Open AccessBayesian estimation of SARS-CoV-2 prevalence in Indiana by random testing
Author(s) -
Constantin T. Yiannoutsos,
Paul K. Halverson,
Nir Menachemi
Publication year - 2021
Publication title -
proceedings of the national academy of sciences of the united states of america
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 5.011
H-Index - 771
eISSN - 1091-6490
pISSN - 0027-8424
DOI - 10.1073/pnas.2013906118
Subject(s) - medicine , population , covid-19 , demography , pandemic , census , statistics , disease , environmental health , infectious disease (medical specialty) , mathematics , sociology
Significance Infection with the novel coronovirus severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has resulted in a worldwide pandemic of COVID-19 disease. Efforts to design local, regional, and national responses to the virus are constrained by a lack of information on the extent of the epidemic as well as inaccuracies in newly developed diagnostic tests. In this study we analyze data from testing randomly selected Indiana state residents for infection or previous exposure to SARS-CoV-2 and derive estimates of the statewide COVID-19 prevalence in an attempt to address potential biases arising from nonresponse and diagnostic testing errors.