What Can We Learn about SARS-CoV-2 Prevalence from Testing and Hospital Data?

• 成果类型: Article
• 署名作者: Sacks, Daniel W.; Menachemi, Nir; Embi, Peter; Wing, Coady
• 署名单位: Indiana University System; Indiana University Bloomington; Regenstrief Institute Inc; Vanderbilt University; Vanderbilt University
• 刊物名称: REVIEW OF ECONOMICS AND STATISTICS
• ISSN/ISSBN: 0034-6535
• DOI: 10.1162/rest_a_01179
• 发表日期: 2024-05
• 页码: 848-858
• 关键词: random sample; population

摘要

Measuring the prevalence of active SARS-CoV-2 infections in the general population is difficult because tests are conducted on a small and nonrandom segment of the population. However, hospitalized patients are tested at very high rates, even those admitted for non-COVID reasons. We show how to use information on testing of non-COVID hospitalized patients to obtain tight bounds on population prevalence, under conditions weaker than those usually used. We apply our approach to the population of test and hospitalization data for Indiana, and we validate our approach. Our bounds could be constructed at relatively low cost, and for other heavily tested populations.