About: Estimating the COVID-19 Infection Rate: Anatomy of an Inference Problem

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An Entity of Type : schema:ScholarlyArticle, within Data Space : wasabi.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Estimating the COVID-19 Infection Rate: Anatomy of an Inference Problem
Creator	Manski, Charles Molinari, Francesca
Source	ArXiv
abstract	As a consequence of missing data on tests for infection and imperfect accuracy of tests, reported rates of population infection by the SARS CoV-2 virus are lower than actual rates of infection. Hence, reported rates of severe illness conditional on infection are higher than actual rates. Understanding the time path of the COVID-19 pandemic has been hampered by the absence of bounds on infection rates that are credible and informative. This paper explains the logical problem of bounding these rates and reports illustrative findings, using data from Illinois, New York, and Italy. We combine the data with assumptions on the infection rate in the untested population and on the accuracy of the tests that appear credible in the current context. We find that the infection rate might be substantially higher than reported. We also find that the infection fatality rate in Italy is substantially lower than reported.
has issue date	2020-04-13(xsd:dateTime)
has license	arxiv
sha1sum (hex)	d9bf11dd9fdf075a26144b7c816440092200b4f0
resource representing a document's title	Estimating the COVID-19 Infection Rate: Anatomy of an Inference Problem
resource representing a document's body	covid:d9bf11dd9fdf075a26144b7c816440092200b4f0#body_text
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