scholarly journals Estimates of SARS-COV-2 Behavior in the COVID-19 Crisis: Addressing Sample-Selection Bias for Public Health Applications

2020 ◽  
Author(s):  
John Straka
Keyword(s):  
Selection Bias ◽  
Sample Selection ◽  
Herd Immunity ◽  
Model Specification ◽  
Sample Selection Bias ◽  
Case Scenario ◽  
Worst Case ◽  
Reproduction Ratio ◽  
Social Distancing ◽  
The U.S

This study surveys and assesses the implications from recent empirical studies and reports to highlight the characteristics of SARS-Cov-2 and the COVID-19 crisis, and then proposes a recursive bivariate probit (RBP) model specification and possible applications. The RBP model addresses sample selection bias to estimate key determinants of virus infection given nonrandom testing. Applicable to anonymized case-level or widely available local-area data in the U.S., multiple data sources are shown. With suitable data the model can control for observed (e.g. population density) and unobserved factors to estimate the marginal effects of varying state-prescribed measures and behavioral social distancing. Case-level scoring models may, in addition, eventually assist in clinical diagnostic assessments. Although not proposed to substitute for more random population testing and other methods, results could also be used in advance of more testing. Uncertain assumptions in epidemiological models reflect unclear effects from gradations of social distancing now occurring. Despite many calls for broader testing and targeted quarantining in the U.S., many practical obstacles remain, leaving unknowns, especially across local areas. Differing local transmission rates respond to stronger or weaker social distancing and quarantining. High risks from latent non-quarantining spread warn of potential overwhelming local outbreaks. The insidious nature of SARS-Cov-2 invites complacency, especially in non-hotspot areas. Complacent behaviors can fail to adequately address the public-goods problem, leading to various forms of continued local and macro COVID-19 waves and crises. To assess a worst case scenario, no model projection is needed, only the herd immunity threshold equation, estimates of the reproduction ratio, and the estimated mortality rate. With no ultimately successful countermeasures in treatment, vaccine, and non-pharmaceutical interventions (NPIs), the analysis here suggests an eventual number of deaths much like the 1918 pandemic in U.S. deaths per capita (1.8-2.7 million U.S. deaths) and in the total number of deaths worldwide (around 50 million). This toll also reflects a hypothetical global “surrender” strategy of business-as-usual and no social distancing, which in practice no nation has followed. Some successes across the three broad social countermeasure efforts – which appears most likely, in a mix of outcomes – can lessen the high social costs.

scholarly journals Estimates of SARS-COV-2 Behavior in the COVID-19 Crisis: Addressing Sample-Selection Bias for Public Health Applications

2020 ◽  
Author(s):  
John Straka
Keyword(s):  
Selection Bias ◽  
Sample Selection ◽  
Herd Immunity ◽  
Model Specification ◽  
Sample Selection Bias ◽  
Case Scenario ◽  
Worst Case ◽  
Reproduction Ratio ◽  
Social Distancing ◽  
The U.S

This study surveys and assesses the implications from recent empirical studies and reports to highlight the characteristics of SARS-Cov-2 and the COVID-19 crisis, and then proposes a recursive bivariate probit (RBP) model specification and possible applications. The RBP model addresses sample selection bias to estimate key determinants of virus infection given nonrandom testing. Applicable to anonymized case-level or widely available local-area data in the U.S., multiple data sources are shown. With suitable data the model can control for observed (e.g. population density) and unobserved factors to estimate the marginal effects of varying state-prescribed measures and behavioral social distancing. Case-level scoring models may, in addition, eventually assist in clinical diagnostic assessments. Although not proposed to substitute for more random population testing and other methods, results could also be used in advance of more testing. Uncertain assumptions in epidemiological models reflect unclear effects from gradations of social distancing now occurring. Despite many calls for broader testing and targeted quarantining in the U.S., many practical obstacles remain, leaving unknowns, especially across local areas. Differing local transmission rates respond to stronger or weaker social distancing and quarantining. High risks from latent non-quarantining spread warn of potential overwhelming local outbreaks. The insidious nature of SARS-Cov-2 invites complacency, especially in non-hotspot areas. Complacent behaviors can fail to adequately address the public-goods problem, leading to various forms of continued local and macro COVID-19 waves and crises. To assess a worst-case scenario, no model projection is needed, only the herd immunity threshold equation, estimates of the reproduction ratio, and the estimated mortality rate. With no ultimately successful countermeasures in treatment, vaccine, and non-pharmaceutical interventions (NPIs), the analysis here suggests an eventual number of deaths much like the 1918 pandemic in U.S. deaths per capita (1.8-2.7 million U.S. deaths) and in the total number of deaths worldwide (around 50 million). This toll also reflects a hypothetical global “surrender” strategy of business-as-usual and no social distancing, which in practice no nation has followed. Some successes across the three broad social countermeasure efforts – which appears most likely, in a mix of outcomes – can lessen the high social costs.

scholarly journals COVID-19 Transmission Estimates and Forecasts: A Model to Test Sample Selection Bias in a ‘Low Risk’ Crisis

2020 ◽  
Author(s):  
John Straka
Keyword(s):  
Selection Bias ◽  
Sample Selection ◽  
Herd Immunity ◽  
Probit Model ◽  
Test Sample ◽  
Sample Selection Bias ◽  
Economic Damage ◽  
Critical Question ◽  
Worst Case ◽  
The U.S

This paper surveys estimates of the transmission features of the novel coronavirus, and then proposes a model to address sample-selection bias in estimated determinants of infection. Containment assumptions of the infection forecasting models depend on assumed effects of policies and self-regulating behavior. In the commons dilemma of the pandemic, the perceived ‘low risks’ of unregulated marginal choices do not reflect the full social cost, implying non-pharmaceutical interventions (NPI) to reduce mortality can enhance social welfare. As more economic activity renews with liftings of restrictive NPI (RNPI), a critical question concerns the ability of milder NPI (MNPI) and voluntary precautions to mitigate the risk of greater infections and deaths while also limiting the pandemic’s economic damage and its social costs. Ineffective NPI could lead to continued COVID-19 waves and new types of crises, worsened expectations and delayed economic recoveries. From the central range of surveyed estimates of transmission and alternative herd-immunity-threshold estimates, a ‘worst-case’ virus guidepost suggests eventual deaths of around 25 to 41 million worldwide and 1.1 to 1.7 million in the U.S. needed to reach herd immunity with no vaccine or treatment. The most optimistic study surveyed (theoretical model from a non-reviewed preprint study) combined with the low end of the range of the estimated mortality rate suggests 6 to 9 million deaths worldwide and 250 to 370 thousand in the U.S. to reach herd immunity. Successes in the mix of NPI, treatments, and vaccine can limit the eventual global death toll of the virus. Improved estimation models for forecasting and decision making may assist in better targeting the local timings and mix of NPI. Diagnostic tests for the virus have been largely limited to symptomatic cases, causing possible sample selection bias. A recursive bivariate probit model of infection and testing is proposed along with several possible applications from cross-section or panel-data estimation. Multiple potential explanatory variables, data sources, and estimation needs are specified and discussed.

scholarly journals Flattening the curve and the effect of atypical events on mitigation measures in Mexico: a modeling perspective

2020 ◽  
Author(s):  
Mario Santana-Cibrian ◽  
Manuel Adrian Acuna-Zegarra ◽  
Jorge X. Velasco-Hernandez
Keyword(s):  
Mitigation Measures ◽  
Case Scenario ◽  
Worst Case ◽  
Social Distancing ◽  
Epidemic Curve ◽  
Effective Contact ◽  
High Incidence ◽  
The Government ◽  
Mother’S Day ◽  
Short Time

On 23 and 30 March 2020 the Mexican Federal government implemented social distancing measures to mitigate the COVID-19 epidemic. We use a mathematical model to explore atypical transmission events within the confinement period, triggered by the timing and strength of short time perturbations of social distancing. We show that social distancing measures were successful in achieving a significant reduction of the effective contact rate in the early weeks of the intervention. However, "flattening the curve" had an undesirable effect, since the epidemic peak was delayed too far, almost to the government preset day for lifting restrictions (01 June 2020). If the peak indeed occurs in late May or early June, then the events of children's day and mother's day may either generate a later peak (worst case scenario), a long plateau with relatively constant but high incidence (middle case scenario) or the same peak date as in the original baseline epidemic curve, but with a post-peak interval of slower decay.

scholarly journals Heterogeneous Causal Effects and Sample Selection Bias

2015 ◽  
Vol 2 ◽  
pp. 351-369 ◽  
Author(s):  
Richard Breen ◽  
Seungsoo Choi ◽  
Anders Holm
Keyword(s):  
Selection Bias ◽  
Sample Selection ◽  
Causal Effects ◽  
Sample Selection Bias

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.

Face Hallucination Based On Sample Selection Bias Correction

2012 ◽  
Vol 4 (5) ◽  
pp. 91-98
Author(s):  
Tao Lu ◽  
Ruimin Hu ◽  
Zhen Han ◽  
Junjun Jiang ◽  
Jun Chang
Keyword(s):  
Selection Bias ◽  
Bias Correction ◽  
Sample Selection ◽  
Sample Selection Bias ◽  
Face Hallucination
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