Social Distancing Index Research Articles

Social Distancing During the COVID-19 Pandemic and Neonatal Mortality in the US

ImportanceNeonatal mortality is a major public health concern that was potentially impacted by the COVID-19 pandemic. To prepare for future health crises, it is important to investigate whether COVID-19 pandemic–related interventions were associated with changes in neonatal mortality.ObjectiveTo investigate whether social distancing during the pandemic was associated with a higher neonatal mortality rate.Design, Setting, and ParticipantsThis cohort study examined maternal-linked birth and infant death records from the National Center for Health Statistics, a population-level US database, from 2016 through 2020. The mortality rates were correlated using machine learning–based autoregressive integrated moving average (ARIMA) models with the social distancing index (SDI). The reference period was January 2016 through February 2020, and the pandemic period was March through December 2020. Statistical analysis was performed from March 2023 to May 2024.ExposuresSDI, computed from 6 mobility metrics.Main Outcomes and MeasuresThe primary outcome was neonatal mortality rate, defined as death at age less than 28 days.ResultsThe study included 18 011 173 births, of which 15 136 596 were from the reference period (7 753 555 [51.22%] male; 11 643 094 [76.92%] with maternal age of 20 to 34 years) and 2 874 577 were from the pandemic period (1 472 539 [51.23%] male; 2 190 158 [76.19%] with maternal age of 20 to 34 years). Through ARIMA-adjusted analyses, accounting for the declining mortality trend in the reference period, the mortality rates during the pandemic period did not significantly differ from the expected rates. SDI did not exhibit significant correlations with neonatal mortality (unadjusted: correlation coefficient [CC], 0.14 [95% CI, −0.53 to 0.70]; ARIMA adjusted: CC, 0.29 [95% CI, −0.41 to 0.77]), early neonatal mortality (unadjusted: CC, 0.33 [95% CI, −0.37 to 0.79]; ARIMA adjusted: CC, 0.45 [95% CI, −0.24 to 0.84]), and infant mortality (unadjusted: CC, −0.09 [95% CI, −0.68 to 0.57]; ARIMA adjusted: CC, 0.35 [95% CI, −0.35 to 0.80]). However, lag analyses found that SDI was associated with higher neonatal and early neonatal mortality rates with a 2-month lag period, but not with infant mortality rate. SDI was also associated with increases in 22-to-27 weeks’ and 28-to-32 weeks’ preterm delivery with a 1-month lag period.Conclusions and RelevanceIn this population-level study of National Center for Health Statistics databases, neonatal, early neonatal, and infant mortality rates did not increase during the initial COVID-19 pandemic period. However, associations were observed between the pandemic period social distancing measures and higher rates of neonatal and early neonatal mortality, as well as preterm birth rate with a lag period, suggesting the importance of monitoring infant health outcomes following pandemic-related population behavior changes.

Social distancing and extremely preterm births in the initial COVID-19 pandemic period

HypothesisIncreased social distancing was associated with a lower incidence of extremely preterm live births (EPLB) during the initial COVID-19 pandemic period.Study designProspective study at the NICHD Neonatal Research Network sites comparing EPLB (220/7–286/7 weeks) and extremely preterm intrapartum stillbirths (EPIS) rates during the pandemic period (March-July, weeks 9–30 of 2020) with the reference period (same weeks in 2018 and 2019), correlating with state-specific social distancing index (SDI).ResultsEPLB and EPIS percentages did not significantly decrease (1.58–1.45%, p = 0.07, and 0.08–0.06%, p = 0.14, respectively). SDI was not significantly correlated with percent change of EPLB (CC = 0.29, 95% CI = −0.12, 0.71) or EPIS (CC = −0.23, 95% CI = −0.65, 0.18). Percent change in mean gestational age was positively correlated with SDI (CC = 0.49, 95% CI = 0.07, 0.91).ConclusionsIncreased social distancing was not associated with change in incidence of EPLB but was associated with a higher gestational age of extremely preterm births.ClinicalTrials.gov IDGeneric Database: NCT00063063.

IMPACT OF SOCIOECONOMIC VULNERABILITY ON COVID-19 OUTCOMES AND SOCIAL DISTANCING IN BRAZIL

Due to the persistently high cases and deaths, Brazil became one of the worst countries affected by the COVID-19 pandemic. Understanding the possible health inequities is essential, given the population's diversity and the country's fragile socioeconomic situation. Thus, this study aimed to assess the impact and correlation of socioeconomic vulnerability on COVID-19 outcomes and social distancing in Brazil. The Gini Coefficient (GC), the Social Vulnerability Index (SVI), epidemiological data on the COVID-19 epidemic in Brazil, and the Social Distancing Index (SDI) were retrieved from online databases and assessed for each Brazilian state. Data was statistically analyzed through non-parametric tests and multiple linear regressions. The mean values for the GC and SVI were 0.495 and 0.261, respectively. A positive statistically significant correlation was found between the socioeconomic indicators and the three variables related to the COVID-19 outbreak. States with very low social vulnerability presented fewer deaths per 100 thousand inhabitants due to COVID-19 than states with moderate social vulnerability. SVI was a predictor of accumulated cases, confirmed deaths, and social distancing. The COVID-19 outcomes and SDI in Brazilian states are correlated to socioeconomic conditions. The pandemic impacts are more severe on less favored communities.

Impacts of social distancing policy and vaccination during the COVID-19 pandemic in the Republic of Korea

This paper investigates the dynamic impact of social distancing policy on coronavirus disease (COVID-19) infection control, mobility of people, and consumption expenditures in the Republic of Korea. We employ structural and threshold vector autoregressive (VAR) models using big-data-driven mobility data, credit card expenditure, and a social distancing index. We find that the social distancing policy significantly reduces the spread of COVID-19, but there exists a significant, growing trade-off between infection control and economic activity over time. When the level of stringency in social distancing is already high, its marginal effect on mobility is estimated to be smaller than when social distancing stringency is low. The effect of social distancing also becomes secondary after vaccination. Increased vaccination is found to significantly reduce the critical cases while it increases visitors and consumption expenditures. The results also show that the effect of social distancing policy on mobility reduction is strongest among the population of age under 20 and the weakest among the population of age over 60.

The Perceived Social Distance in A Traditional Multi-Confessional Community

The paper presents the results of a sociological study conducted in a multi-confessional community situated in the central area of Moldova, Romania. Its main aim was to establish the way in which social distance is perceived and affirmed in this rural community. We used the scale of social distance, starting from the model developed by Bogardus (1925), which has become the most frequently used method of emphasizing the acceptance or isolation of social groups. We calculated the Social Distance Index (SDI), the Social Contact Index (SCI), and the Qualitative Index of Social Contacts (QISC). Results are focused on inter-confessional issues, and conclusions reveal a community open to what is close and known, but reluctant to what is distant and less known.

Disappearance and Re-Emergence of Influenza during the COVID-19 Pandemic: Association with Infection Control Measures.

During the coronavirus disease 2019 (COVID-19) pandemic, the influenza virus had a very low prevalence, and in many areas, outbreaks were almost non-existent. In this study, the associations between infection control measures taken for COVID-19 and the global disappearance of the influenza virus were investigated. The detection rate of influenza from baseline was investigated during four seasons (12 weeks from epidemiological week 49 in 2020 and 2021 and 12 weeks from epidemiological week 23 in 2020 and 2021) in each country participating in the surveillance system of the World Health Organization. Three measures of infection control: mask use ratio, social distancing index (an index of human mobility and physical distance obligations), and an index of stringency of measures taken by authorities were studied. In mid-2020, most countries analyzed had high levels of infection control measures, and in most countries, influenza was drastically reduced compared to previous years. Multiple regression analyses compared the study data with data from other seasons. There was an association between high mask use with low influenza detection in all three remaining seasons, an association between a low social distancing index (low mobility and more social contact obligations) with a low influenza detection rate in two seasons, and a marginal significant association of high stringency index with a low influenza detection rate(in 2020-end-seasons). These results support the notion that seasonal influenza is controllable through effective preventive measures, especially those of mask use and human social contact, and these measures should be recommended during future waves of novel influenza virus infection.

COVID-19 and income profile: How communities in the United States responded to mobility restrictions in the pandemic's early stages.

Mobility interventions in communities play a critical role in containing a pandemic at an early stage. The real-world practice of social distancing can enlighten policymakers and help them implement more efficient and effective control measures. A lack of such research using real-world observations initiates this article. We analyzed the social distancing performance of 66,149 census tracts from 3,142 counties in the United States with a specific focus on income profile. Six daily mobility metrics, including a social distancing index, stay-at-home percentage, miles traveled per person, trip rate, work trip rate, and non-work trip rate, were produced for each census tract using the location data from over 100 million anonymous devices on a monthly basis. Each mobility metric was further tabulated by three perspectives of social distancing performance: "best performance," "effort," and "consistency." We found that for all 18 indicators, high-income communities demonstrated better social distancing performance. Such disparities between communities of different income levels are presented in detail in this article. The comparisons across scenarios also raise other concerns for low-income communities, such as employment status, working conditions, and accessibility to basic needs. This article lays out a series of facts extracted from real-world data and offers compelling perspectives for future discussions.

High-resolution estimates of social distancing feasibility, mapped for urban areas in sub-Saharan Africa

Social distancing has been widely-implemented as a public health measure during the COVID-19 pandemic. Despite widespread application of social distancing guidance, the feasibility of people adhering to such guidance varies in different settings, influenced by population density, the built environment and a range of socio-economic factors. Social distancing constraints however have only been identified and mapped for limited areas. Here, we present an ease of social distancing index, integrating metrics on urban form and population density derived from new multi-country building footprint datasets and gridded population estimates. The index dataset provides estimates of social distancing feasibility, mapped at high-resolution for urban areas across 50 countries in sub-Saharan Africa.

Variability in Opioid-Related Drug Overdoses, Social Distancing, and Area-Level Deprivation during the COVID-19 Pandemic: a Bayesian Spatiotemporal Analysis.

Monitoring the spatial and temporal course of opioid-related drug overdose mortality is a key public health determinant. Despite previous studies exploring the evolution of drug-related fatalities following the stay-at-home mandates during the COVID-19 pandemic, little is known about the spatiotemporal dynamics that mitigation efforts had on overdose deaths. The purpose of this study was to describe the spatial and temporal dynamics of overdose death relative risk using a 4-week interval over a span of 5 months following the implementation of the COVID-19 lockdown in the city of Chicago, IL. A Bayesian space–time model was used to produce posterior risk estimates and exceedance probabilities of opioid-related overdose deaths controlling for measures of area-level deprivation and stay-at-home mandates. We found that area-level temporal risk and inequalities in drug overdose mortality increased significantly in the initial months of the pandemic. We further found that a change in the area-level deprivation from the first to the fourth quintile increased the relative risk of a drug overdose risk by 44.5%. The social distancing index measuring the proportion of persons who stayed at home in each census block group was not associated with drug overdose mortality. We conclude by highlighting the importance of contextualizing the spatial and temporal risk in overdose mortality for implementing effective and safe harm reduction strategies during a global pandemic.Supplementary InformationThe online version contains supplementary material available at 10.1007/s11524-022-00675-x.

Recreational Visit to Suburban Forests during the COVID-19 Pandemic: A Case Study of Taiwan

COVID-19 global pandemic has caused massive disruption of travel behaviors along with other aspects of human life, such as social distancing, staying at home, and avoiding crowds. People substituted outdoor activities for indoor activities, and the forest environment has become a popular alternative. Taiwan has a high population density, but it had few COVID-19 confirmed cases in 2020 during the first wave of the COVID-19 pandemic. No forest areas have been closed due to the COVID-19 outbreak. In light of this generally increased demand for suburban forests for recreational uses, the current COVID-19 pandemic situation poses specific challenges regarding forest use, management, and policy. This study integrates visitation numbers of the popular forest recreation area and selects the unblocking index and social distancing index as the COVID-19 index to capture the impacts of forest recreation area on the COVID-19 outbreak in Taiwan. The results show both COVID-19 indices have high explanatory power for suburban forest visitation and both have a significant impact on the number of visitors. Although the number of visitors to suburban forests decreased during the COVID-19 pandemic alert, it bounced when the COVID-19 outbreak was under control. This study provides a brief overview of management implications for recreational visits during COVID-19. We posed an early warning to forest managers for greater revenge traveling post-COVID-19.

The Research of “Products Rapidly Attracting Users” Based on the Fully Integrated Link Prediction Algorithm

One of the main problems encountered by social networks is the cold start problem. The term “cold start problem” refers to the difficulty in predicting new users’ friendships due to the limited number of links those users have with existing nodes. To fill the gap, this paper proposes a Fully Integrated Link Prediction Algorithm (FILPA) that describes the social distance of nodes by using “betweenness centrality,” and develops a Social Distance Index (SDI) based on micro- and macro-network structure according to social distance. With the aim of constructing adaptive SDIs that are suitable for the characteristics of a network, a naive Bayes (NB) method is firstly adopted to select appropriate SDIs according to the density and social distance characteristics of common neighbors in the local network. To avoid the risk of algorithm accuracy reduction caused by blind combination of SDIs, the AdaBoost meta-learning strategy is applied to develop a Fully Integrated Social Distance Index (FISDI) composed of the best SDIs screened by NB. The possible friendships among nodes will then be comprehensively presented using high performance FISDI. Finally, in order to realize the “products rapidly attracting users” in new user marketing, FILPA is used to predict the possible friendship between new users in an online brand community and others in different product circles.

Social Distancing, Stroke Admissions and Stroke Mortality During the COVID-19 Pandemic: A Multicenter, Longitudinal Study

ObjectivesWe aimed to evaluate the relationship between social distancing, stroke admissions and stroke mortality during the COVID-19 pandemic, while accounting for the rate of COVID-19 admissions. MethodsWe performed a longitudinal analysis of a multicenter, prospective, hospital-based registry of intensive care units from 19 hospitals from Brazil, comprising a 14-month period of the COVID-19 pandemic. We investigated whether the daily rate of admissions (DRAstroke) and daily mortality rate for stroke were associated with the social distancing index (SDI), taking into account the daily rate of admissions for COVID-19 (DRACOVID) in univariate and multivariate regression models. We also compared the clinical characteristics of patients with stroke admitted before and during the pandemic. ResultsWe found that DRAstroke decreased significantly in association with a strong rise in the SDI during the early months of the pandemic. However, in the latter period of the pandemic, only minor changes were observed in the SDI, and still, DRAstroke was inversely associated with the DRACOVID. Throughout the pandemic, higher SDI and DRACOVID were associated with higher in-hospital mortality for stroke. ConclusionsThe severity of surges of the COVID-19 pandemic were independently and persistently associated with declines in stroke admissions, even during periods when social distancing policies were not intensified.

An improved numerical model for epidemic transmission and infection risks assessment in indoor environment

Social distance will remain the key measure to contain COVID-19 before the global widespread vaccination coverage expected in 2024. Containing the virus outbreak in the office is prioritised to relieve socio-economic burdens caused by COVID-19 and potential pandemics in the future. However, “what is the transmissible distance of SARS-CoV-2” and “what are the appropriate ventilation rates in the office” have been under debate. Without quantitative evaluation of the infection risk, some studies challenged the current social distance policies of 1–2 m adopted by most countries and suggested that longer social distance rule is required as the maximum transmission distance of cough ejected droplets could reach 3–10 m. With the emergence of virus variants such as the Delta variant, the applicability of previous social distance rules are also in doubt. To address the above problem, this study conducted transient Computational Fluid Dynamics (CFD) simulations to evaluate the infection risks under calm and wind scenarios. The calculated Social Distance Index (SDI) indicates that lower humidity leads to a higher infection risk due to weaker evaporation. The infection risk in office was found more sensitive to social distance than ventilation rate. In standard ventilation conditions, social distance of 1.7 m–1.8 m is sufficient distances to reach low probability of infection (PI) target in a calm scenario when coughing is the dominant transmission route. However in the wind scenario (0.25 m/s indoor wind), distance of 2.8 m is required to contain the wild virus type and 3 m is insufficient to contain the spread of the Delta variant. The numerical methods developed in this study provide a framework to evaluate the COVID-19 infection risk in indoor environment. The predicted PI will be beneficial for governments and regulators to make appropriate social-distance and ventilation rules in the office.

The stringency of the containment measures in response to COVID-19 inversely correlates with the overall disease occurrence over the epidemic wave.

Non-pharmaceutical interventions (NPIs) were the only viable choice to mitigate or suppress transmission of COVID-19 in the absence of efficient and safe vaccines. In this study, we examined the association between the stringency of containment measures and cumulative incidence of the COVID-19 cases in the first wave of the pandemic across 28 European countries. Our results support the effectiveness of containment measures in the mitigation or suppression of COVID-19 epidemics. Early adoption of stringent containment measures prior to detection of the first confirmed case, together with ramping up containment stringency during the early days of epidemics, was associated with a lower disease occurrence. The delayed adoption of stringent containment measures did not fully compensate for the lack of early response. Containment measures continue to play a significant role in the control of COVID-19 in the post-vaccination period, when limited vaccination coverage, the emergence of vaccine resistance, and/or increased mobility enabled further disease transmission (Tab. 4, Fig. 22, Ref. 50). Keywords: non-pharmaceutical interventions, containment, COVID-19, mobility, social distancing, Containment and Health Index, epidemiology, public health measures, SARS-CoV-2.

The impact of weather condition and social activity on COVID-19 transmission in the United States

The coronavirus disease 2019 (COVID-19) has been first reported in December 2019 and rapidly spread worldwide. As other severe acute respiratory syndromes, it is a widely discussed topic whether seasonality affects the COVID-19 infection spreading. This study presents two different approaches to analyse the impact of social activity factors and weather variables on daily COVID-19 cases at county level over the Continental U.S. (CONUS). The first one is a traditional statistical method, i.e., Pearson correlation coefficient, whereas the second one is a machine learning algorithm, i.e., random forest regression model. The Pearson correlation is analysed to roughly test the relationship between COVID-19 cases and the weather variables or the social activity factor (i.e. social distance index). The random forest regression model investigates the feasibility of estimating the number of county-level daily confirmed COVID-19 cases by using different combinations of eight factors (county population, county population density, county social distance index, air temperature, specific humidity, shortwave radiation, precipitation, and wind speed). Results show that the number of daily confirmed COVID-19 cases is weakly correlated with the social distance index, air temperature and specific humidity through the Pearson correlation method. The random forest model shows that the estimation of COVID-19 cases is more accurate with adding weather variables as input data. Specifically, the most important factors for estimating daily COVID-19 cases are the population and population density, followed by the social distance index and the five weather variables, with temperature and specific humidity being more critical than shortwave radiation, wind speed, and precipitation. The validation process shows that the general values of correlation coefficients between the daily COVID-19 cases estimated by the random forest model and the observed ones are around 0.85.

Interactive COVID-19 Mobility Impact and Social Distancing Analysis Platform.

The research team has utilized privacy-protected mobile device location data, integrated with COVID-19 case data and census population data, to produce a COVID-19 impact analysis platform that can inform users about the effects of COVID-19 spread and government orders on mobility and social distancing. The platform is being updated daily, to continuously inform decision-makers about the impacts of COVID-19 on their communities, using an interactive analytical tool. The research team has processed anonymized mobile device location data to identify trips and produced a set of variables, including social distancing index, percentage of people staying at home, visits to work and non-work locations, out-of-town trips, and trip distance. The results are aggregated to county and state levels to protect privacy, and scaled to the entire population of each county and state. The research team is making their data and findings, which are updated daily and go back to January 1, 2020, for benchmarking, available to the public to help public officials make informed decisions. This paper presents a summary of the platform and describes the methodology used to process data and produce the platform metrics.

SOCIAL DISTANCES IN A MULTI ETHNIC REGION

The paper presents the results of a sociological study conducted in some multiethnic communities situated in the central area of Moldova, Romania. Its main aim was to establish the ethnic identity of Catholic population in the villages of this region and to discover the way in which these communities build their ethnic and cultural identity. We used the scale of social distance, starting from the model developed by Bogardus (1925), which has become the most frequently used method of emphasizing the acceptance or isolation of social groups. We calculated the Social Distance Index (SDI) which highlights the number of social contact opportunities rejected by the respondent, the Social Contact Index (SCI) derived from the number of social contact opportunities accepted by the respondents, and the Qualitative Index of Social Contacts (QISC). The results are focused on ethnic aspects and local features of studied communities and the conclusions reveal the methods of ethnic self-defining for each community and their tendency to conserve local identity by refusing external influences that have an ideological stake.

Impact of COVID-19 on city-scale transportation and safety: An early experience from Detroit.

The COVID-19 pandemic brought unprecedented levels of disruption to the local and regional transportation networks throughout the United States, especially the Motor City---Detroit. That was mainly a result of swift restrictive measures such as statewide quarantine and lock-down orders to confine the spread of the virus and the rising number of COVID-19 confirmed cases and deaths. This work is driven by analyzing five types of real-world data sets from Detroit related to traffic volume, daily cases, weather, social distancing index, and crashes from January 2019 to June 2020. The primary goals of this work are: i) figuring out the impacts of COVID-19 on the transportation network usage (traffic volume) and safety (crashes) for the City of Detroit, ii) determining whether each type of data (e.g. traffic volume data) could be a useful factor in the confirmed-cases prediction, and iii) providing an early future prediction method for COVID-19 rates, which can be a vital contributor to life-saving advanced preventative and preparatory responses.In addressing these problems, the prediction results of six feature groups are presented and analyzed to quantify the prediction effectiveness of each type of data. Then, a deep learning model was developed using long short-term memory networks to predict the number of confirmed cases within the next week. The model demonstrated a promising prediction result with a coefficient of determination () of up to approximately 0.91. Furthermore, six essential observations with supporting evidence are presented, which will be helpful for decision-makers to take specific measures that aid in preventing the spread of COVID-19 and protecting public health and safety. The proposed approaches could be applied, customized, adjusted, and replicated for analysis of the impact of COVID-19 on a transportation network and prediction of the anticipated COVID-19 cases using a similar data set obtained for other large cities in the USA or from around the world.

Modelling the impacts of COVID-19 pandemic on the quality of population-based colorectal cancer screening

COVID-19 pandemic has severely affected regular public health interventions including population-based cancer screening. Impacts of such screening delays on the changes in structure and screening process and the resultant long-term outcomes are unknown. It is therefore necessary to develop a systematic framework to assess theses impacts related to these components of quality. Using population-based cancer screening with fecal immunochemical test (FIT) as an illustration, the main analysis was to assess how various scenarios of screening delays were associated with the capacity for primary screening and full time equivalent (FTE) for colonoscopy and impact long-term outcomes based on a Markov decision tree model on population level. The second analysis was to quantify how the extent of COVID-19 epidemic measured by social distancing index affected capacity and FTE that were translated to delays with an exponential relationship. COVID-19 epidemic led to 25%, 29%, 34%, and 39% statistically significantly incremental risks of late cancer for the delays of 0.5-year, 1-year,1.5-year, and 2-year, respectively compared with regular biennial FIT screening. The corresponding statistically findings of four delayed schedules for death from colorectal cancer (CRC) were 26%, 28%, 29%, and 30%, respectively. The higher social distancing index led to a lower capacity of uptake screening and a larger reduction of FTE, resulting in longer screening delay and longer waiting time, which further impacted long-term outcomes as above. In summary, a systematic modelling approach was developed for demonstrating the strong impact of screening delays caused by COVID-19 epidemic on long-term outcomes illustrated with a Taiwan population-based FIT screening of CRC.

Differential impact of mitigation policies and socioeconomic status on COVID-19 prevalence and social distancing in the United States

BackgroundThe spread of COVID-19 has highlighted the long-standing health inequalities across the U.S. as neighborhoods with fewer resources were associated with higher rates of COVID-19 transmission. Although the stay-at-home order was one of the most effective methods to contain its spread, residents in lower-income neighborhoods faced barriers to practicing social distancing. We aimed to quantify the differential impact of stay-at-home policy on COVID-19 transmission and residents’ mobility across neighborhoods of different levels of socioeconomic disadvantage.MethodsThis was a comparative interrupted time-series analysis at the county level. We included 2087 counties from 38 states which both implemented and lifted the state-wide stay-at-home order. Every county was assigned to one of four equally-sized groups based on its levels of disadvantage, represented by the Area Deprivation Index. Prevalence of COVID-19 was calculated by dividing the daily number of cumulative confirmed COVID-19 cases by the number of residents from the 2010 Census. We used the Social Distancing Index (SDI), derived from the COVID-19 Impact Analysis Platform, to measure the mobility. For the evaluation of implementation, the observation started from Mar 1st 2020 to 1 day before lifting; and, for lifting, it ranged from 1 day after implementation to Jul 5th 2020. We calculated a comparative change of daily trends in COVID-19 prevalence and Social Distancing Index between counties with three highest disadvantage levels and those with the least level before and after the implementation and lifting of the stay-at-home order, separately.ResultsOn both stay-at-home implementation and lifting dates, COVID-19 prevalence was much higher among counties with the highest or lowest disadvantage level, while mobility decreased as the disadvantage level increased. Mobility of the most disadvantaged counties was least impacted by stay-at-home implementation and relaxation compared to counties with the most resources; however, disadvantaged counties experienced the largest relative increase in COVID-19 infection after both stay-at-home implementation and relaxation.ConclusionsNeighborhoods with varying levels of socioeconomic disadvantage reacted differently to the implementation and relaxation of COVID-19 mitigation policies. Policymakers should consider investing more resources in disadvantaged counties as the pandemic may not stop until most neighborhoods have it under control.