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 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.

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.

THE IMPACT OF SMALL AND MEDIUM BUSINESSES ON THE UNITED STATES ECONOMY

2015 ◽  
Vol 15 (1) ◽  
pp. 77-82
Author(s):  
Nathaniel Sanchez ◽  
Balasundram Maniam ◽  
Hadley Leavell
Keyword(s):  
United States ◽  
The United States ◽  
United States Economy ◽  
Medium Businesses ◽  
The Impact

scholarly journals The Spatiotemporal Interaction Effect of COVID-19 Transmission in the United States

2021 ◽  
Vol 10 (6) ◽  
pp. 387
Author(s):  
Lingbo Liu ◽  
Tao Hu ◽  
Shuming Bao ◽  
Hao Wu ◽  
Zhenghong Peng ◽  
...  
Keyword(s):  
United States ◽  
Population Density ◽  
Spatial Autocorrelation ◽  
Low Income ◽  
Pearson Correlation ◽  
The United States ◽  
Time Varying ◽  
Ols Regression ◽  
Highly Educated ◽  
The Impact

(1) Background: Human mobility between geographic units is an important way in which COVID-19 is spread across regions. Due to the pressure of epidemic control and economic recovery, states in the United States have adopted different policies for mobility limitations. Assessing the impact of these policies on the spatiotemporal interaction of COVID-19 transmission among counties in each state is critical to formulating epidemic policies. (2) Methods: We utilized Moran’s I index and K-means clustering to investigate the time-varying spatial autocorrelation effect of 49 states (excluding the District of Colombia) with daily new cases at the county level from 22 January 2020 to 20 August 2020. Based on the dynamic spatial lag model (SLM) and the SIR model with unreported infection rate (SIRu), the integrated SLM-SIRu model was constructed to estimate the inter-county spatiotemporal interaction coefficient of daily new cases in each state, which was further explored by Pearson correlation test and stepwise OLS regression with socioeconomic factors. (3) Results: The K-means clustering divided the time-varying spatial autocorrelation curves of the 49 states into four types: continuous increasing, fluctuating increasing, weak positive, and weak negative. The Pearson correlation analysis showed that the spatiotemporal interaction coefficients in each state estimated by SLM-SIRu were significantly positively correlated with the variables of median age, population density, and proportions of international immigrants and highly educated population, but negatively correlated with the birth rate. Further stepwise OLS regression retained only three positive correlated variables: poverty rate, population density, and highly educated population proportion. (4) Conclusions: This result suggests that various state policies in the U.S. have imposed different impacts on COVID-19 transmission among counties. All states should provide more protection and support for the low-income population; high-density populated states need to strengthen regional mobility restrictions; and the highly educated population should reduce unnecessary regional movement and strengthen self-protection.

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.

scholarly journals Influence of Socio-Economic, Demographic and Climate Factors on the Regional Distribution of Dengue in the United States and Mexico

2020 ◽  
Author(s):  
Matthew Watts ◽  
Panagiota Kotsilla ◽  
P Graham Mortyn ◽  
Victor Sarto i Monteys ◽  
Cesira Urzi Brancati
Keyword(s):  
United States ◽  
Factor Analysis ◽  
Population Density ◽  
Age Structure ◽  
Regional Distribution ◽  
The United States ◽  
Economic Indicators ◽  
Climate Factors ◽  
Demographic Indicators ◽  
The Impact

Abstract Background: Dengue is one of the important vector-borne diseases in the world today; it infects tens of millions of people each year and has been on the rise since the 1950s. In this study, we develop a set of indicators that help us examine the impact of socio-economic and demographic factors on the occurrence of dengue in regions of the United States and Mexico. Methods: We assess the relationship between dengue occurrence in humans, climate factors (temperature and minimum quarterly rainfall), socio-economic factors (such as household income, regional rates of education, housing overcrowding, life expectancy, and medical resources), and demographic factors (such as migration flows, age structure of the population, and population density). Areas at risk of dengue are first selected based on the predicted presence of at least one of the two mosquito vectors responsible for dengue’s transmission: Aedes aegypti and Aedes albopictus. In those regions where the vectors had a high probability of presence, we assess the impact of the composite socio-economic indicators (derived through factor analysis to account for collinearity), and three composite demographic indicators (also derived from factor analysis) on the regional distribution of dengue cases, controlling for climate and spatial correlation. Results: We found that an increase of one unit in one of our socio-economic indicators representing labour force with at least secondary education, better broadband access, and rooms per inhabitant, a higher proportions of active physicians is related to a drop in the occurrence of dengue, whereas the demographic indicators such as population density, age structure of the population and population growth showed no significant impact after taking climate into account. More importantly, our socio-economic indicator can also explain differences in the occurrence of dengue across Mexico, whereas simpler measures, such as regional GDP could not. Conclusions: These results suggest that the set of indicators developed is a better indicator than GDP at predicting the distribution of dengue, by capturing information that is much more tailored to poverty related conditions which aid dengue transmission. Given that data for these indicators are available at a sub-national scale for OECD countries and selected OECD non-member economies, these indices may help us better understand factors responsible for the global distribution of dengue and also, given a warming climate, may help us to better predict vulnerable populations.

Against the Asian tide: the Sudan divestment campaign

2009 ◽  
Vol 47 (4) ◽  
pp. 551-573 ◽  
Author(s):  
Luke A. Patey
Keyword(s):  
United States ◽  
Financial Market ◽  
The United States ◽  
American Government ◽  
Economic Relations ◽  
Oil Companies ◽  
National Oil Companies ◽  
Strong Current ◽  
The Impact ◽  
Pressure State

ABSTRACTThe efforts of American activists to pressure Asian corporations in Sudan have to date resembled a struggle to find the light switch in the dark, or swimming against a strong current. While the impact of the divestment campaign in the United States has been increasingly evident, its effectiveness in producing actual results in Sudan remains suspect. Thanks to China and a trio of Asian national oil companies, oil still flows in Sudan. The campaign's activities have failed to incorporate Sudan's wider international political and economic relations into its strategy. It has rather paradoxically sought to pressure state-owned corporations through financial market divestment. The nature of its Asian targets, reluctant Western investors and a distracted American government have obstructed the campaign from having a resounding impact in Sudan.

scholarly journals Understanding the Impact of Walkability, Population Density, and Population Size on COVID-19 Spread: A Pilot Study of the Early Contagion in the United States

Entropy ◽  
2021 ◽  
Vol 23 (11) ◽  
pp. 1512
Author(s):  
Fernando T. Lima ◽  
Nathan C. Brown ◽  
José P. Duarte
Keyword(s):  
United States ◽  
Los Angeles ◽  
Population Density ◽  
Population Size ◽  
Spatial Configuration ◽  
The United States ◽  
Population Characteristics ◽  
Clear Correlation ◽  
Population Mixing ◽  
The Impact

The novel coronavirus disease 2019 (COVID-19) pandemic is an unprecedented global event that has been challenging governments, health systems, and communities worldwide. Available data from the first months indicated varying patterns of the spread of COVID-19 within American cities, when the spread was faster in high-density and walkable cities such as New York than in low-density and car-oriented cities such as Los Angeles. Subsequent containment efforts, underlying population characteristics, variants, and other factors likely affected the spread significantly. However, this work investigates the hypothesis that urban configuration and associated spatial use patterns directly impact how the disease spreads and infects a population. It follows work that has shown how the spatial configuration of urban spaces impacts the social behavior of people moving through those spaces. It addresses the first 60 days of contagion (before containment measures were widely adopted and had time to affect spread) in 93 urban counties in the United States, considering population size, population density, walkability, here evaluated through walkscore, an indicator that measures the density of amenities, and, therefore, opportunities for population mixing, and the number of confirmed cases and deaths. Our findings indicate correlations between walkability, population density, and COVID-19 spreading patterns but no clear correlation between population size and the number of cases or deaths per 100 k habitants. Although virus spread beyond these initial cases may provide additional data for analysis, this study is an initial step in understanding the relationship between COVID-19 and urban configuration.

scholarly journals The Spatiotemporal Interaction Effect of COVID-19 Transmission in the United States

2021 ◽  
Author(s):  
Lingbo Liu ◽  
Tao Hu ◽  
Shuming Bao ◽  
Hao Wu ◽  
Zhenghong Peng ◽  
...  
Keyword(s):  
United States ◽  
Population Density ◽  
Spatial Autocorrelation ◽  
Low Income ◽  
Pearson Correlation ◽  
The United States ◽  
Time Varying ◽  
Ols Regression ◽  
Highly Educated ◽  
The Impact

Abstract Background: Human mobility among geographic units is a possible cause of the widespread transmission of COVID-19 across regions. Due to the pressure of epidemic control and economic recovery, the states of the United States have adopted different policies for mobility limitations. Assessing the impact of these policies on the spatiotemporal interaction of COVID-19 transmission among counties in each state is critical to formulating the epidemic policies.Methods: The study utilized Moran’s I index and K-means clustering to investigate the time-varying spatial autocorrelation effect of 49 states (except the District of Colombia) with the daily new cases at the county level from Jan 22, 2020, to August 20, 2020. Based on the dynamic spatial lag model (SLM) and the SIR model with unreported infection rate (SIRu), the integrated SLM-SIRu model was constructed to estimate the inter-county spatiotemporal interaction coefficient of daily new cases in each state, which was further explored by Pearson correlation and stepwise OLS regression with socioeconomic factors.Results: The K-means clustering divided the time-varying spatial autocorrelation curves of 49 states into four types: continuous increasing, fluctuating increasing, weak positive, and weak negative. The Pearson correlation analysis showed that the spatiotemporal interaction coefficients in each state estimated by SLM-SIRu were significantly positively correlated with median age, population density, and the proportion of international immigrants and the highly educated population, but negatively correlated with the birth rate. The voting rate for Donald Trump in the 2016 U.S. presidential election showed a weak negative correlation. Further stepwise OLS regression retained only three positive correlated variables: poverty rate, population density, and the highly educated population proportion.Interpretation: This result suggests that various state policies in the U.S. have imposed different impacts on COVID-19 transmission among counties. All states should provide more protection and support for the low-income population, high-density populated states need to strengthen regional mobility restrictions, and the highly educated population should reduce unnecessary regional movement and strengthen self-protection.

Sign in / Sign up

Export Citation Format

Share Document