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

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

10.1101/2020.08.26.20182758 ◽

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.

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

Humanities and Social Sciences Communications ◽

10.1057/s41599-021-00767-0 ◽

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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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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Data-driven Public Health and Social Intervention Modeling to Promote Effective Policy Responses to Curtailing the COVID-19 Pandemic

10.36227/techrxiv.12616142 ◽

2020 ◽

Author(s):

Robin Qiu

Keyword(s):

Public Health ◽

United States ◽

Critical Role ◽

The United States ◽

Daily Basis ◽

Data Driven ◽

Social Intervention ◽

Social Interventions ◽

Policy Responses ◽

Effective Policy

We might have to live with COVID-19 until 2025 according to a recent report published in Science! But if we act smartly, the adverse consequence of living with the virus could be minimized. Currently, many states in the United States are seeing spiking cases on a daily basis due to their inadequate policy responses to COVID-19. This paper promotes more research on improving SEIR modeling as it will play a critical role in facilitating the decision-making on promoting and implementing appropriate public health and social interventions. Hopefully, policymakers will listen to science and enact and implement adequate policy responses in combating the COVID-19 pandemic in each of the states across the United States so that we can win this “war” and be well prepared for the promising future.

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Social Distancing as a Health Behavior: County-level Movement in the United States During the COVID-19 Pandemic is Associated with Conventional Health Behaviors

10.31234/osf.io/bt5ac ◽

2020 ◽

Cited By ~ 2

Author(s):

Kyle J. Bourassa ◽

David Sbarra ◽

Avshalom Caspi ◽

Terrie Moffitt

Keyword(s):

Physical Activity ◽

United States ◽

Health Behavior ◽

Health Behaviors ◽

Health Behavior Change ◽

The United States ◽

Movement Behavior ◽

County Level ◽

Social Contacts ◽

Social Distancing

Background: Social distancing—when people reduce their physical movement and limit social contacts beyond their immediate household—is a primary intervention available to combat the COVID-19 pandemic. The importance of social distancing is unlikely to change until effective treatments or vaccines become widely available. However, relatively little is known about how best to promote social distancing. Applying knowledge from social and behavioral research on conventional health behaviors (e.g., smoking, physical activity) to support social distancing public health efforts and research is promising, but empirical evidence supporting this approach is needed. Purpose: We examined whether one type of social distancing behavior—reductions in movement outside the home—was associated with conventional health behaviors. Method: We examined the association between GPS-derived movement behavior in 2,858 counties in United States from March 1st to April 7th, 2020 and the prevalence of county-level indicators influenced by residents’ conventional health behaviors. Results: Changes in movement were associated with conventional health behaviors, and the magnitude of these associations were similar to the associations among the conventional health behaviors. Counties with healthier behaviors—particularly less obesity and greater physical activity—evidenced greater decreases in movement outside the home during the initial phases of the pandemic in the United States. Conclusions: Social distancing, in the form of reduced movement outside the home, is associated with conventional health behaviors. Existing scientific literature on health behavior and health behavior change can be more confidently used to promote social distancing during the COVID-19 pandemic.

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

10.2196/preprints.23902 ◽

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 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.

Download Full-text