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

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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Investigating the effects of absolute humidity and human encounters on transmission of COVID-19 in the United States

10.1101/2020.10.30.20223446 ◽

2020 ◽

Author(s):

Gary Lin ◽

Alisa Hamilton ◽

Oliver Gatalo ◽

Fardad Haghpanah ◽

Takeru Igusa ◽

...

Keyword(s):

United States ◽

Close Contact ◽

Human Mobility ◽

Additive Model ◽

Absolute Humidity ◽

The United States ◽

Respiratory Viruses ◽

Mobile Phone Data ◽

Direct Result ◽

Combining Data

AbstractBackgroundMounting evidence suggests that the primary mode of transmission of SARS-CoV-2 is aerosolized transmission from close contact with infected individuals. Even though transmission is a direct result of human encounters, environmental conditions, such as lower humidity, may enhance aerosolized transmission risks similar to other respiratory viruses such as influenza.MethodsWe utilized dynamic time warping to cluster all 3,137 counties in the United States based on temporal data on absolute humidity from March 10 to September 29, 2020. We then used a multivariate generalized additive model (GAM) combining data on human mobility derived from mobile phone data with humidity data to identify the potential effect of absolute humidity and mobility on new daily cases of COVID-19 while considering the temporal differences between seasons.ResultsThe clustering analysis found ten groups of counties with similar humidity levels. We found a significant negative effect between increasing humidity and new cases of COVID-19 in most regions, particularly in the period from March to July. The effect was greater in regions with generally lower humidity in the Western, Midwest, and Northeast regions of the US. In the two regions with the largest effect, a 1 g/m3 increase of absolute humidity resulted in a 0.21 and 0.15 decrease in cases. The effect of mobility on cases was positive and significant across all regions in the July-Sept time period, though the relationship in some regions was more mixed in the March to June period.ConclusionsWe found that increasing humidity played an important role in falling cases in the spring, while increasing mobility in the summer contributed more significantly to increases in the summer. Our findings suggest that, similar to other respiratory viruses, the decreasing humidity in the winter is likely to lead to an increase in COVID-19 cases. Furthermore, the fact that mobility data were positively correlated suggests that efforts to counteract the rise in cases due to falling humidity can be effective in limiting the burden of the pandemic.

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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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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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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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Effects of Varying Approaches to Lifting COVID-19 Pandemic Restrictions in the United States

10.1101/2022.01.04.22268766 ◽

2022 ◽

Author(s):

Jessica E Galarraga ◽

Daniel Popovsky ◽

Kevin Delijani ◽

Hannah Hanson ◽

Mark Hanlon

Keyword(s):

United States ◽

Health Outcomes ◽

Population Health ◽

Large Scale ◽

The United States ◽

Negative Effects ◽

Difference In Differences ◽

Social Distancing ◽

Fold Reduction ◽

Control And Prevention

Background Policy approaches to lifting COVID-19 restrictions have varied significantly across the United States. An evaluation of the effects of state reopening policies on population health outcomes can inform ongoing and future pandemic responses. This study evaluates the approaches to lifting social distancing restrictions based on adherence to the Centers for Disease Control and Prevention (CDC) guidance established during the first wave of the COVID-19 pandemic. Methods We performed a retrospective study using difference-in-differences analyses to examine the effects of reopening policies on COVID-19 outcomes with risk-adjustment for population density, temporal changes, and concurrent mask policy implementation. We examined the effects of reopening policies on per capita case rates and rates of severe COVID-19 outcomes, including hospitalizations and deaths. Results Adherence to CDC reopening gating metrics and phased social distancing guidelines resulted in fewer COVID-19 cases, hospitalizations, and deaths. Phase one adherent states exhibited a 50-fold reduction in daily new cases and a 3-fold reduction in daily new deaths after reopening. Phase two adherent states experienced improvements in COVID-19 outcomes after reopening, while non-adherent states had a resurgence of worsening outcomes after lifting restrictions. Conclusions Our study findings indicate that adherence to CDC reopening guidance after implementing social distancing restrictions during the COVID-19 pandemic substantially prevents new cases, hospitalizations, and deaths. Following a stepwise reopening strategy and ensuring a sustained decline in case rates and test positivity rates before lifting restrictions can mitigate on a large scale the negative effects of a pandemic on population health outcomes.

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Population, Politics, and Unemployment Policy in the Great Depression

Social Science History ◽

10.1017/ssh.2015.7 ◽

2014 ◽

Vol 38 (1-2) ◽

pp. 79-87 ◽

Cited By ~ 1

Author(s):

Miriam Cohen

Keyword(s):

United States ◽

Social History ◽

Great Depression ◽

Large Scale ◽

Public Attitudes ◽

Local Level ◽

The United States ◽

The Great Depression ◽

History Of ◽

Unemployment Policy

Working out large-scale processes through close attention to local-level analysis remained central to Louise Tilly's approach to social history. An ongoing commitment to agency and strategy undergirded her vision for a global history that made connections between large-scale processes across space, between human agency and structure, and between the past and present. Her vision remains an important influence in my coauthored comparative history of the welfare state in England, France, and the United States. This is illustrated by a discussion of unemployment policies in the three countries at one particular moment of crisis, the Great Depression, concentrating on the United States, where the Depression hit first and hit the hardest. Important differences in demography, the mobilization of ordinary citizens, the responsiveness of state structures to democratic pressure, and public attitudes about the legitimate role of government all affected the history of unemployment policy in each country.

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Changes in risk perception and protective behavior during the first week of the COVID-19 pandemic in the United States

10.31234/osf.io/dz428 ◽

2020 ◽

Cited By ~ 56

Author(s):

Toby Wise ◽

Tomislav Damir Zbozinek ◽

Giorgia Michelini ◽

Cindy C. Hagan ◽

dean mobbs

Keyword(s):

United States ◽

Risk Perception ◽

Behavioral Interventions ◽

Large Scale ◽

The United States ◽

Medical Attention ◽

Protective Behavior ◽

Protective Behaviors ◽

Social Distancing ◽

Us Government

By mid-March 2020, the COVID-19 pandemic spread to over 100 countries and all 50 states in the US. Government efforts to minimize the spread of disease emphasized behavioral interventions, including raising awareness of the disease and encouraging protective behaviors such as social distancing and hand washing, and seeking medical attention if experiencing symptoms. However, it is unclear to what extent individuals are aware of the risks associated with the disease, how they are altering their behavior, factors which could influence the spread of the virus to vulnerable populations. We characterized risk perception and engagement in preventative measures in 1591 United States based individuals over the first week of the pandemic (March 11th-16th 2020) and examined the extent to which protective behaviors are predicted by individuals’ perception of risk. Over 5 days, subjects demonstrated growing awareness of the risk posed by the virus, and largely reported engaging in protective behaviors with increasing frequency. However, they underestimated their personal risk of infection relative to the average person in the country. We found that engagement in social distancing and handwashing was most strongly predicted by the perceived likelihood of personally being infected, rather than likelihood of transmission or severity of potential transmitted infections. However, substantial variability emerged among individuals, and using data-driven methods we found a subgroup of subjects who are largely disengaged, unaware, and not practicing protective behaviors. Our results have implications for our understanding of how risk perception and protective behaviors can facilitate early interventions during large-scale pandemics.

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State-level impact of social distancing and testing on COVID-19 in the United States

10.21203/rs.3.rs-40364/v1 ◽

2020 ◽

Author(s):

Weihsueh A. Chiu ◽

Rebecca Fischer ◽

Martial L. Ndeffo-Mbah

Keyword(s):

United States ◽

Large Scale ◽

Disease Incidence ◽

State Level ◽

The United States ◽

Contact Tracing ◽

Reproductive Number ◽

Social Distancing ◽

Epidemic Curve ◽

The Impact

Abstract Social distancing measures have been implemented in the United States (US) since March 2020, to mitigate the spread of SARS-CoV-2, the causative agent of COVID-19. However, by mid-May most states began relaxing these measures to support the resumption of economic activity, even as disease incidence continued to increase in many states. To evaluate the impact of relaxing social distancing restrictions on COVID-19 dynamics and control in the US, we developed a transmission dynamic model and calibrated it to US state-level COVID-19 cases and deaths from March to June 20th, 2020, using Bayesian methods. We used this model to evaluate the impact of reopening, social distancing, testing, contact tracing, and case isolation on the COVID-19 epidemic in each state. We found that using stay-at-home orders, most states were able to curtail their COVID-19 epidemic curve by reducing and achieving an effective reproductive number below 1. But by June 20th, 2020, only 19 states and the District of Columbia were on track to curtail their epidemic curve with a 75% confidence, at current levels of reopening. Of the remaining 31 states, 24 may have to double their current testing and/or contact tracing rate to curtail their epidemic curve, and seven need to further restrict social contact by 25% in addition to doubling their testing and contact tracing rates. When social distancing restrictions are being eased, greater state-level testing and contact tracing capacity remains paramount for mitigating the risk of large-scale increases in cases and deaths.

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Human mobility trends during the early stage of the COVID-19 pandemic in the United States

PLoS ONE ◽

10.1371/journal.pone.0241468 ◽

2020 ◽

Vol 15 (11) ◽

pp. e0241468

Author(s):

Minha Lee ◽

Jun Zhao ◽

Qianqian Sun ◽

Yixuan Pan ◽

Weiyi Zhou ◽

...

Keyword(s):

Large Scale ◽

Early Stage ◽

Human Mobility ◽

Public Awareness ◽

The United States ◽

Health Crisis ◽

Social Distancing ◽

Tangible Evidence ◽

The Government ◽

At Home

In March of this year, COVID-19 was declared a pandemic, and it continues to threaten public health. This global health crisis imposes limitations on daily movements, which have deteriorated every sector in our society. Understanding public reactions to the virus and the non-pharmaceutical interventions should be of great help to fight COVID-19 in a strategic way. We aim to provide tangible evidence of the human mobility trends by comparing the day-by-day variations across the U.S. from January 2020 to early April 2020. Large-scale public mobility at an aggregated level is observed by leveraging mobile device location data and the measures related to social distancing. Our study captures spatial and temporal heterogeneity as well as the sociodemographic variations and teleworking trends regarding the pandemic propagation and the non-pharmaceutical mobility interventions. All metrics adapted capture decreased public movements after the national emergency declaration. The population staying home has increased in all states before the stay-at-home mandates implemented and becomes more stable after the order with a smaller range of fluctuation. The public had been taking active responses, voluntarily staying home more, to the in-state confirmed cases while the stay-at-home orders stabilize the variations. As the estimated teleworking rates also continue to incline throughout the study period, the teleworking trend can be another driving factor for the growing stay-at-home population. We confirm that there exists overall mobility heterogeneity between the income or population density groups. The study suggests that public mobility trends are in line with the government message urging to stay home. We anticipate our data-driven analysis offers integrated perspectives and serves as evidence to raise public awareness and, consequently, reinforce the importance of social distancing while assisting policymakers.

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The relationship between human mobility measures and SAR-Cov-2 transmission varies by epidemic phase and urbanicity: results from the United States

10.1101/2021.04.15.21255562 ◽

2021 ◽

Author(s):

Nishant Kishore ◽

Aimee R Taylor ◽

Pierre E Jacob ◽

Navin Vembar ◽

Ted Cohen ◽

...

Keyword(s):

United States ◽

Mobile Phones ◽

Human Mobility ◽

Population Level ◽

The United States ◽

Mobile Phone Data ◽

Reproductive Number ◽

Generation Process ◽

Data Generation ◽

The Relationship

Global efforts to prevent the spread of the SARS-COV-2 pandemic in early 2020 focused on non-pharmaceutical interventions like social distancing; policies that aim to reduce transmission by changing mixing patterns between people. As countries have implemented these interventions, aggregated location data from mobile phones have become an important source of real-time information about human mobility and behavioral changes on a population level. Human activity measured using mobile phones reflects the aggregate behavior of a subset of people, and although metrics of mobility are related to contact patterns between people that spread the coronavirus, they do not provide a direct measure. In this study, we use results from a nowcasting approach from 1,396 counties across the US between January 22nd, 2020 and July 9th, 2020 to determine the effective reproductive number (R(t)) along an urban/rural gradient. For each county, we compare the time series of R(t) values with mobility proxies from mobile phone data from Camber Systems, an aggregator of mobility data from various providers in the United States. We show that the reproduction number is most strongly associated with mobility proxies for change in the travel into counties compared to baseline, but that the relationship weakens considerably after the initial 15 weeks of the epidemic, consistent with the emergence of a more complex ecosystem of local policies and behaviors including masking. Importantly, we highlight potential issues in the data generation process, representativeness and equity of access which must be addressed to allow for general use of these data in public health.

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