movement data from over 27 million mobile devices, social network connections among over 220 million Facebook users, daily temperature and precipitation data from 62,000 weather stations, and county-level census data on population demographics to estimate the geographic and social network spillovers created by regional policies across the United States. Our analysis shows that the contact patterns of people in a given region are significantly influenced by the policies and behaviors of people in other, sometimes distant, regions. When just one-third of a state’s social and geographic peer states adopt shelter-in-place policies, it creates a reduction in mobility equal to the state’s own policy decisions. These spillovers are mediated by peer travel and distancing behaviors in those states. A simple analytical model calibrated with our empirical estimates demonstrated that the “loss from anarchy” in uncoordinated state policies is increasing in the number of noncooperating states and the size of social and geographic spillovers. These results suggest a substantial cost of uncoordinated government responses to COVID-19 when people, ideas, and media move across borders.

scholarly journals Interdependence and the Cost of Uncoordinated Responses to COVID-19

2020 ◽  
Author(s):  
David Holtz ◽  
Michael Zhao ◽  
Seth G. Benzell ◽  
Cathy Y. Cao ◽  
M. Amin Rahimian ◽  
...  
Keyword(s):  
Social Network ◽  
Local Governments ◽  
Census Data ◽  
The United States ◽  
Federal State ◽  
County Level ◽  
Social Distancing ◽  
Quantitative Evidence ◽  
Regional Policies ◽  
State And Local

Social distancing is the core policy response to COVID-19. But as federal, state and local governments begin opening businesses and relaxing shelter-in-place orders worldwide, we lack quantitative evidence on how policies in one region affect mobility and social distancing in other regions and the consequences of uncoordinated regional policies adopted in the presence of such spillovers. We therefore combined daily, county-level data on shelter-in-place and business closure policies with movement data from over 27 million mobile devices, social network connections among over 220 million of Facebook users, daily temperature and precipitation data from 62,000 weather stations and county-level census data on population demographics to estimate the geographic and social network spillovers created by regional policies across the United States. Our analysis showed the contact patterns of people in a given region are significantly influenced by the policies and behaviors of people in other, sometimes distant, regions. When just one third of a state’s social and geographic peer states adopt shelter in place policies, it creates a reduction in mobility equal to the state’s own policy decisions. These spillovers are mediated by peer travel and distancing behaviors in those states. A simple analytical model calibrated with our empirical estimates demonstrated that the “loss from anarchy” in uncoordinated state policies is increasing in the number of non cooperating states and the size of social and geographic spillovers. These results suggest a substantial cost of uncoordinated government responses to COVID-19 when people, ideas, and media move across borders.

Are social networking platforms the key to effective social distancing for COVID-19? (Preprint)

2020 ◽  
Author(s):  
Viknesh Sounderajah ◽  
Hutan Ashrafian ◽  
Sheraz Markar ◽  
Ara Darzi
Keyword(s):  
Operating System ◽  
Social Networking ◽  
Contact Tracing ◽  
Text Data ◽  
Social Distancing ◽  
Web Mapping ◽  
Viral Spread ◽  
Behavioural Insights ◽  
Mapping Software ◽  
Incidence And Mortality

UNSTRUCTURED If health systems are to effectively employ social distancing measures to in response to further COVID-19 peaks, they must adopt new behavioural metrics that can supplement traditional downstream measures, such as incidence and mortality. Access to mobile digital innovations may dynamically quantify compliance to social distancing (e.g. web mapping software) as well as establish personalised real-time contact tracing of viral spread (e.g. mobile operating system infrastructure through Google-Apple partnership). In particular, text data from social networking platforms can be mined for unique behavioural insights, such as symptom tracking and perception monitoring. Platforms, such as Twitter, have shown significant promise in tracking communicable pandemics. As such, it is critical that social networking companies collaborate with each other in order to (1) enrich the data that is available for analysis, (2) promote the creation of open access datasets for researchers and (3) cultivate relationships with governments in order to affect positive change.

scholarly journals Addressing the COVID-19 transmission in inner Brazil by a mathematical model

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
G. B. Almeida ◽  
T. N. Vilches ◽  
C. P. Ferreira ◽  
C. M. C. B. Fortaleza
Keyword(s):  
Mathematical Model ◽  
Disease Transmission ◽  
Abiotic Factors ◽  
Severe Pneumonia ◽  
Mitigation Strategies ◽  
Social Distancing ◽  
Control Disease ◽  
Set Up ◽  
Novel Coronavirus ◽  
Public Health Strategies

AbstractIn 2020, the world experienced its very first pandemic of the globalized era. A novel coronavirus, SARS-CoV-2, is the causative agent of severe pneumonia and has rapidly spread through many nations, crashing health systems and leading a large number of people to death. In Brazil, the emergence of local epidemics in major metropolitan areas has always been a concern. In a vast and heterogeneous country, with regional disparities and climate diversity, several factors can modulate the dynamics of COVID-19. What should be the scenario for inner Brazil, and what can we do to control infection transmission in each of these locations? Here, a mathematical model is proposed to simulate disease transmission among individuals in several scenarios, differing by abiotic factors, social-economic factors, and effectiveness of mitigation strategies. The disease control relies on keeping all individuals’ social distancing and detecting, followed by isolating, infected ones. The model reinforces social distancing as the most efficient method to control disease transmission. Moreover, it also shows that improving the detection and isolation of infected individuals can loosen this mitigation strategy. Finally, the effectiveness of control may be different across the country, and understanding it can help set up public health strategies.

scholarly journals Self-Rated Health Status Based on the Type of Health Insurance: A Socioeconomic Perspective

2021 ◽  
Vol 58 ◽  
pp. 004695802110281
Author(s):  
Minsung Sohn ◽  
Minsoo Jung ◽  
Mankyu Choi
Keyword(s):  
Socioeconomic Status ◽  
Health Insurance ◽  
Health Status ◽  
Regression Models ◽  
Private Health Insurance ◽  
Public And Private ◽  
Logistic Regression Models ◽  
Self Rated Health ◽  
Interaction Terms ◽  
Healthcare Coverage

To investigate the effects of public and private health insurance on self-rated health (SRH) status within the National Health Insurance (NHI) system based on socioeconomic status in South Korea. The data were obtained from 10 867 respondents of the Korea Health Panel (2008-2011). We used hierarchical panel logistic regression models to assess the SRH status. We also added the interaction terms of socioeconomic status and type of health insurance as moderators. Medical aid (MA) recipients were 2.10 times more likely to have a low SRH status than those who were covered only by the NHI, even though the healthcare utilization was higher. When the interaction terms were included, those not covered by the NHI and had completed elementary school or less were 16.59 times more likely to have a low SRH status than those covered by the NHI and had earned a college degree or higher. Expanding healthcare coverage to reduce the burden of non-payment and unmet use to improve the health status of MA beneficiaries should be considered. Particularly, the vulnerability of less-educated groups should be focused on.

scholarly journals The effect of social distancing on the reach of an epidemic in social networks

2021 ◽  
Author(s):  
Gregory Gutin ◽  
Tomohiro Hirano ◽  
Sung-Ha Hwang ◽  
Philip R. Neary ◽  
Alexis Akira Toda
Keyword(s):  
Public Health ◽  
Infectious Disease ◽  
Social Networks ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Disease Transmission ◽  
Global Level ◽  
Social Distancing ◽  
The Public ◽  
Simulation Results

AbstractHow does social distancing affect the reach of an epidemic in social networks? We present Monte Carlo simulation results of a susceptible–infected–removed with social distancing model. The key feature of the model is that individuals are limited in the number of acquaintances that they can interact with, thereby constraining disease transmission to an infectious subnetwork of the original social network. While increased social distancing typically reduces the spread of an infectious disease, the magnitude varies greatly depending on the topology of the network, indicating the need for policies that are network dependent. Our results also reveal the importance of coordinating policies at the ‘global’ level. In particular, the public health benefits from social distancing to a group (e.g. a country) may be completely undone if that group maintains connections with outside groups that are not following suit.

scholarly journals A versatile web app for identifying the drivers of COVID-19 epidemics

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Wayne M. Getz ◽  
Richard Salter ◽  
Ludovica Luisa Vissat ◽  
Nir Horvitz
Keyword(s):  
Model Fitting ◽  
Likelihood Estimation ◽  
Stochastic Simulations ◽  
Social Distancing ◽  
Epidemic Curve ◽  
Policies And Measures ◽  
Incidence And Mortality ◽  
Credible Intervals ◽  
Web App ◽  
Second Wave

Abstract Background No versatile web app exists that allows epidemiologists and managers around the world to comprehensively analyze the impacts of COVID-19 mitigation. The http://covid-webapp.numerusinc.com/ web app presented here fills this gap. Methods Our web app uses a model that explicitly identifies susceptible, contact, latent, asymptomatic, symptomatic and recovered classes of individuals, and a parallel set of response classes, subject to lower pathogen-contact rates. The user inputs a CSV file of incidence and, if of interest, mortality rate data. A default set of parameters is available that can be overwritten through input or online entry, and a user-selected subset of these can be fitted to the model using maximum-likelihood estimation (MLE). Model fitting and forecasting intervals are specifiable and changes to parameters allow counterfactual and forecasting scenarios. Confidence or credible intervals can be generated using stochastic simulations, based on MLE values, or on an inputted CSV file containing Markov chain Monte Carlo (MCMC) estimates of one or more parameters. Results We illustrate the use of our web app in extracting social distancing, social relaxation, surveillance or virulence switching functions (i.e., time varying drivers) from the incidence and mortality rates of COVID-19 epidemics in Israel, South Africa, and England. The Israeli outbreak exhibits four distinct phases: initial outbreak, social distancing, social relaxation, and a second wave mitigation phase. An MCMC projection of this latter phase suggests the Israeli epidemic will continue to produce into late November an average of around 1500 new case per day, unless the population practices social-relaxation measures at least 5-fold below the level in August, which itself is 4-fold below the level at the start of July. Our analysis of the relatively late South African outbreak that became the world’s fifth largest COVID-19 epidemic in July revealed that the decline through late July and early August was characterised by a social distancing driver operating at more than twice the per-capita applicable-disease-class (pc-adc) rate of the social relaxation driver. Our analysis of the relatively early English outbreak, identified a more than 2-fold improvement in surveillance over the course of the epidemic. It also identified a pc-adc social distancing rate in early August that, though nearly four times the pc-adc social relaxation rate, appeared to barely contain a second wave that would break out if social distancing was further relaxed. Conclusion Our web app provides policy makers and health officers who have no epidemiological modelling or computer coding expertise with an invaluable tool for assessing the impacts of different outbreak mitigation policies and measures. This includes an ability to generate an epidemic-suppression or curve-flattening index that measures the intensity with which behavioural responses suppress or flatten the epidemic curve in the region under consideration.

scholarly journals 503. The Misappropriation of COVID-19 Related TV Ad Expenditures

2020 ◽  
Vol 7 (Supplement_1) ◽  
pp. S317-S317
Author(s):  
Kartavya J Vyas
Keyword(s):  
Cost Effectiveness ◽  
Performance Metrics ◽  
Control Measures ◽  
Mortality Data ◽  
Preventive Interventions ◽  
Social Distancing ◽  
Epidemic Curve ◽  
Incidence And Mortality ◽  
The Cost ◽  
The U.S

Abstract Background With nearly three-fourths of the U.S. population isolated in their homes between early March and the end of May, almost all of whom regularly watch television (TV), it was no surprise that companies began to purchase airtime on major television networks to advertise (ad) their brands and showcase their empathy with the populace. But how would the coronavirus disease 2019 (COVID-19) epidemic curve have changed had these same dollars been allocated to proven preventive interventions? Methods Performance and activity metrics on all COVID-19 related TV ads that have aired in the U.S. between February 26th and June 7th, 2020, were provided by iSpot.tv, Inc., including expenditures. COVID-19 incidence and mortality data were collected from the Centers for Disease Control and Prevention (CDC). Descriptive statistics were performed to calculate total TV ad expenditures and other performance metrics across industry categories. Leveraging a previously published stochastic agent-based model that was used to assess the cost-effectiveness of non-pharmaceutical interventions to control COVID-19, the number of cases that would have been prevented had these same dollars been used for preventive interventions was calculated using cost-effectiveness ratios (CERs), the cost divided by cases prevented. Results A total of 1,513 companies purchased TV airtime during the study period, totaling approximately 1.1 million airings, 215.5 billion impressions, and $2.7 billion in expenditures; most of the expenditures were spent by the restaurant (15.9%), electronics and communications (15.4%), and vehicle (13.7%) industries. The CERs for PPE and social distancing measures were $13,856 and $29,552, respectively; therefore, had all of these TV ad dollars instead been allocated to PPE or social distancing measures, approximately 194,908 and 91,386 cases of COVID-19 may have been prevented by the end of the study period, respectively. Figure 2. COVID-19 cases prevented had TV ad expenditures been reallocated for interventions. Conclusion Americans were inundated with COVID-19 related TV ads during the early months of the pandemic and companies are now showing some signs to relent. In times of disaster, however, it is paramount that the private sector go beyond showcasing their empathy and truly become socially responsible by allocating their funds to proven prevention and control measures. Disclosures All Authors: No reported disclosures

scholarly journals Incarceration and Cancer-Related Outcomes (ICRO) study protocol: using a mixed-methods approach to investigate the role of incarceration on cancer incidence, mortality and quality of care

BMJ Open ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. e048863
Author(s):  
Lisa Puglisi ◽  
Alexandra A Halberstam ◽  
Jenerius Aminawung ◽  
Colleen Gallagher ◽  
Lou Gonsalves ◽  
...  
Keyword(s):  
Socioeconomic Status ◽  
Quality Of Care ◽  
Mixed Methods ◽  
Cancer Incidence ◽  
Cancer Care ◽  
Survival Models ◽  
Structural Racism ◽  
Interaction Terms ◽  
The Relationship

IntroductionIncarceration is associated with decreased cancer screening rates and a higher risk for hospitalisation and death from cancer after release from prison. However, there is a paucity of data on the relationship between incarceration and cancer outcomes and quality of care. In the Incarceration and Cancer-Related Outcomes Study, we aim to develop a nuanced understanding of how incarceration affects cancer incidence, mortality and treatment, and moderates the relationship between socioeconomic status, structural racism and cancer disparities.Methods and analysisWe will use a sequential explanatory mixed-methods study design. We will create the first comprehensive linkage of data from the Connecticut Department of Correction and the statewide Connecticut Tumour Registry. Using the linked dataset, we will examine differences in cancer incidence and stage at diagnosis between individuals currently incarcerated, formerly incarcerated and never incarcerated in Connecticut from 2005 to 2016. Among individuals with invasive cancer, we will assess relationships among incarceration, quality of cancer care and mortality, and will assess the degree to which incarceration status moderates relationships among race, socioeconomic status, quality of cancer care and cancer mortality. We will use multivariable logistic regression and Cox survival models with interaction terms as appropriate. These results will inform our conduct of in-depth interviews with individuals diagnosed with cancer during or shortly after incarceration regarding their experiences with cancer care in the correctional system and the immediate postrelease period. The results of this qualitative work will help contextualise the results of the data linkage.Ethics and disseminationThe Yale University Institutional Review Board (#2000022899) and the Connecticut Department of Public Health Human Investigations Committee approved this study. We will disseminate study findings through peer-reviewed publications and academic and community presentations. Access to the deidentified quantitative and qualitative datasets will be made available on review of the request.

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