scholarly journals A Data‐Driven Framework to Characterize State‐Level Water Use in the United States

2020 ◽  
Vol 56 (9) ◽  
Author(s):  
E. Wongso ◽  
R. Nateghi ◽  
B. Zaitchik ◽  
S. Quiring ◽  
R. Kumar
Keyword(s):  
United States ◽  
Water Use ◽  
State Level ◽  
The United States ◽  
Data Driven

scholarly journals Using Test Positivity and Reported Case Rates to Estimate State-Level COVID-19 Prevalence and Seroprevalence in the United States

2020 ◽  
Author(s):  
Weihsueh A. Chiu ◽  
Martial L. Ndeffo-Mbah
Keyword(s):  
Public Health ◽  
United States ◽  
Geometric Mean ◽  
State Level ◽  
Credible Interval ◽  
Assistant Professor ◽  
The United States ◽  
Data Driven ◽  
Infection Prevalence ◽  
Public Health Decision

AbstractAccurate estimates of infection prevalence and seroprevalence are essential for evaluating and informing public health responses needed to address the ongoing spread of COVID-19 in the United States. A data-driven Bayesian single parameter semi-empirical model was developed and used to evaluate state-level prevalence and seroprevalence of COVID-19 using daily reported cases and test positivity ratios. COVID-19 prevalence is well-approximated by the geometric mean of the positivity rate and the reported case rate. As of December 8, 2020, we estimate nation-wide a prevalence of 1.4% [Credible Interval (CrI): 0.8%-1.9%] and a seroprevalence of 11.1% [CrI: 10.1%-12.2%], with state-level prevalence ranging from 0.3% [CrI: 0.2%-0.4%] in Maine to 3.0% [CrI: 1.1%-5.7%] in Pennsylvania, and seroprevalence from 1.4% [CrI: 1.0%-2.0%] in Maine to 22% [CrI: 18%-27%] in New York. The use of this simple and easy-to-communicate model will improve the ability to make public health decisions that effectively respond to the ongoing pandemic.Biographical Sketch of AuthorsDr. Weihsueh A. Chiu, is a professor of environmental health sciences at Texas A&M University. He is an expert in data-driven Bayesian modeling of public health related dynamical systems. Dr. Martial L. Ndeffo-Mbah, is an Assistant Professor of Epidemiology at Texas A&M University. He is an expert in mathematical and computational modeling of infectious diseases.Summary LineRelying on reported cases and test positivity rates individually can result in incorrect inferences as to the spread of COVID-19, and public health decision-making can be improved by instead using their geometric mean as a measure of COVID-19 prevalence and transmission.

scholarly journals Using test positivity and reported case rates to estimate state-level COVID-19 prevalence and seroprevalence in the United States

2021 ◽  
Vol 17 (9) ◽  
pp. e1009374
Author(s):  
Weihsueh A. Chiu ◽  
Martial L. Ndeffo-Mbah
Keyword(s):  
Public Health ◽  
United States ◽  
State Level ◽  
The United States ◽  
Empirical Modeling ◽  
Data Driven ◽  
Infection Prevalence ◽  
Modeling Framework ◽  
Seroprevalence Data ◽  
Semi Empirical

Accurate estimates of infection prevalence and seroprevalence are essential for evaluating and informing public health responses and vaccination coverage needed to address the ongoing spread of COVID-19 in each United States (U.S.) state. However, reliable, timely data based on representative population sampling are unavailable, and reported case and test positivity rates are highly biased. A simple data-driven Bayesian semi-empirical modeling framework was developed and used to evaluate state-level prevalence and seroprevalence of COVID-19 using daily reported cases and test positivity ratios. The model was calibrated to and validated using published state-wide seroprevalence data, and further compared against two independent data-driven mathematical models. The prevalence of undiagnosed COVID-19 infections is found to be well-approximated by a geometrically weighted average of the positivity rate and the reported case rate. Our model accurately fits state-level seroprevalence data from across the U.S. Prevalence estimates of our semi-empirical model compare favorably to those from two data-driven epidemiological models. As of December 31, 2020, we estimate nation-wide a prevalence of 1.4% [Credible Interval (CrI): 1.0%-1.9%] and a seroprevalence of 13.2% [CrI: 12.3%-14.2%], with state-level prevalence ranging from 0.2% [CrI: 0.1%-0.3%] in Hawaii to 2.8% [CrI: 1.8%-4.1%] in Tennessee, and seroprevalence from 1.5% [CrI: 1.2%-2.0%] in Vermont to 23% [CrI: 20%-28%] in New York. Cumulatively, reported cases correspond to only one third of actual infections. The use of this simple and easy-to-communicate approach to estimating COVID-19 prevalence and seroprevalence will improve the ability to make public health decisions that effectively respond to the ongoing COVID-19 pandemic.

Can increases in Twitter posts predict increases in cumulative incidence of COVID-19 in the United States? Evidence that social media can inform epidemic surveillance. (Preprint)

2020 ◽  
Author(s):  
Ruoyan Sun ◽  
Henna Budhwani
Keyword(s):  
Public Health ◽  
United States ◽  
Cumulative Incidence ◽  
Hot Spots ◽  
Adult Population ◽  
State Level ◽  
The United States ◽  
Education Attainment ◽  
Public Health Systems ◽  
Per Capita

BACKGROUND Though public health systems are responding rapidly to the COVID-19 pandemic, outcomes from publicly available, crowd-sourced big data may assist in helping to identify hot spots, prioritize equipment allocation and staffing, while also informing health policy related to “shelter in place” and social distancing recommendations. OBJECTIVE To assess if the rising state-level prevalence of COVID-19 related posts on Twitter (tweets) is predictive of state-level cumulative COVID-19 incidence after controlling for socio-economic characteristics. METHODS We identified extracted COVID-19 related tweets from January 21st to March 7th (2020) across all 50 states (N = 7,427,057). Tweets were combined with state-level characteristics and confirmed COVID-19 cases to determine the association between public commentary and cumulative incidence. RESULTS The cumulative incidence of COVID-19 cases varied significantly across states. Ratio of tweet increase (p=0.03), number of physicians per 1,000 population (p=0.01), education attainment (p=0.006), income per capita (p = 0.002), and percentage of adult population (p=0.003) were positively associated with cumulative incidence. Ratio of tweet increase was significantly associated with the logarithmic of cumulative incidence (p=0.06) with a coefficient of 0.26. CONCLUSIONS An increase in the prevalence of state-level tweets was predictive of an increase in COVID-19 diagnoses, providing evidence that Twitter can be a valuable surveillance tool for public health.

Dissent in the Atlantic World, 1787–1830

2018 ◽  
Author(s):  
Katherine Carté Engel
Keyword(s):  
United States ◽  
African Americans ◽  
First Amendment ◽  
Church And State ◽  
State Level ◽  
The United States ◽  
American Population ◽  
Simple Fact ◽  
General Role

The very term ‘Dissenter’ became problematic in the United States, following the passing of the First Amendment. The formal separation of Church and state embodied in the First Amendment was followed by the ending of state-level tax support for churches. None of the states established after 1792 had formal religious establishments. Baptists, Congregationalists, Presbyterians, and Methodists accounted for the majority of the American population both at the beginning and end of this period, but this simple fact masks an important compositional shift. While the denominations of Old Dissent declined relatively, Methodism grew quickly, representing a third of the population by 1850. Dissenters thus faced several different challenges. Primary among these were how to understand the idea of ‘denomination’ and also the more general role of institutional religion in a post-establishment society. Concerns about missions, and the positions of women and African Americans are best understood within this context.

The Oxford Handbook of Preservice Music Teacher Education in the United States

2019 ◽  
Keyword(s):  
United States ◽  
Teacher Education ◽  
Music Education ◽  
State Level ◽  
The United States ◽  
Music Teacher ◽  
Music Teacher Education ◽  
Education Faculty ◽  
Song Writing ◽  
Faculty And Administrators

The Oxford Handbook of Preservice Music Teacher Education in the United States aims to work from within the profession of music teacher education to push the boundaries of P-12 music education. In this book, we will provide all of those working in music teacher education—music education faculty and administrators, music researchers, graduate students, department of education faculty and administrators, and state-level certification agencies—with research and promising practices for all areas of traditional preservice music teacher preparation. We define the areas of music teacher education as encompassing the more traditional structures, such as band, jazz band, marching band, orchestra, choir, musical theater, and elementary and secondary general music, as well as less common or newer areas: alternative string ensembles, guitar and song-writing, vernacular and popular music, early childhood music, and adult learners

scholarly journals Patterns and correlates of mis-implementation in state chronic disease public health practice in the United States

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Margaret M. Padek ◽  
Stephanie Mazzucca ◽  
Peg Allen ◽  
Emily Rodriguez Weno ◽  
Edward Tsai ◽  
...  
Keyword(s):  
Public Health ◽  
United States ◽  
Decision Making ◽  
Chronic Disease ◽  
Multinomial Logistic Regression ◽  
Public Health Practice ◽  
State Level ◽  
The United States ◽  
Organizational Level ◽  
Effective Programs

Abstract Background Much of the disease burden in the United States is preventable through application of existing knowledge. State-level public health practitioners are in ideal positions to affect programs and policies related to chronic disease, but the extent to which mis-implementation occurring with these programs is largely unknown. Mis-implementation refers to ending effective programs and policies prematurely or continuing ineffective ones. Methods A 2018 comprehensive survey assessing the extent of mis-implementation and multi-level influences on mis-implementation was reported by state health departments (SHDs). Questions were developed from previous literature. Surveys were emailed to randomly selected SHD employees across the Unites States. Spearman’s correlation and multinomial logistic regression were used to assess factors in mis-implementation. Results Half (50.7%) of respondents were chronic disease program managers or unit directors. Forty nine percent reported that programs their SHD oversees sometimes, often or always continued ineffective programs. Over 50% also reported that their SHD sometimes or often ended effective programs. The data suggest the strongest correlates and predictors of mis-implementation were at the organizational level. For example, the number of organizational layers impeded decision-making was significant for both continuing ineffective programs (OR=4.70; 95% CI=2.20, 10.04) and ending effective programs (OR=3.23; 95% CI=1.61, 7.40). Conclusion The data suggest that changing certain agency practices may help in minimizing the occurrence of mis-implementation. Further research should focus on adding context to these issues and helping agencies engage in appropriate decision-making. Greater attention to mis-implementation should lead to greater use of effective interventions and more efficient expenditure of resources, ultimately to improve health outcomes.

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