scholarly journals Early and massive testing saves lives: COVID-19 related infections and deaths in the United States during March of 2020

2020 ◽  
Author(s):  
James B. Hittner ◽  
Folorunso O. Fasina ◽  
Almira L. Hoogesteijn ◽  
Renata Piccinini ◽  
Prakasha Kempaiah ◽  
...  
Keyword(s):  
Population Density ◽  
Prevalence Rate ◽  
The United States ◽  
Cumulative Number ◽  
Least Squares Regression ◽  
Predictors Of Mortality ◽  
Apparent Prevalence ◽  
Case Count ◽  
The Us ◽  
Early Testing

To optimize epidemiologic interventions, predictors of mortality should be identified. The US COVID-19 epidemic data, reported up to 31 March 2020, were analyzed using kernel regularized least squares regression. Six potential predictors of mortality were investigated: (i) the number of diagnostic tests performed in testing week I; (ii) the proportion of all tests conducted during week I of testing; (iii) the cumulative number of (test-positive) cases through 3-31-2020, (iv) the number of tests performed/million citizens; (v) the cumulative number of citizens tested; and (vi) the apparent prevalence rate, defined as the number of cases/million citizens. Two metrics estimated mortality: the number of deaths and the number of deaths/million citizens. While both expressions of mortality were predicted by the case count and the apparent prevalence rate, the number of deaths/million citizens was ≈3.5 times better predicted by the apparent prevalence rate than the number of cases. In eighteen states, early testing/million citizens/population density was inversely associated with the cumulative mortality reported by 31 March, 2020. Findings support the hypothesis that early and massive testing saves lives. Other factors —e.g., population density— may also influence outcomes. To optimize national and local policies, the creation and dissemination of high resolution geo-referenced, epidemic data is recommended.

scholarly journals Quantifying the effect of quarantine control in Covid-19 infectious spread using machine learning

2020 ◽  
Author(s):  
Raj Dandekar ◽  
George Barbastathis
Keyword(s):  
Neural Network ◽  
South Korea ◽  
The United States ◽  
Government Interventions ◽  
Infected Case ◽  
Health Measures ◽  
Case Count ◽  
The Us ◽  
Spread Of Infection ◽  
Limited Testing

Since the first recording of what we now call Covid-19 infection in Wuhan, Hubei province, China on Dec 31, 2019 (CHP 2020), the disease has spread worldwide and met with a wide variety of social distancing and quarantine policies. The effectiveness of these responses is notoriously difficult to quantify as individuals travel, violate policies deliberately or inadvertently, and infect others without themselves being detected (Li et al. 2020a; Wu & Leung 2020; Wang et al. 2020; Chinazzi et al. 2020; Ferguson et al. 2020; Kraemer et al. 2020). Moreover, the publicly available data on infection rates are themselves unreliable due to limited testing and even possibly under-reporting (Li et al. 2020b). In this paper, we attempt to interpret and extrapolate from publicly available data using a mixed first-principles epidemiological equations and data-driven neural network model. Leveraging our neural network augmented model, we focus our analysis on four locales: Wuhan, Italy, South Korea and the United States of America, and compare the role played by the quarantine and isolation measures in each of these countries in controlling the effective reproduction number Rt of the virus. Our results unequivocally indicate that the countries in which rapid government interventions and strict public health measures for quarantine and isolation were implemented were successful in halting the spread of infection and prevent it from exploding exponentially. In the case of Wuhan especially, where the available data were earliest available, we have been able to test the predicting ability of our model by training it from data in the January 24th till March 3rd window, and then matching the predictions up to April 1st. Even for Italy and South Korea, we have a buffer window of one week (25 March - 1 April) to validate the predictions of our model. In the case of the US, our model captures well the current infected curve growth and predicts a halting of infection spread by 20 April 2020. We further demonstrate that relaxing or reversing quarantine measures right now will lead to an exponential explosion in the infected case count, thus nullifying the role played by all measures implemented in the US since mid March 2020.

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 Disparities in Case Frequency and Mortality of Coronavirus Disease 2019 (COVID-19) Among Various States in the United States

2020 ◽  
Author(s):  
Rohit S. Loomba ◽  
Gaurav Aggarwal ◽  
Saurabh Aggarwal ◽  
Saul Flores ◽  
Enrique G. Villarreal ◽  
...  
Keyword(s):  
Population Density ◽  
State Level ◽  
The United States ◽  
Testing Frequency ◽  
Level Data ◽  
The Us ◽  
Mortality Case ◽  
Percent Mortality ◽  
Beta Coefficient ◽  
Reported Data

Objective: To utilize publicly reported, state-level data to identify factors associated with the frequency of cases, tests, and mortality in the US. Materials & Methods: Retrospective study using publicly reported data collected included the number of COVID-19 cases, tests, and mortality from March 14th through April 30th, 2020. Publicly available state-level data was collected which included: demographics comorbidities, state characteristics and environmental factors. Univariate and multivariate regression analyses were performed to identify the significantly associated factors with percent mortality, case and testing frequency. All analyses were state-level analyses and not patient-level analyses. Results: A total of 1,090,500 COVID-19 cases were reported during the study period. The calculated case and testing frequency were 3,332 and 19,193 per 1,000,000 patients. There were 63,642 deaths during this period which resulted in a mortality of 5.8%. Factors including to but not limited to population density (beta coefficient 7.5, p< 0.01), transportation volume (beta coefficient 0.1, p< 0.01), tourism index (beta coefficient -0.1, p=0.02) and older age (beta coefficient 0.2, p=0.01) are associated with case frequency and percent mortality. Conclusions: There were wide variations in testing and case frequencies of COVID-19 among different states in the US. States with higher population density had a higher case and testing rate. States with larger population of elderly and higher tourism had a higher mortality.

scholarly journals County-level exposures to greenness and associations with COVID-19 incidence and mortality in the United States

2020 ◽  
Author(s):  
Jochem O Klompmaker ◽  
Jaime E Hart ◽  
Isabel Holland ◽  
M Benjamin Sabath ◽  
Xiao Wu ◽  
...  
Keyword(s):  
United States ◽  
Population Density ◽  
Vegetation Index ◽  
Negative Binomial ◽  
The United States ◽  
Systems Science ◽  
County Level ◽  
Incidence And Mortality ◽  
The Us ◽  
At Home

AbstractBackgroundCOVID-19 is an infectious disease that has killed more than 246,000 people in the US. During a time of social distancing measures and increasing social isolation, green spaces may be a crucial factor to maintain a physically and socially active lifestyle while not increasing risk of infection.ObjectivesWe evaluated whether greenness is related to COVID-19 incidence and mortality in the United States.MethodsWe downloaded data on COVID-19 cases and deaths for each US county up through June 7, 2020, from Johns Hopkins University, Center for Systems Science and Engineering Coronavirus Resource Center. We used April-May 2020 Normalized Difference Vegetation Index (NDVI) data, to represent the greenness exposure during the initial COVID-19 outbreak in the US. We fitted negative binomial mixed models to evaluate associations of NDVI with COVID-19 incidence and mortality, adjusting for potential confounders such as county-level demographics, epidemic stage, and other environmental factors. We evaluated whether the associations were modified by population density, proportion of Black residents, median home value, and issuance of stay-at-home order.ResultsAn increase of 0.1 in NDVI was associated with a 6% (95% Confidence Interval: 3%, 10%) decrease in COVID-19 incidence rate after adjustment for potential confounders. Associations with COVID-19 incidence were stronger in counties with high population density and in counties with stay-at-home orders. Greenness was not associated with COVID-19 mortality in all counties; however, it was protective in counties with higher population density.DiscussionExposures to NDVI had beneficial impacts on county-level incidence of COVID-19 in the US and may have reduced county-level COVID-19 mortality rates, especially in densely populated counties.

Understanding the asymmetric spread and case fatality rate (CFR) for COVID-19 among countries (Preprint)

2020 ◽  
Author(s):  
Eldhose Iype ◽  
Sadhya Gulati
Keyword(s):  
New York ◽  
Population Density ◽  
Case Fatality Rate ◽  
Case Fatality ◽  
The United States ◽  
Control Measures ◽  
High Rate ◽  
Cumulative Number ◽  
Fatality Rate ◽  
Rate Of Increase

UNSTRUCTURED The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections are rising rapidly every day in the world, causing the disease COVID-19 with around 2 million people infected and more than 100,000 people died so far, in more than 200 countries. One of the baffling aspects of this pandemic is the asymmetric increase in cases and case fatality rate (CFR) among countries. We analyze the time series of the infection and fatality numbers and found two interesting aspects. Firstly, the rate of spread in a region is directly connected to the population density of the region where the virus is spreading. For example, the high rate of increase in cases in the United States of America (USA) is related to the high population density of New York City. This is shown by scaling the cumulative number of cases with a measure of the population density of the affected region in countries such as Italy, Spain, Germany, and the USA and we see that the curves are coinciding. Secondly, we analyzed the CFR number as a function of the number of days, since the first death, and we found that there are two clear categories among countries: one category with high CFR numbers (around 10%) and the other category with low CFR numbers (2% to 4%). When we analyzed the results, we see that countries with lower CFR numbers more or less tend to have implemented active control measures such as aggressive testing, tracking down possible infections, effective quarantine measures, etc. Moreover, we did not see any convincing correlation between mortality rates and the median age of the population.

scholarly journals Understanding the asymmetric spread and case fatality rate (CFR) for COVID-19 among countries

2020 ◽  
Author(s):  
Eldhose Iype ◽  
Sadhya Gulati
Keyword(s):  
New York ◽  
Population Density ◽  
Case Fatality Rate ◽  
Case Fatality ◽  
The United States ◽  
Control Measures ◽  
High Rate ◽  
Cumulative Number ◽  
Fatality Rate ◽  
Rate Of Increase

AbstractThe severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections are rising rapidly every day in the world, causing the disease COVID-19 with around 2 million people infected and more than 100,000 people died so far, in more than 200 countries. One of the baffling aspects of this pandemic is the asymmetric increase in cases and case fatality rate (CFR) among countries. We analyze the time series of the infection and fatality numbers and found two interesting aspects. Firstly, the rate of spread in a region is directly connected to the population density of the region where the virus is spreading. For example, the high rate of increase in cases in the United States of America (USA) is related to the high population density of New York City. This is shown by scaling the cumulative number of cases with a measure of the population density of the affected region in countries such as Italy, Spain, Germany, and the USA and we see that the curves are coinciding. Secondly, we analyzed the CFR number as a function of the number of days, since the first death, and we found that there are two clear categories among countries: one category with high CFR numbers (around 10%) and the other category with low CFR numbers (2% to 4%). When we analyzed the results, we see that countries with lower CFR numbers more or less tend to have implemented active control measures such as aggressive testing, tracking down possible infections, effective quarantine measures, etc. Moreover, we did not see any convincing correlation between mortality rates and the median age of the population.

scholarly journals Unmasking the Actual COVID-19 Case Count

2020 ◽  
Vol 71 (11) ◽  
pp. 2949-2951
Author(s):  
Samuel C Kou ◽  
Shihao Yang ◽  
Chia-Jung Chang ◽  
Teck-Hua Ho ◽  
Lisa Graver
Keyword(s):  
United States ◽  
Disease Control ◽  
The United States ◽  
Surveillance Data ◽  
Prescription Data ◽  
Cumulative Number ◽  
Influenza Like Illness ◽  
Novel Approach ◽  
Case Count ◽  
Centers For Disease Control

Abstract This report presents a novel approach to estimate the total number of COVID-19 cases in the United States, including undocumented infections, by combining the Centers for Disease Control and Prevention’s influenza-like illness surveillance data with aggregated prescription data. We estimated that the cumulative number of COVID-19 cases in the United States by 4 April 2020 was &gt; 2.5 million.

scholarly journals Prescribing of antibiotics and other drugs to children from birth to age 5 in the United States: an observational study

2021 ◽  
Author(s):  
Stephen M Kissler ◽  
Bill Wang ◽  
Ateev Mehrotra ◽  
Michael Barnett ◽  
Yonatan M Grad
Keyword(s):  
United States ◽  
Observational Study ◽  
Prescription Drugs ◽  
Respiratory Infections ◽  
Antibiotic Prescription ◽  
Insurance Coverage ◽  
Antibiotic Use ◽  
The United States ◽  
Cumulative Number ◽  
The Us

Objectives. To inform efforts to reduce pediatric antibiotic use, we measured cumulative pediatric prescriptions for antibiotics and non-antibiotics and how this varies across geography and patient subgroups. Design. Observational study. Setting. United States, 2008-2018. Participants. 207,814 children under age 5 born in the United States between 2008 and 2013 with private medical insurance coverage. Interventions. None. Main outcome measures. Study outcomes included (1) the cumulative number of prescriptions received per child by age 5, (2) the proportion of these prescriptions that were attributable to respiratory infections, (3) the proportion of children who received at least one prescription by age 5, and (4) the fraction of total prescriptions received by the top 20% of prescription recipients. Results. Children received a mean of 8.21 (95% confidence interval [CI] (8.19, 8.22)) prescriptions for antibiotics and 9.81 (95% CI 9.80, 9.82) prescriptions for non-antibiotics by age five. Most antibiotic prescriptions (64%, 95% CI 63, 65) and many non-antibiotic prescriptions (25%, 95% CI 24, 26) were associated with outpatient visits for respiratory infections. By age 5, 93.8% (95% CI 93.4, 94.2) of children had received at least one antibiotic prescription while 88.3% (95% CI 87.9, 88.7) had received at least one prescription for a non-antibiotic. The top 20% of antibiotic prescription recipients accounted for 50.6% of all antibiotic prescriptions, and the top 20% of non-antibiotic prescription recipients accounted for 64.2% of all non-antibiotic prescriptions. Relative to other regions, the South featured higher prescribing rates and earlier time to first prescription. Conclusions. Children in the US receive a substantial number of antibiotics and other prescription drugs early in their lives, largely related to respiratory infections.

scholarly journals The Association between Early Country-level Testing Capacity and Later COVID-19 Mortality Outcomes

2021 ◽  
Author(s):  
Sneha Kannoth ◽  
Sasikiran Kandula ◽  
Jeffrey Shaman
Keyword(s):  
Ordinary Least Squares ◽  
Global Scale ◽  
Cumulative Number ◽  
Least Squares Regression ◽  
Country Level ◽  
Early Action ◽  
Hospital Systems ◽  
Early Testing ◽  
Per Capita ◽  
And Control

ABSTRACTBackgroundThe COVID-19 pandemic has overrun hospital systems while exacerbating economic hardship and food insecurity on a global scale. In an effort to understand how early action to find and control the virus is associated with cumulative outcomes, we explored how country-level testing capacity affects later COVID-19 mortality.MethodsWe used the Our World in Data database to explore testing and mortality records in 27 countries from December 31, 2019 to September 30, 2020; we applied ordinary-least squares regression with clustering on country to determine the association between early COVID-19 testing capacity (cumulative tests per case) and later COVID-19 mortality (time to specified mortality thresholds), adjusting for country-level confounders, including median age, GDP, hospital bed capacity, population density, and non-pharmaceutical interventions.ResultsHigher early testing implementation, as indicated by more cumulative tests per case when mortality was still low, was associated with longer accrual time for higher per capita deaths. For instance, a higher cumulative number of tests administered per case at the time of 6 deaths per million persons was positively predictive of a longer time to reach 15 deaths per million, after adjustment for all confounders (β=0.659; P=0.001).ConclusionsCountries that developed stronger COVID-19 testing capacity at early timepoints, as measured by tests administered per case identified, experienced a slower increase of deaths per capita. Thus, this study operationalizes the value of testing and provides empirical evidence that stronger testing capacity at early timepoints is associated with reduced mortality and better pandemic control.

Relation of Statistically Significant, Abnormal, and Typical WAIS-R VIQ-PIQ Discrepancies to Full Scale IQs

2000 ◽  
Vol 16 (2) ◽  
pp. 107-114 ◽  
Author(s):  
Louis M. Hsu ◽  
Judy Hayman ◽  
Judith Koch ◽  
Debbie Mandell
Keyword(s):  
Graphical Method ◽  
The United States ◽  
Full Scale ◽  
True Score ◽  
Absolute Value ◽  
Normative Population ◽  
The Us ◽  
The Mean ◽  
And Performance ◽  
Quadratic Models

Summary: In the United States' normative population for the WAIS-R, differences (Ds) between persons' verbal and performance IQs (VIQs and PIQs) tend to increase with an increase in full scale IQs (FSIQs). This suggests that norm-referenced interpretations of Ds should take FSIQs into account. Two new graphs are presented to facilitate this type of interpretation. One of these graphs estimates the mean of absolute values of D (called typical D) at each FSIQ level of the US normative population. The other graph estimates the absolute value of D that is exceeded only 5% of the time (called abnormal D) at each FSIQ level of this population. A graph for the identification of conventional “statistically significant Ds” (also called “reliable Ds”) is also presented. A reliable D is defined in the context of classical true score theory as an absolute D that is unlikely (p < .05) to be exceeded by a person whose true VIQ and PIQ are equal. As conventionally defined reliable Ds do not depend on the FSIQ. The graphs of typical and abnormal Ds are based on quadratic models of the relation of sizes of Ds to FSIQs. These models are generalizations of models described in Hsu (1996) . The new graphical method of identifying Abnormal Ds is compared to the conventional Payne-Jones method of identifying these Ds. Implications of the three juxtaposed graphs for the interpretation of VIQ-PIQ differences are discussed.

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