scholarly journals County-Level Social Distancing and Policy Impact in the United States: A Dynamical Systems Model (Preprint)

2020 ◽  
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 <i>r</i> ranging from 0.11 to 0.31) and median household income (Spearman <i>r</i> ranging from –0.03 to 0.39). Stay-at-home order effects were negatively correlated with both (<i>r</i>=–0.37 and <i>r</i>=–0.38, respectively), while the effects of the ban on all gatherings were positively correlated with both (<i>r</i>=0.51, <i>r</i>=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.

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

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.

scholarly journals Diverse local epidemics reveal the distinct effects of population density, demographics, climate, depletion of susceptibles, and intervention in the first wave of COVID-19 in the United States

2020 ◽  
Author(s):  
Niayesh Afshordi ◽  
Benjamin Holder ◽  
Mohammad Bahrami ◽  
Daniel Lichtblau
Keyword(s):  
United States ◽  
Population Density ◽  
The United States ◽  
Effective Response ◽  
Susceptible Population ◽  
Local Climate ◽  
Climate Conditions ◽  
Mortality And Morbidity ◽  
Social Distancing ◽  
The Impact

The SARS-CoV-2 pandemic has caused significant mortality and morbidity worldwide, sparing almost no community. As the disease will likely remain a threat for years to come, an understanding of the precise influences of human demographics and settlement, as well as the dynamic factors of climate, susceptible depletion, and intervention, on the spread of localized epidemics will be vital for mounting an effective response. We consider the entire set of local epidemics in the United States; a broad selection of demographic, population density, and climate factors; and local mobility data, tracking social distancing interventions, to determine the key factors driving the spread and containment of the virus. Assuming first a linear model for the rate of exponential growth (or decay) in cases/mortality, we find that population-weighted density, humidity, and median age dominate the dynamics of growth and decline, once interventions are accounted for. A focus on distinct metropolitan areas suggests that some locales benefited from the timing of a nearly simultaneous nationwide shutdown, and/or the regional climate conditions in mid-March; while others suffered significant outbreaks prior to intervention. Using a first-principles model of the infection spread, we then develop predictions for the impact of the relaxation of social distancing and local climate conditions. A few regions, where a significant fraction of the population was infected, show evidence that the epidemic has partially resolved via depletion of the susceptible population (i.e., “herd immunity”), while most regions in the United States remain overwhelmingly susceptible. These results will be important for optimal management of intervention strategies, which can be facilitated using our online dashboard.

scholarly journals Exposure to air pollution and COVID-19 mortality in the United States: A nationwide cross-sectional study

2020 ◽  
Author(s):  
Xiao Wu ◽  
Rachel C Nethery ◽  
M Benjamin Sabath ◽  
Danielle Braun ◽  
Francesca Dominici
Keyword(s):  
United States ◽  
Air Pollution ◽  
Death Rate ◽  
The United States ◽  
Sensitivity Analyses ◽  
List Type ◽  
County Level ◽  
Cross Sectional ◽  
Increased Risk

AbstractObjectivesUnited States government scientists estimate that COVID-19 may kill tens of thousands of Americans. Many of the pre-existing conditions that increase the risk of death in those with COVID-19 are the same diseases that are affected by long-term exposure to air pollution. We investigated whether long-term average exposure to fine particulate matter (PM2.5) is associated with an increased risk of COVID-19 death in the United States.DesignA nationwide, cross-sectional study using county-level data.Data sourcesCOVID-19 death counts were collected for more than 3,000 counties in the United States (representing 98% of the population) up to April 22, 2020 from Johns Hopkins University, Center for Systems Science and Engineering Coronavirus Resource Center.Main outcome measuresWe fit negative binomial mixed models using county-level COVID-19 deaths as the outcome and county-level long-term average of PM2.5 as the exposure. In the main analysis, we adjusted by 20 potential confounding factors including population size, age distribution, population density, time since the beginning of the outbreak, time since state’s issuance of stay-at-home order, hospital beds, number of individuals tested, weather, and socioeconomic and behavioral variables such as obesity and smoking. We included a random intercept by state to account for potential correlation in counties within the same state. We conducted more than 68 additional sensitivity analyses.ResultsWe found that an increase of only 1 μg/m3 in PM2.5 is associated with an 8% increase in the COVID-19 death rate (95% confidence interval [CI]: 2%, 15%). The results were statistically significant and robust to secondary and sensitivity analyses.ConclusionsA small increase in long-term exposure to PM2.5 leads to a large increase in the COVID-19 death rate. Despite the inherent limitations of the ecological study design, our results underscore the importance of continuing to enforce existing air pollution regulations to protect human health both during and after the COVID-19 crisis. The data and code are publicly available so our analyses can be updated routinely.Summary BoxWhat is already known on this topicLong-term exposure to PM2.5 is linked to many of the comorbidities that have been associated with poor prognosis and death in COVID-19 patients, including cardiovascular and lung disease.PM2.5 exposure is associated with increased risk of severe outcomes in patients with certain infectious respiratory diseases, including influenza, pneumonia, and SARS.Air pollution exposure is known to cause inflammation and cellular damage, and evidence suggests that it may suppress early immune response to infection.What this study addsThis is the first nationwide study of the relationship between historical exposure to air pollution exposure and COVID-19 death rate, relying on data from more than 3,000 counties in the United States. The results suggest that long-term exposure to PM2.5 is associated with higher COVID-19 mortality rates, after adjustment for a wide range of socioeconomic, demographic, weather, behavioral, epidemic stage, and healthcare-related confounders.This study relies entirely on publicly available data and fully reproducible, public code to facilitate continued investigation of these relationships by the broader scientific community as the COVID-19 outbreak evolves and more data become available.A small increase in long-term PM2.5 exposure was associated with a substantial increase in the county’s COVID-19 mortality rate up to April 22, 2020.

scholarly journals Effects of Government Mandated Social Distancing Measures on Cumulative Incidence of COVID-19 in the United States and its Most Populated Cities

2020 ◽  
Author(s):  
Bhuma Krishnamachari ◽  
Alexander Morris ◽  
Diane Zastrow ◽  
Andrew Dsida ◽  
Brian Harper ◽  
...  
Keyword(s):  
United States ◽  
Population Density ◽  
Cumulative Incidence ◽  
Negative Binomial ◽  
State Level ◽  
The United States ◽  
Reverse Causality ◽  
Social Distancing ◽  
School Closing ◽  
Adjusted Rate

AbstractCOVID-19, caused by the SARS-CoV-2 virus, has quickly spread throughout the world, necessitating assessment of the most effective containment methods. Very little research exists on the effects of social distancing measures on this pandemic. The purpose of this study was to examine the effects of government implemented social distancing measures on the cumulative incidence rates of COVID-19 in the United States on a state level, and in the 25 most populated cities, while adjusting for socio-demographic risk factors. The social distancing variables assessed in this study were: days to closing of non-essential business; days to stay home orders; days to restrictions on gathering, days to restaurant closings and days to school closing. Using negative binomial regression, adjusted rate ratios and 95% confidence intervals were calculated comparing two levels of a binary variable: “above median value,” and “median value and below” for days to implementing a social distancing measure. For city level data, the effects of these social distancing variables were also assessed in high (above median value) vs low (median value and below) population density cities. For the state level analysis, days to school closing was associated with cumulative incidence, with an adjusted rate ratio of 1.59 (95% CI:1.03,2.44), p=0.04 at 35 days. Some results were counterintuitive, including inverse associations between cumulative incidence and days to closure of non-essential business and restrictions on gatherings. This finding is likely due to reverse causality, where locations with slower growth rates initially chose not to implement measures, and later implemented measures when they absolutely needed to respond to increasing rates of infection. Effects of social distancing measures seemed to vary by population density in cities. Our results suggest that the effect of social distancing measures may differ between states and cities and between locations with different population densities. States and cities need individual approaches to containment of an epidemic, with an awareness of their own structure in terms of crowding and socio-economic variables. In an effort to reduce infection rates, cities may want to implement social distancing in advance of state mandates.

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

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.

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 Covid-19 Social Distancing Interventions by State Mandate and their Correlation to Mortality in the United States

2020 ◽  
Author(s):  
Sean McCafferty ◽  
Sean Ashley
Keyword(s):  
United States ◽  
Mortality Rate ◽  
Population Density ◽  
Total Mortality ◽  
The United States ◽  
The State ◽  
Human Interaction ◽  
Daily Mortality ◽  
Social Distancing ◽  
Initial Peak

Background: Evaluate the correlation between U.S. state mandated social interventions and Covid-19 mortality using a retrospective analysis of Institute for Health Metrics and Evaluation (IHME) data. Methods: Twenty-seven (27) states in the United States were selected on June 17, 2020 from IHME data which had clearly defined and dated establishment of statewide mandates for social distancing measures to include: School closures, Prohibition on mass gatherings, business closures, stay at home orders, severe travel restrictions, and closure of non-essential businesses. The state Covid-19 mortality prevalence was defined as total normalized deaths to the peak daily mortality rate. The state mortality prevalence was correlated to the total number of mandates-days from their date of establishment to the peak daily mortality date. The slope of the maximum daily mortality rate was also correlated to mandate-days. Results: The standardized mortality per state to the initial peak mortality rate did not demonstrate a discernable correlation to the total mandate days (R2 = 0.000006, p= 0.995). The standardized peak mortality rate per state suggested a slight correlation to the total mandate days (R2 = 0.053,p=0.246), but was not statistically significant. There was a significant correlation between standardized mortality and state population density (R2 = 0.524,p=0.00002). Conclusions: The analysis appears to suggest no mandate effective reduction in Covid-19 mortality nor a reduction in Covid-19 mortality rate to its defined initial peak when interpreting the mean-effect of the mandates as present in the data. A strong correlation to population density suggests human interaction frequency does affect the total mortality and maximum mortality rate.

scholarly journals Forecasting the Spread of COVID-19 under Different Reopening Strategies

2020 ◽  
Author(s):  
Meng Liu ◽  
Raphael Thomadsen ◽  
Song Yao
Keyword(s):  
United States ◽  
Growth Rate ◽  
Population Density ◽  
The United States ◽  
Sir Model ◽  
Social Distancing ◽  
Mobility Data ◽  
The Social ◽  
The Us ◽  
Case Data

ABSTRACTWe combine COVID-19 case data with demographic and mobility data to estimate a modified susceptible-infected-recovered (SIR) model for the spread of this disease in the United States. We find that the incidence of infectious COVID-19 individuals has a concave effect on contagion, as would be expected if people have inter-related social networks. We also demonstrate that social distancing and population density have large effects on the rate of contagion. The social distancing in late March and April substantially reduced the number of COVID-19 cases. However, the concave contagion pattern means that when social distancing measures are lifted, the growth rate is considerable but will not be exponential as predicted by standard SIR models. Furthermore, counties with the lowest population density could likely avoid high levels of contagion even with no social distancing. We forecast rates of new cases for COVID-19 under different social distancing norms and find that if social distancing is eliminated there will be a massive increase in the cases of COVID-19, about double what would occur if the US only restored to 50% of the way to normalcy.

scholarly journals Population density and basic reproductive number of COVID-19 across United States counties

PLoS ONE ◽  
2021 ◽  
Vol 16 (4) ◽  
pp. e0249271
Author(s):  
Karla Therese L. Sy ◽  
Laura F. White ◽  
Brooke E. Nichols
Keyword(s):  
United States ◽  
Population Density ◽  
Disease Transmission ◽  
Household Income ◽  
The United States ◽  
Basic Reproductive Number ◽  
Reproductive Number ◽  
Main Mode ◽  
Contact Rates ◽  
Mode Of Transportation

The basic reproductive number (R0) is a function of contact rates among individuals, transmission probability, and duration of infectiousness. We sought to determine the association between population density and R0 of SARS-CoV-2 across U.S. counties. We conducted a cross-sectional analysis using linear mixed models with random intercept and fixed slopes to assess the association of population density and R0, and controlled for state-level effects using random intercepts. We also assessed whether the association was differential across county-level main mode of transportation percentage as a proxy for transportation accessibility, and adjusted for median household income. The median R0 among the United States counties was 1.66 (IQR: 1.35–2.11). A population density threshold of 22 people/km2 was needed to sustain an outbreak. Counties with greater population density have greater rates of transmission of SARS-CoV-2, likely due to increased contact rates in areas with greater density. An increase in one unit of log population density increased R0 by 0.16 (95% CI: 0.13 to 0.19). This association remained when adjusted for main mode of transportation and household income. The effect of population density on R0 was not modified by transportation mode. Our findings suggest that dense areas increase contact rates necessary for disease transmission. SARS-CoV-2 R0 estimates need to consider this geographic variability for proper planning and resource allocation, particularly as epidemics newly emerge and old outbreaks resurge.

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

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Myles Ingram ◽  
Ashley Zahabian ◽  
Chin Hur
Keyword(s):  
United States ◽  
Prediction Model ◽  
Mobile Phone ◽  
The United States ◽  
Population Based ◽  
Mobile Phone Data ◽  
County Level ◽  
Social Distancing ◽  
Work From Home ◽  
First Case

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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