scholarly journals Estimating COVID-19 Prevalence in the United States: A Sample Selection Model Approach

2020 ◽  
Author(s):  
David Benatia ◽  
Raphael Godefroy ◽  
Joshua Lewis
Keyword(s):  
United States ◽  
Sample Selection ◽  
The United States ◽  
Selection Model ◽  
Sample Selection Model ◽  
Model Approach

scholarly journals Estimating COVID-19 Prevalence in the United States: A Sample Selection Model Approach

2020 ◽  
Author(s):  
David Benatia ◽  
Raphael Godefroy ◽  
Joshua Lewis
Keyword(s):  
Public Health ◽  
United States ◽  
Sample Selection ◽  
The United States ◽  
Underlying Disease ◽  
Selection Model ◽  
Sample Period ◽  
Infection Rates ◽  
Public Health Response ◽  
Sample Selection Model

SummaryBackgroundPublic health efforts to determine population infection rates from coronavirus disease 2019 (COVID-19) have been hampered by limitations in testing capabilities and the large shares of mild and asymptomatic cases. We developed a methodology that corrects observed positive test rates for non-random sampling to estimate population infection rates across U.S. states from March 31 to April 7.MethodsWe adapted a sample selection model that corrects for non-random testing to estimate population infection rates. The methodology compares how the observed positive case rate vary with changes in the size of the tested population, and applies this gradient to infer total population infection rates. Model identification requires that variation in testing rates be uncorrelated with changes in underlying disease prevalence. To this end, we relied on data on day-to-day changes in completed tests across U.S. states for the period March 31 to April 7, which were primarily influenced by immediate supply-side constraints. We used this methodology to construct predicted infection rates across each state over the sample period. We also assessed the sensitivity of the results to controls for state-specific daily trends in infection rates.ResultsThe median population infection rate over the period March 31 to April 7 was 0.9% (IQR 0.64 1.77). The three states with the highest prevalence over the sample period were New York (8.5%), New Jersey (7.6%), and Louisiana (6.7%). Estimates from mod-els that control for state-specific daily trends in infection rates were virtually identical to the baseline findings. The estimates imply a nationwide average of 12 population infections per diagnosed case. We found a negative bivariate relationship (corr. = -0.51) between total per capita state testing and the ratio of population infections per diagnosed case.InterpretationThe effectiveness of the public health response to the coronavirus pandemic will depend on timely information on infection rates across different regions. With increasingly available high frequency data on COVID-19 testing, our methodology could be used to estimate population infection rates for a range of countries and subnational districts. In the United States, we found widespread undiagnosed COVID-19 infection. Expansion of rapid diagnostic and serological testing will be critical in preventing recurrent unobserved community transmission and identifying the large numbers individuals who may have some level of viral immunity.FundingSocial Sciences and Humanities Research Council.

scholarly journals A Flexible Sample Selection Model: A Gtl-Copula Approach

2012 ◽  
Author(s):  
Takuya Hasebe ◽  
Wim Vijverberg
Keyword(s):  
Sample Selection ◽  
Selection Model ◽  
Sample Selection Model ◽  
Copula Approach

scholarly journals A Sample Selection Model with Skew-normal Distribution

2015 ◽  
Vol 43 (1) ◽  
pp. 172-190 ◽  
Author(s):  
Emmanuel O. Ogundimu ◽  
Jane L. Hutton
Keyword(s):  
Normal Distribution ◽  
Sample Selection ◽  
Selection Model ◽  
Skew Normal Distribution ◽  
Sample Selection Model ◽  
Skew Normal

scholarly journals Sample-Selection Biases and the Industrialization Puzzle

2017 ◽  
Vol 77 (1) ◽  
pp. 171-207 ◽  
Author(s):  
Howard Bodenhorn ◽  
Timothy W. Guinnane ◽  
Thomas A. Mroz
Keyword(s):  
United States ◽  
Selection Bias ◽  
Meta Analysis ◽  
Sample Selection ◽  
Well Being ◽  
The United States ◽  
Compelling Evidence ◽  
Long Term Changes ◽  
The U.S

Understanding long-term changes in human well-being is central to understanding the consequences of economic development. An extensive anthropometric literature purports to show that heights in the United States declined between the 1830s and the 1890s, which is when the U.S. economy modernized. Most anthropometric research contends that declining heights reflect the negative health consequences of industrialization and urbanization. This interpretation, however, relies on sources subject to selection bias. Our meta-analysis shows that the declining height during industrialization emerges primarily in selected samples. We also develop a parsimonious diagnostic test that reveals, but does not correct for, selection bias in height samples. When applied to four representative height samples, the diagnostic provides compelling evidence of selection.

The Job Satisfaction Determinants using Sample Selection Model

2021 ◽  
Vol 23 (1) ◽  
pp. 115-124
Author(s):  
Jangsik Cho
Keyword(s):  
Job Satisfaction ◽  
Sample Selection ◽  
Selection Model ◽  
Sample Selection Model

Determinants of Crash Fatalities in Urban Area: A Bayesian Ordered Probit with Sample Selection Model

CICTP 2020 ◽  
2020 ◽  
Author(s):  
Daiquan Xiao ◽  
Xiaofei Jin ◽  
Xuecai Xu ◽  
Shengyang Kang
Keyword(s):  
Urban Area ◽  
Sample Selection ◽  
Selection Model ◽  
Ordered Probit ◽  
Sample Selection Model

A site selection model for high technology manufacturing firms in the United States

1985 ◽  
Vol 8 (1-2) ◽  
pp. 103-116 ◽  
Author(s):  
Barbara R. Davies ◽  
J. Martin Lazniarz
Keyword(s):  
United States ◽  
Site Selection ◽  
High Technology ◽  
The United States ◽  
Manufacturing Firms ◽  
Selection Model ◽  
A Site
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