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Publication Date

2023-5

First Advisor

Ben Baumer

Second Advisor

Nicholas Reich

Document Type

Honors Project

Degree Name

Bachelor of Arts

Department

Statistical and Data Sciences

Keywords

probabilistic bias analysis, sensitivity analysis, Bayesian melding, COVID-19, SARS-CoV-@, infections, Sampling Importance Resampling

Abstract

As we have navigated the COVID-19 pandemic, case counts have been a central source of information for understanding transmission dynamics and the effect of public health interventions. However, because the number of cases we observe is limited by the testing effort in a given location, the case counts presented on local or national dashboards are only a fraction of the true infections. Variation in testing rate by time and location impacts the number of cases that go unobserved, which can cloud our understanding of the true COVID-19 incidence at a given time point and can create biases in downstream analyses. Additionally, the number of cases we observe is impacted by the sensitivity and specificity of the diagnostic test. To quantify the number of true infections given incomplete testing and diagnostic test inaccuracy, we implement probabilistic bias analysis at a biweekly time scale from the beginning of March of 2021 to the beginning of March of 2022. In doing so, we estimate a range of possible true infections for every given time interval and location considered. This approach can be applied at the state level across the United States, as well as in some counties where the needed data are available.

Rights

©2023 Quinn White. Access limited to the Smith College community and other researchers while on campus. Smith College community members also may access from off-campus using a Smith College log-in. Other off-campus researchers may request a copy through Interlibrary Loan for personal use.

Language

English

Comments

[8], 144 pages: color illustrations, charts. Includes bibliographical references (pages 137-144).

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