scholarly journals How Climate Variables Influence the Spread of SARS-CoV-19 in the United States

2020 ◽  
Vol 12 (21) ◽  
pp. 9192
Author(s):  
André de Souza Melo ◽  
Ana Iza Gomes da Penha Sobral ◽  
Marcelo Luiz Monteiro Marinho ◽  
Gisleia Benini Duarte ◽  
Thiago Henrique Ferreira Gomes ◽  
...  
Keyword(s):  
United States ◽  
Infection Rate ◽  
The United States ◽  
Infection Model ◽  
Climate Variables ◽  
Temperature Variations ◽  
Infected People ◽  
Average Temperature ◽  
Weather Stations ◽  
The Impact

During the 2020 Coronavirus pandemic, several scientific types of research investigated the causes of high transmissibility and deaths caused by SARS-CoV-2. Among the spreading factors of the disease, it is known that there is an association between temperature and infected people. However, the studies that identified this phenomenon explored an association relationship, which is weaker and does not allow the identification of which variable would be the cause. This study aimed to analyze the impact of temperature variations and other climatic variables on the infection rate of COVID-19. Data were extracted from weather stations in the United States, which were segregated by county and day. Daily COVID-19 infections and deaths per county were also collected. Two models were used: the first model to analyze the temperature and the number of infected cases and the second model to evaluate the variables of temperature, precipitation, and snow in relation to COVID-19 infection. Model 1 shows that an increase in temperature at time zero caused a decrease in the number of infected cases. Meanwhile, a decrease in temperature after the temperature shock was associated with an increase in the number of cases, which tended to zero overall. A 1% increase in temperature caused a 0.002% decrease in the number of cases. The results suggested a causal relationship between the average temperature and number of CODIV-19 cases. Model 2, which includes temperature, precipitation, and snow shows that an increase in temperature resulted in a 0.00154% decrease response. There was no significant effect of increased precipitation and snow on the infection rate with COVID-19.

Racism: A Public Health Crisis

2020 ◽  
Vol 19 (3) ◽  
pp. 506-515
Author(s):  
Katie L. Acosta
Keyword(s):  
United States ◽  
General Population ◽  
The United States ◽  
Systemic Racism ◽  
Health Crisis ◽  
Infected People ◽  
The Face ◽  
The Impact ◽  
Racial Ethnic ◽  
Bear Witness

The impact of COVID–19 on racially minoritized communities in the United States has forced us all to look square in the face of the systemic racism that is embedded in every fabric of our society. As the number of infected people continues to rise, the racial disparities are glaringly obvious. Black and Latinx communities have been hit considerably harder by this pandemic. Both racial/ethnic groups have seen rates of infection well above their percentage in the general population and African Americans have seen rates of death from COVID–19 as high as twice their percentage in the general population. These numbers bear witness to the high cost of racism in the United States.

scholarly journals Pharmacodynamic Evaluation of the Activities of Six Parenteral Vancomycin Products Available in the United States

2014 ◽  
Vol 59 (1) ◽  
pp. 622-632 ◽  
Author(s):  
Arnold Louie ◽  
Michael T. Boyne ◽  
Vikram Patel ◽  
Clayton Huntley ◽  
Weiguo Liu ◽  
...  
Keyword(s):  
United States ◽  
Drug Exposure ◽  
The United States ◽  
Mouse Serum ◽  
Infection Model ◽  
Population Pharmacokinetic ◽  
Bacterial Killing ◽  
The Impact

ABSTRACTA recent report found that generic parenteral vancomycin products may not havein vivoefficacies equivalent to those of the innovator in a neutropenic murine thigh infection model despite having similarin vitromicrobiological activities and murine serum pharmacokinetics. We compared thein vitroandin vivoactivities of six of the parenteral vancomycin products available in the United States. Thein vitroassessments for the potencies of the vancomycin products included MIC/minimal bactericidal concentration (MBC) determinations, quantifying the impact of human and murine serum on the MIC values, and time-kill studies. Also, the potencies of the vancomycin products were quantified with a biological assay, and the human and mouse serum protein binding rates for the vancomycin products were measured. Thein vivostudies included dose-ranging experiments with the 6 vancomycin products for three isolates ofStaphylococcus aureusin a neutropenic mouse thigh infection model. The pharmacokinetics of the vancomycin products were assessed in infected mice by population pharmacokinetic modeling. No differences were seen across the vancomycin products with regard to anyin vitroevaluation. Inhibitory sigmoid maximal bacterial kill (Emax) modeling of the relationship between vancomycin dosage and the killing of the bacteria in micein vivoyielded similarEmaxand EC50(drug exposure driving one-halfEmax) values for bacterial killing. Further, there were no differences in the pharmacokinetic clearances of the 6 vancomycin products from infected mice. There were no important pharmacodynamic differences in thein vitroorin vivoactivities among the six vancomycin products evaluated.

scholarly journals A transnational and transregional study of the impact and effectiveness of social distancing for COVID-19 mitigation

2021 ◽  
Author(s):  
Tarcísio M. Rocha Filho ◽  
Marcelo A. Moret ◽  
José F. F. Mendes
Keyword(s):  
United States ◽  
Infection Rate ◽  
Rank Order ◽  
The United States ◽  
Social Distancing ◽  
The United Kingdom ◽  
The Social ◽  
The Impact ◽  
The Relationship ◽  
Spearman’S Correlation

AbstractWe present an analysis of the relationship between SARS-CoV-2 infection rates and a social distancing metric from data for all the states and most populous cities in the United States and Brazil, all the 22 European Economic Community countries and the United Kingdom. We discuss why the infection rate, instead of the effective reproduction number or growth rate of cases, is a proper choice to perform this analysis when considering a wide span of time. We obtain a strong Spearman’s rank order correlation between the social distancing metric and the infection rate in each locality. We show that mask mandates increase the values of Spearman’s correlation in the United States, where a mandate was adopted. We also obtain an explicit numerical relation between the infection rate and the social distancing metric defined in the present work.

scholarly journals A Transnational and Transregional Study of the Impact and Effectiveness of Social Distancing for COVID-19 Mitigation

Entropy ◽  
2021 ◽  
Vol 23 (11) ◽  
pp. 1530
Author(s):  
Tarcísio M. Rocha Filho ◽  
Marcelo A. Moret ◽  
José F. F. Mendes
Keyword(s):  
United States ◽  
Infection Rate ◽  
Rank Order ◽  
The United States ◽  
Social Distancing ◽  
The United Kingdom ◽  
The Social ◽  
The Impact ◽  
The Relationship ◽  
Spearman’S Correlation

We present an analysis of the relationship between SARS-CoV-2 infection rates and a social distancing metric from data for all the states and most populous cities in the United States and Brazil, all the 22 European Economic Community countries and the United Kingdom. We discuss why the infection rate, instead of the effective reproduction number or growth rate of cases, is a proper choice to perform this analysis when considering a wide span of time. We obtain a strong Spearman’s rank order correlation between the social distancing metric and the infection rate in each locality. We show that mask mandates increase the values of Spearman’s correlation in the United States, where a mandate was adopted. We also obtain an explicit numerical relation between the infection rate and the social distancing metric defined in the present work.

scholarly journals Estimating the number of SARS-CoV-2 infections in the United States

2020 ◽  
Author(s):  
Dayton G. Thorpe ◽  
Kelsey Lyberger
Keyword(s):  
United States ◽  
New York ◽  
Infection Rate ◽  
New York State ◽  
Herd Immunity ◽  
The United States ◽  
Public Events ◽  
Response Team ◽  
The Impact ◽  
Test Result

AbstractWe apply a model developed by The COVID-19 Response Team [S. Flaxman, S. Mishra, A. Gandy, et al., “Estimating the number of infections and the impact of non-pharmaceutical interventions on COVID-19 in 11 European countries,” tech. rep., Imperial College London, 2020.] to estimate the total number of SARS-CoV-2 infections in the United States. Across the United States we estimate as of April 18, 2020 the fraction of the population infected was 4.6% [3.6%, 5.8%], 21 times the portion of the population with a positive test result. Excluding New York state, which we estimate accounts for over half of infections in the United States, we estimate an infection rate of 2.3% [2.1%, 2.8%].We include the timing of each state’s implementation of interventions including encouraging social distancing, closing schools, banning public events, and a lockdown / stay-at-home order. We assume fatalities are reported correctly and infer the number and timing of infections based on the infection fatality rate measured in populations that were tested universally for SARS-CoV-2. Underreporting of deaths would drive our estimates to be too low. Reporting of deaths on the wrong day could drive errors in either direction. This model does not include effects of herd immunity; in states where the estimated infection rate is very high - namely, New York - our estimates may be too high.

scholarly journals Classifying Texas counties using ARIMA Models on COVID-19 daily confirmed cases: the impact of political affiliation and face covering orders

2021 ◽  
Author(s):  
Hao Jiang ◽  
Brigitta Pulins ◽  
Aurelie Thiele
Keyword(s):  
United States ◽  
Infection Rate ◽  
The United States ◽  
Poverty Rate ◽  
Model Parameters ◽  
County Level ◽  
Arima Models ◽  
High Positive Correlation ◽  
Positive Correlation ◽  
The Impact

The aim of this paper is to investigate whether the 254 Texas counties in the United States can be grouped in a meaningful way according to the characteristics of the ARIMA or seasonal ARIMA models fitting the logarithm of daily confirmed cases of the Coronavirus Disease 2019 (COVID-19) for 254 counties in Texas of the United States. We analyze clusters of the model's non-seasonal parameters $(p,d,q)$, distinguishing between county-level political affiliations and face covering orders, and also consider county-level population and poverty rate. Using data from March 4, 2020 to March 15, 2021, we find that 223 of the total 254 counties are clustered into 23 model parameters $(p,d,q)$, while the number of cases in the remaining 31 counties could not be successfully fitted to ARIMA models. We also find the impact of the county-level infection rate and the county-level poverty rate on clusters of counties with different political affiliations and face covering orders. Further, we find that the infection rate and the poverty rate had a significant high positive correlation, and Democrat-leaning counties, which tend to have large populations, had a higher correlation coefficient between infection rate and poverty rate. We also observe a significant high positive correlation between the infection rate and the number of cumulative cases in Republican counties that had not imposed a face covering order.

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