scholarly journals The Association of Social Distancing, Population Density, and Temperature with the SARS-CoV-2 Instantaneous Reproduction Number in Counties Across the United States

2020 ◽  
Author(s):  
David Rubin ◽  
Jing Huang ◽  
Brian T. Fisher ◽  
Antonio Gasparrini ◽  
Vicky Tam ◽  
...  
Keyword(s):  
United States ◽  
Population Density ◽  
Reproduction Number ◽  
Multivariable Analysis ◽  
The United States ◽  
Fatality Rate ◽  
Social Distancing ◽  
Specific Factors ◽  
Lower Population ◽  
Lower Population Density

AbstractImportanceThe Covid-19 pandemic has been marked by considerable heterogeneity in outbreaks across the United States. Local factors that may be associated with variation in SARS-CoV-2 transmission have not been well studied.ObjectiveTo examine the association of county-level factors with variation in the SARS-CoV-2 reproduction number over time.DesignObservational studySetting211 counties in 46 states and the District of Columbia between February 25, 2020 and April 23, 2020.ParticipantsResidents within the counties (55% of the US population)ExposuresSocial distancing as measured by percent change in visits to non-essential businesses, population density, lagged daily wet bulb temperatures.Main Outcomes and MeasuresThe instantaneous reproduction number (Rt) which is the estimated number of cases generated by one case at a given time during the pandemic.ResultsMedian case incidence was 1185 cases and fatality rate was 43.7 deaths per 100,000 people for the top decile of 21 counties, nearly ten times the incidence and fatality rate in the lowest density quartile. Average Rt in the first two weeks was 5.7 (SD 2.5) in the top decile, compared to 3.1 (SD 1.2) in the lowest quartile. In multivariable analysis, a 50% decrease in visits to non-essential businesses was associated with a 57% decrease in Rt (95% confidence interval, 56% to 58%). Cumulative temperature effects over 4 to 10 days prior to case incidence were nonlinear; relative Rt decreased as temperatures warmed above 32°F to 53°F, which was the point of minimum Rt, then increased between 53°F and 66°F, at which point Rt began to decrease. At 55°F, and with a 70% reduction in visits to non-essential business, 96% of counties were estimated to fall below a threshold Rt of 1.0, including 86% of counties among the top density decile and 98% of counties in the lowest density quartile.Conclusions and RelevanceSocial distancing, lower population density, and temperate weather change were associated with a decreased SARS-Co-V-2 Rt in counties across the United States. These relationships can inform selective public policy planning in communities during the SARS-CoV-2 pandemic.Key PointsQuestionHow is the instantaneous reproduction number (Rt) of SARS-CoV-2 influenced by local area effects of social distancing, wet bulb temperature, and population density in counties across the United States?FindingsSocial distancing, temperate weather, and lower population density were associated with a decrease in Rt. Of these county-specific factors, social distancing appeared to be the most significant in reducing SARS-CoV-2 transmission.MeaningRt varies significantly across counties. The relationship between Rt and county-specific factors can inform policies to reduce SARS-CoV-2 transmission in selective and heterogeneous communities.

Mitigation of COVID Strategies Comparison Between United States and Europe

2021 ◽  
pp. 359-382
Author(s):  
Mercedes Barrachina ◽  
Lucia Barrachina
Keyword(s):  
United States ◽  
Population Density ◽  
The United States ◽  
American Government ◽  
Additional Time ◽  
Hard Times ◽  
Medium Businesses ◽  
The Impact ◽  
Lower Population ◽  
Lower Population Density

The COVID-19 pandemic started in China at the end of 2019; however, during 2020, it has spread to more than 188 countries causing very hard times. Europe and the United States have followed different strategies to fight the virus. The differences between those areas in relation with the pandemic could be named shortly as for example the additional time that the United States had to prepare everything against the pandemic compared to Europe, as the American government had around three weeks in comparison to Europe to plan the strategy against the pandemic. The density of population is also an example of the differences between those areas as the United States has a lower population density compared to Europe, and this is another key fact affecting the spreading of COVID-19. The main objective of the study is to compare the different measures adopted by each region and analyze the impact they have in the economy and in small and medium businesses. Specific conclusions about the impact of the measures adopted will be extracted, and some lessons could be obtained from those conclusions.

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 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 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.

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 Social Media as an Early Proxy for Social Distancing Indicated by the COVID-19 Reproduction Number: Observational Study (Preprint)

2020 ◽  
Author(s):  
Joseph Younis ◽  
Harvy Freitag ◽  
Jeremy S Ruthberg ◽  
Jonathan P Romanes ◽  
Craig Nielsen ◽  
...  
Keyword(s):  
United States ◽  
Social Media ◽  
Observational Study ◽  
Social Mobility ◽  
Reproduction Number ◽  
The United States ◽  
Google Trends ◽  
Basic Reproductive Number ◽  
Social Distancing ◽  
Google Search

BACKGROUND  The magnitude and time course of the COVID-19 epidemic in the United States depends on early interventions to reduce the basic reproductive number to below 1. It is imperative, then, to develop methods to actively assess where quarantine measures such as social distancing may be deficient and suppress those potential resurgence nodes as early as possible. OBJECTIVE We ask if social media is an early indicator of public social distancing measures in the United States by investigating its correlation with the time-varying reproduction number (R<sub>t</sub>) as compared to social mobility estimates reported from Google and Apple Maps. METHODS  In this observational study, the estimated R<sub>t</sub> was obtained for the period between March 5 and April 5, 2020, using the EpiEstim package. Social media activity was assessed using queries of “social distancing” or “#socialdistancing” on Google Trends, Instagram, and Twitter, with social mobility assessed using Apple and Google Maps data. Cross-correlations were performed between R<sub>t</sub> and social media activity or mobility for the United States. We used Pearson correlations and the coefficient of determination (ρ) with significance set to <i>P</i>&lt;.05. RESULTS Negative correlations were found between Google search interest for “social distancing” and R<sub>t</sub> in the United States (<i>P</i>&lt;.001), and between search interest and state-specific R<sub>t</sub> for 9 states with the highest COVID-19 cases (<i>P</i>&lt;.001); most states experienced a delay varying between 3-8 days before reaching significance. A negative correlation was seen at a 4-day delay from the start of the Instagram hashtag “#socialdistancing” and at 6 days for Twitter (<i>P</i>&lt;.001). Significant correlations between R<sub>t</sub> and social media manifest earlier in time compared to social mobility measures from Google and Apple Maps, with peaks at –6 and –4 days. Meanwhile, changes in social mobility correlated best with R<sub>t</sub> at –2 days and +1 day for workplace and grocery/pharmacy, respectively. CONCLUSIONS Our study demonstrates the potential use of Google Trends, Instagram, and Twitter as epidemiological tools in the assessment of social distancing measures in the United States during the early course of the COVID-19 pandemic. Their correlation and earlier rise and peak in correlative strength with R<sub>t</sub> when compared to social mobility may provide proactive insight into whether social distancing efforts are sufficiently enacted. Whether this proves valuable in the creation of more accurate assessments of the early epidemic course is uncertain due to limitations. These limitations include the use of a biased sample that is internet literate with internet access, which may covary with socioeconomic status, education, geography, and age, and the use of subtotal social media mentions of social distancing. Future studies should focus on investigating how social media reactions change during the course of the epidemic, as well as the conversion of social media behavior to actual physical behavior.

scholarly journals Social Media as an Early Proxy for Social Distancing Indicated by the COVID-19 Reproduction Number: Observational Study

10.2196/21340 ◽  
2020 ◽  
Vol 6 (4) ◽  
pp. e21340 ◽  
Author(s):  
Joseph Younis ◽  
Harvy Freitag ◽  
Jeremy S Ruthberg ◽  
Jonathan P Romanes ◽  
Craig Nielsen ◽  
...  
Keyword(s):  
United States ◽  
Social Media ◽  
Observational Study ◽  
Social Mobility ◽  
Reproduction Number ◽  
The United States ◽  
Google Trends ◽  
Basic Reproductive Number ◽  
Social Distancing ◽  
Google Search

Background  The magnitude and time course of the COVID-19 epidemic in the United States depends on early interventions to reduce the basic reproductive number to below 1. It is imperative, then, to develop methods to actively assess where quarantine measures such as social distancing may be deficient and suppress those potential resurgence nodes as early as possible. Objective We ask if social media is an early indicator of public social distancing measures in the United States by investigating its correlation with the time-varying reproduction number (Rt) as compared to social mobility estimates reported from Google and Apple Maps. Methods  In this observational study, the estimated Rt was obtained for the period between March 5 and April 5, 2020, using the EpiEstim package. Social media activity was assessed using queries of “social distancing” or “#socialdistancing” on Google Trends, Instagram, and Twitter, with social mobility assessed using Apple and Google Maps data. Cross-correlations were performed between Rt and social media activity or mobility for the United States. We used Pearson correlations and the coefficient of determination (ρ) with significance set to P<.05. Results Negative correlations were found between Google search interest for “social distancing” and Rt in the United States (P<.001), and between search interest and state-specific Rt for 9 states with the highest COVID-19 cases (P<.001); most states experienced a delay varying between 3-8 days before reaching significance. A negative correlation was seen at a 4-day delay from the start of the Instagram hashtag “#socialdistancing” and at 6 days for Twitter (P<.001). Significant correlations between Rt and social media manifest earlier in time compared to social mobility measures from Google and Apple Maps, with peaks at –6 and –4 days. Meanwhile, changes in social mobility correlated best with Rt at –2 days and +1 day for workplace and grocery/pharmacy, respectively. Conclusions Our study demonstrates the potential use of Google Trends, Instagram, and Twitter as epidemiological tools in the assessment of social distancing measures in the United States during the early course of the COVID-19 pandemic. Their correlation and earlier rise and peak in correlative strength with Rt when compared to social mobility may provide proactive insight into whether social distancing efforts are sufficiently enacted. Whether this proves valuable in the creation of more accurate assessments of the early epidemic course is uncertain due to limitations. These limitations include the use of a biased sample that is internet literate with internet access, which may covary with socioeconomic status, education, geography, and age, and the use of subtotal social media mentions of social distancing. Future studies should focus on investigating how social media reactions change during the course of the epidemic, as well as the conversion of social media behavior to actual physical behavior.

scholarly journals Nigeria and Italy Divergences in Coronavirus Experience: Impact of Population Density

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Emmanuel O. Amoo ◽  
Olujide Adekeye ◽  
Adebanke Olawole-Isaac ◽  
Fagbeminiyi Fasina ◽  
Paul O. Adekola ◽  
...  
Keyword(s):  
Population Density ◽  
Qualitative Description ◽  
Fatality Rate ◽  
Rapid Spread ◽  
Daily Change ◽  
Physician Patient ◽  
The Impact ◽  
Similar Experience ◽  
Lower Population ◽  
Lower Population Density

Background. The reports and information on coronavirus are not conspicuously emphasising the possible impact of population density on the explanation of difference in rapid spread and fatality due to the disease and not much has been done on bicountry comparisons. Objective. The study examined the impact of population density on the spread of COVID-19 pandemic in two sociodemographic divergent countries. Methods. The study conducted a scoping review of published and unpublished articles including blogs on incidences and fatalities of COVID-19. The analysis followed qualitative description and quantitative presentation of the findings using only frequency distribution, percentages, and graphs. Results. The two countries shared similar experience of “importation” of COVID-19, but while different states ordered partial lockdown in Nigeria, it was an immediate total lockdown in Italy. The physician/patient ratio is high in Italy (1 : 328) but low in Nigeria (1 : 2500), while population density is 221 in Nigeria and 206 in Italy. Daily change in incidence rate reduced to below 20% after 51 and 30 days of COVID-19 first incidence in Italy and Nigeria, respectively. Fatality rate has plummeted to below 10% after the 66th day in Italy but has not been stabilised in Nigeria. Conclusion. The authors upheld both governments’ recommending measures that tilted towards personal hand-hygienic practices and social distancing. Authors suggested that if Italy with its high physician/patient ratio and lower population density compared to Nigeria could suffer high fatality from COVID-19 pandemic under four weeks, then Nigeria with its low physician/patient ratio and higher population density should prepare to face harder time if the pandemic persists.

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