Prediction of COVID-19 Social Distancing Adherence (SoDA) on the United States county-level

AbstractSocial distancing policies are currently the best method of mitigating the spread of the COVID-19 pandemic. However, adherence to these policies vary greatly on a county-by-county level. We used social distancing adherence (SoDA) estimated from mobile phone data and population-based demographics/statistics of 3054 counties in the United States to determine which demographics features correlate to adherence on a countywide level. SoDA scores per day were extracted from mobile phone data and aggregated from March 16, 2020 to April 14, 2020. 45 predictor features were evaluated using univariable regression to determine their level of correlation with SoDA. These 45 features were then used to form a SoDA prediction model. Persons who work from home prior to the COVID-19 pandemic (β = 0.259, p < 0.00001) and owner-occupied housing unit rate (β = −0.322, p < 0.00001) were the most positively correlated and negatively correlated features to SoDA, respectively. Counties with higher per capita income, older persons, and more suburban areas were positively associated with adherence while counties with higher African American population, high obesity rate, earlier first COVID-19 case/death, and more Republican-leaning residents were negatively correlated with adherence. The base model predicted county SoDA with 90.8% accuracy. The model using only COVID-19-related features predicted with 64% accuracy and the model using the top 25 most substantial features predicted with 89% accuracy. Our results indicate that economic features, health features, and a few other features, such as political affiliation, race, and the time since the first case/death, impact SoDA on a countywide level. These features, combined, can predict adherence with a high level of confidence. Our prediction model could be utilized to inform health policy planning and potential interventions in areas with lower adherence.

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Predictive Modeling on the Number of Covid-19 Death Toll in the United States Considering the Effects of Coronavirus-Related Changes and Covid-19 Recovered Cases

International Journal of Mathematical Engineering and Management Sciences ◽

10.33889/ijmems.2020.5.6.087 ◽

2020 ◽

Vol 5 (6) ◽

pp. 1140-1155 ◽

Cited By ~ 1

Author(s):

Hoang Pham

Keyword(s):

United States ◽

United Kingdom ◽

Policy Decision ◽

The United States ◽

Social Distancing ◽

Death Toll ◽

The United Kingdom ◽

First Case ◽

Staying At Home ◽

At Home

COVID-19 is caused by a coronavirus called SARS-CoV-2. Many countries around the world implemented their own policies and restrictions designed to limit the spread of Covid-19 in recent months. Businesses and schools transitioned into working and learning remotely. In the United States, many states were under strict orders to stay home at least in the month of April. In recent weeks, there are some significant changes related restrictions include social-distancing, reopening states, and staying-at-home orders. The United States surpassed 2 million coronavirus cases on Monday, June 15, 2020 less than five months after the first case was confirmed in the country. The virus has killed at least 115,000 people in the United States as of Monday, June 15, 2020, according to data from Johns Hopkins University. With the recent easing of coronavirus-related restrictions and changes on business and social activity such as stay-at-home, social distancing since late May 2020 hoping to restore economic and business activities, new Covid-19 outbreaks are on the rise in many states across the country. Some researchers expressed concern that the process of easing restrictions and relaxing stay-at-home orders too soon could quickly surge the number of infected Covid-19 cases as well as the death toll in the United States. Some of these increases, however, could be due to more testing sites in the communities while others may be are the results of easing restrictions due to recent reopening and changed policies, though the number of daily death toll does not appear to be going down in recent days due to Covid-19 in the U.S. This raises the challenging question: • How can policy decision-makers and community leaders make the decision to implement public policies and restrictions and keep or lift staying-at-home orders of ongoing Covid-19 pandemic for their communities in a scientific way? In this study, we aim to develop models addressing the effects of recent Covid-19 related changes in the communities such as reopening states, practicing social-distancing, and staying-at-home orders. Our models account for the fact that changes to these policies which can lead to a surge of coronavirus cases and deaths, especially in the United States. Specifically, in this paper we develop a novel generalized mathematical model and several explicit models considering the effects of recent reopening states, staying-at-home orders and social-distancing practice of different communities along with a set of selected indicators such as the total number of coronavirus recovered and new cases that can estimate the daily death toll and total number of deaths in the United States related to Covid-19 virus. We compare the modeling results among the developed models based on several existing criteria. The model also can be used to predict the number of death toll in Italy and the United Kingdom (UK). The results show very encouraging predictability for the proposed models in this study. The model predicts that 128,500 to 140,100 people in the United States will have died of Covid-19 by July 4, 2020. The model also predicts that between 137,900 and 154,000 people will have died of Covid-19 by July 31, and 148,500 to 169,700 will have died by the end of August 2020, as a result of the SARS-CoV-2 coronavirus that causes COVID-19 based on the Covid-19 death data available on June 13, 2020. The model also predicts that 34,900 to 37,200 people in Italy will have died of Covid-19 by July 4, and 36,900 to 40,400 people will have died by the end of August based on the data available on June 13, 2020. The model also predicts that between 43,500 and 46,700 people in the United Kingdom will have died of Covid-19 by July 4, and 48,700 to 51,900 people will have died by the end of August, as a result of the SARS-CoV-2 coronavirus that causes COVID-19 based on the data available on June 13, 2020. The model can serve as a framework to help policy makers a scientific approach in quantifying decision-makings related to Covid-19 affairs.

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County-Level Social Distancing and Policy Impact in the United States: A Dynamical Systems Model

JMIR Public Health and Surveillance ◽

10.2196/23902 ◽

2020 ◽

Vol 6 (4) ◽

pp. e23902

Author(s):

Kevin L McKee ◽

Ian C Crandell ◽

Alexandra L Hanlon

Keyword(s):

United States ◽

Differential Equation ◽

Population Density ◽

Household Income ◽

The United States ◽

Policy Impact ◽

County Level ◽

Median Household Income ◽

Social Distancing

Background Social distancing and public policy have been crucial for minimizing the spread of SARS-CoV-2 in the United States. Publicly available, county-level time series data on mobility are derived from individual devices with global positioning systems, providing a variety of indices of social distancing behavior per day. Such indices allow a fine-grained approach to modeling public behavior during the pandemic. Previous studies of social distancing and policy have not accounted for the occurrence of pre-policy social distancing and other dynamics reflected in the long-term trajectories of public mobility data. Objective We propose a differential equation state-space model of county-level social distancing that accounts for distancing behavior leading up to the first official policies, equilibrium dynamics reflected in the long-term trajectories of mobility, and the specific impacts of four kinds of policy. The model is fit to each US county individually, producing a nationwide data set of novel estimated mobility indices. Methods A differential equation model was fit to three indicators of mobility for each of 3054 counties, with T=100 occasions per county of the following: distance traveled, visitations to key sites, and the log number of interpersonal encounters. The indicators were highly correlated and assumed to share common underlying latent trajectory, dynamics, and responses to policy. Maximum likelihood estimation with the Kalman-Bucy filter was used to estimate the model parameters. Bivariate distributional plots and descriptive statistics were used to examine the resulting county-level parameter estimates. The association of chronology with policy impact was also considered. Results Mobility dynamics show moderate correlations with two census covariates: population density (Spearman r ranging from 0.11 to 0.31) and median household income (Spearman r ranging from –0.03 to 0.39). Stay-at-home order effects were negatively correlated with both (r=–0.37 and r=–0.38, respectively), while the effects of the ban on all gatherings were positively correlated with both (r=0.51, r=0.39). Chronological ordering of policies was a moderate to strong determinant of their effect per county (Spearman r ranging from –0.12 to –0.56), with earlier policies accounting for most of the change in mobility, and later policies having little or no additional effect. Conclusions Chronological ordering, population density, and median household income were all associated with policy impact. The stay-at-home order and the ban on gatherings had the largest impacts on mobility on average. The model is implemented in a graphical online app for exploring county-level statistics and running counterfactual simulations. Future studies can incorporate the model-derived indices of social distancing and policy impacts as important social determinants of COVID-19 health outcomes.

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

Scientific Reports ◽

10.1038/s41598-020-77751-2 ◽

2020 ◽

Vol 10 (1) ◽

Author(s):

Yixuan Pan ◽

Aref Darzi ◽

Aliakbar Kabiri ◽

Guangchen Zhao ◽

Weiyu Luo ◽

...

Keyword(s):

United States ◽

Real Time ◽

Human Mobility ◽

The United States ◽

Control Measures ◽

Social Distancing ◽

First Case ◽

Mobility Behaviour ◽

Novel Coronavirus ◽

Behaviour Changes

AbstractSince 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.

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Iterative near-term forecasting of the transmission and management of SARS-CoV-2/COVID-19 using social interventions at the county-level in the United States

10.21203/rs.3.rs-96665/v1 ◽

2020 ◽

Author(s):

Kenneth Newcomb ◽

Morgan E. Smith ◽

Rose E. Donohue ◽

Sebastian Wyngaard ◽

Caleb Reinking ◽

...

Keyword(s):

United States ◽

The United States ◽

The State ◽

Data Driven ◽

Effective Control ◽

County Level ◽

Movement Restriction ◽

Social Distancing ◽

Social Interventions ◽

Near Term

Abstract The control of the initial outbreak and spread of SARS-CoV-2/COVID-19 by the implementation of unprecedented population-wide non-pharmaceutical mitigation measures has led to remarkable success in dampening the pandemic globally. With many countries easing or beginning to lift these measures to restart activities presently, concern is growing regarding the impacts that such reopening of societies could have on the subsequent transmission of the virus. While mathematical models of COVID-19 transmission have played important roles in evaluating the general population-level impacts of these measures for curbing virus transmission, a key need is for models that are able to effectively capture the effects of the spatial and social heterogeneities that drive the epidemic dynamics observed at the local community level. Iterative near-term forecasting that uses new incoming epidemiological and social behavioural data to sequentially update locally-applicable transmission models can overcome this gap, potentially leading to better predictions and intervention actions. Here, we present the development of one such data-driven iterative modelling tool based on publically-available data and an extended SEIR model for forecasting SARS-CoV-2 at the county level in the United States, and demonstrate, using data from the state of Florida, how this tool can be used to explore the outcomes of the social measures proposed for containing the course of the pandemic as a result of easing the initially imposed lockdown in the state. We provide comprehensive results showing the use of the locally identified models for accessing the impacts and societal tradeoffs of using specific strategies involving movement restriction, social distancing and mass testing, and conclude that while it is absolutely vital to continue with these measures over the near-term and likely to the end of March 2021 in all counties for containing the ongoing pandemic before less socially-disruptive vaccination strategies come into play, it could be possible to lift the more disruptive movement restriction/social distancing measures by end of December 2020 if these are accompanied by widespread testing and contact tracing. Our findings further show that such intensified social interventions could potentially also bring about the control of the epidemic in low and some medium incidence counties first, supporting the development and deployment of a geographically-phased approach to reopening the economy of Florida. We have made our data-driven forecasting system publicly available for policymakers and health officials to use in their own locales, with the hope that a more efficient coordinated strategy for controlling SARS-CoV-2 state-wide, based on effective control of viral transmission at the county level, can be developed and successfully implemented.

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

10.1101/2020.05.15.20103655 ◽

2020 ◽

Cited By ~ 56

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.

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A County-Level Susceptibility Index and Coronavirus Disease 2019 Mortality in the United States: A Socioecological Study

10.1101/2020.07.04.20146084 ◽

2020 ◽

Cited By ~ 2

Author(s):

Sadiya S. Khan ◽

Megan E. McCabe ◽

Amy E. Krefman ◽

Lucia C. Petito ◽

Xiaoyun Yang ◽

...

Keyword(s):

United States ◽

Latent Class ◽

The United States ◽

County Level ◽

Linear Regression Models ◽

Susceptibility Index ◽

First Case ◽

Per Capita ◽

Multiple Domains ◽

Increased Susceptibility

ABSTRACTAs of June 2020, the United States (US) has experienced the highest number of deaths related to coronavirus disease 2019 (Covid-19) in the world, but significant geographic heterogeneity exists at the county-level. Therefore, we sought to classify counties in the United States across multiple domains utilizing a socioecological framework and examine the association between these county-level groups and Covid-19 mortality. We harmonized and linked county-level sociodemographic, health, and environmental metrics associated with increased susceptibility for Covid-19 mortality. Latent class analysis defined a county-level susceptibility index (CSI) based on these metrics (n=2701 counties). Next, we used linear regression models to estimate the associations of the CSI and Covid-19 deaths per capita and initial mortality doubling time (as of 6/2/20), adjusted for days since first Covid-19 case. We identified 4 groups classified by the CSI with distinct sociodemographic, health, and environmental profiles and widespread geographic dispersion. Covid-19 deaths per capita were significantly higher in the group consisting of rural, vulnerable counties (55.8 [95% CI 50.3-61.2] deaths per 100,000) compared with the group with diverse, urban counties (32.2 [27.3-37.0]) at similar points in the outbreak (76 days since first case). Our findings can inform equitable resource allocation for Covid-19 to allow targeted public health preparedness and response in vulnerable counties.

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Social Distancing is Effective at Mitigating COVID-19 Transmission in the United States

10.1101/2020.05.07.20092353 ◽

2020 ◽

Cited By ~ 5

Author(s):

Hamada S. Badr ◽

Hongru Du ◽

Max Marshall ◽

Ensheng Dong ◽

Marietta Squire ◽

...

Keyword(s):

United States ◽

Large Scale ◽

Complex Dynamics ◽

Case Reports ◽

Human Mobility ◽

Local Level ◽

The United States ◽

Mobile Phone Data ◽

Variable Intensity ◽

Social Distancing

AbstractCOVID-19 is present in every state and over 90 percent of all counties in the United States. Decentralized government efforts to reduce spread, combined with the complex dynamics of human mobility and the variable intensity of local outbreaks makes assessing the effect of large-scale social distancing on COVID-19 transmission in the U.S.a challenge. We generate a novel metric to represent social distancing behavior derived from mobile phone data and examine its relationship with COVID-19 case reports at the county level. Our analysis reveals that social distancing is strongly correlated with decreased COVID-19 case growth rates for the 25 most affected counties in the United States, with a lag period consistent with the incubation time of SARS-CoV-2. We also demonstrate evidence that social distancing was already under way in many U.S. counties before state or local-level policies were implemented. This study strongly supports social distancing as an effective way to mitigate COVID-19 transmission in the United States.

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2797. Rates of Respiratory Syncytial Virus (RSV) Infection among Hospitalized Adults by Congestive Heart Failure Status—United States, 2015–2017

Open Forum Infectious Diseases ◽

10.1093/ofid/ofz360.2474 ◽

2019 ◽

Vol 6 (Supplement_2) ◽

pp. S988-S989

Author(s):

Stephanie A Kujawski ◽

Gayle Langley ◽

Evan J Anderson ◽

Ann Thomas ◽

...

Keyword(s):

United States ◽

Heart Failure ◽

Congestive Heart Failure ◽

Respiratory Syncytial Virus ◽

Catchment Area ◽

The United States ◽

Population Based ◽

County Level ◽

Rsv Infection ◽

Syncytial Virus

Abstract Background Respiratory syncytial virus (RSV) can cause severe disease in older adults and adults with cardiopulmonary conditions, such as congestive heart failure (CHF). RSV vaccines in development may target adults based on age or medical conditions. We assessed rates of RSV infection in hospitalized adults by CHF status using RSV surveillance conducted through the Centers for Disease Control and Prevention’s Emerging Infections Program, a population-based platform in the United States Methods RSV surveillance was performed during two seasons (2015–2017) from October 1–April 30 at seven US sites covering an annual catchment population up to 13.7 million adults. Adults (≥ 18 years) admitted to a hospital from the catchment area and with laboratory-confirmed RSV infections identified by clinician-directed testing were included. Demographic data and any history of CHF were abstracted from medical charts. For adults ≥ 65 years, county-level CHF prevalence was obtained from 2015 Centers for Medicare and Medicaid Services (CMS) data. To estimate county-level CHF prevalence for adults < 65 years, we used 2015–2016 National Health and Nutrition Examination Survey and CMS data. We calculated crude incidence rates (and 95% exact Poisson confidence intervals) of RSV by CHF status and age group (< 65 years vs. ≥ 65 years) using RSV cases (numerator) and catchment area county-level population estimates from the US Census (denominator). Results During 2015–2017, a total of 2,211 hospitalized RSV cases were identified; 2,055 (92.9%) had CHF status documented. The majority were ≥ 65 years (n = 1236, 60.1%) and 26.8% (n = 550) had CHF. The crude rate of RSV was 62.7 (95% CI: 57.5–68.2) per 100,000 population in adults with CHF compared with 6.1 (95% CI: 5.7–6.4) per 100,000 population in adults without CHF (rate ratio: 10.3, 95% CI: 9.3–11.3). In both age groups, those with CHF had higher rates of RSV than those without CHF. Rates were highest in adults ≥ 65 years with CHF (73.4 per 100,000 population, 95% CI: 66.4–80.9). Conclusion Using population-based surveillance, we found that adults with CHF had RSV hospitalization rates 10 times higher than those without CHF. Identifying high-risk populations for RSV infection are critical to inform clinical practice and future RSV vaccine policy. Disclosures All authors: No reported disclosures.

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Predictive Modeling on the Number of Covid-19 Death Toll in the United States Considering the Effects of Coronavirus-Related Changes and Covid-19 Recovered Cases

10.1101/2020.06.15.20132357 ◽

2020 ◽

Cited By ~ 1

Author(s):

Hoang Pham

Keyword(s):

United States ◽

United Kingdom ◽

The United States ◽

List Type ◽

Social Distancing ◽

Death Toll ◽

The United Kingdom ◽

First Case ◽

Staying At Home ◽

At Home

AbstractCOVID-19 is caused by a coronavirus called SARS-CoV-2. Many countries around the world implemented their own policies and restrictions designed to limit the spread of Covid-19 in recent months. Businesses and schools transitioned into working and learning remotely. In the United States, many states were under strict orders to stay home at least in the month of April. In recent weeks, there are some significant changes related restrictions include social-distancing, reopening states, and staying-at-home orders. The United States surpassed 2 million coronavirus cases on Monday, June 15, 2020 less than five months after the first case was confirmed in the country. The virus has killed at least 115,000 people in the United States as of Monday, June 15, 2020, according to data from Johns Hopkins University.With the recent easing of coronavirus-related restrictions and changes on business and social activity such as stay-at-home, social distancing since late May 2020 hoping to restore economic and business activities, new Covid-19 outbreaks are on the rise in many states across the country. Some researchers expressed concern that the process of easing restrictions and relaxing stay-at-home orders too soon could quickly surge the number of infected Covid-19 cases as well as the death toll in the United States. Some of these increases, however, could be due to more testing sites in the communities while others may be are the results of easing restrictions due to recent reopening and changed policies, though the number of daily death toll does not appear to be going down in recent days due to Covid-19 in the U.S. This raises the challenging question: How can policy decision-makers and community leaders make the decision to implement public policies and restrictions and keep or lift staying-at-home orders of ongoing Covid-19 pandemic for their communities in a scientific way?In this study, we aim to develop models addressing the effects of recent Covid-19 related changes in the communities such as reopening states, practicing social-distancing, and staying-at-home orders. Our models account for the fact that changes to these policies which can lead to a surge of coronavius cases and deaths, especially in the United States. Specifically, in this paper we develop a novel generalized mathematical model and several explicit models considering the effects of recent reopening states, staying-at-home orders and social-distancing practice of different communities along with a set of selected indicators such as the total number of coronavirus recovered and new cases that can estimate the daily death toll and total number of deaths in the United States related to Covid-19 virus. We compare the modeling results among the developed models based on several existing criteria. The model also can be used to predict the number of death toll in Italy and the United Kingdom (UK). The results show very encouraging predictability for the proposed models in this study.The model predicts that 128,500 to 140,100 people in the United States will have died of Covid-19 by July 4, 2020. The model also predicts that between 137,900 and 154,000 people will have died of Covid-19 by July 31, and 148,500 to 169,700 will have died by the end of August 2020, as a result of the SARS-CoV-2 coronavirus that causes COVID-19 based on the Covid-19 death data available on June 13, 2020.The model also predicts that 34,900 to 37,200 people in Italy will have died of Covid-19 by July 4, and 36,900 to 40,400 people will have died by the end of August based on the data available on June 13, 2020. The model also predicts that between 43,500 and 46,700 people in the United Kingdom will have died of Covid-19 by July 4, and 48,700 to 51,900 people will have died by the end of August, as a result of the SARS-CoV-2 coronavirus that causes COVID-19 based on the data available on June 13, 2020.The model can serve as a framework to help policy makers a scientific approach in quantifying decision-makings related to Covid-19 affairs.

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U.S. county level analysis to determine If social distancing slowed the spread of COVID-19

Revista Panamericana de Salud Pública ◽

10.26633/rpsp.2020.90 ◽

2020 ◽

Vol 44 ◽

pp. 1

Author(s):

Tannista Banerjee ◽

Arnab Nayak

Keyword(s):

United States ◽

Cell Phone ◽

The United States ◽

County Level ◽

Difference In Difference ◽

Social Distancing ◽

The Public ◽

Risks And Benefits ◽

Level Analysis ◽

The U.S

Objective. To analyze the effectiveness of social distancing in the United States (U.S.). Methods. A novel cell-phone ping data was used to quantify the measures of social distancing by all U.S. counties. Results. Using a difference-in-difference approach results show that social distancing has been effective in slowing the spread of COVID-19. Conclusions. As policymakers face the very difficult question of the necessity and effectiveness of social distancing across the U.S., counties where the policies have been imposed have effectively increased social distancing and have seen slowing the spread of COVID-19. These results might help policymakers to make the public understand the risks and benefits of the lockdown.

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