About: Estimating the Fraction of Unreported Infections in Epidemics with a Known Epicenter: an Application to COVID-19

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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
title	Estimating the Fraction of Unreported Infections in Epidemics with a Known Epicenter: an Application to COVID-19
Creator	Athey, Susan Alvarez, Fernando Bayer, Patrick Choi, Rana Einav, Liran Fox, Jeremy Golosov, Mikhail Goolsbee, Austan Griffin, Kenneth Haile, Philip Hortaçsu, Ali Kastl, Jakub Liu, Jiarui Mogstad, Magne Mulligan, Casey Neal, Derek Scheinkman, Jose Schwieg, Timothy Shimer, Robert Syverson, Chad Uhlig, Harald Vassilakis, Theodore Voena, Alessandra
source	MedRxiv
abstract	We develop a simple analytical method to estimate the fraction of unreported infections in epidemics with a known epicenter and estimate the number of unreported COVID-19 infections in the US during the first half of March 2020. Our method utilizes the covariation in initial reported infections across US regions and the number of travelers to these regions from the epicenter, along with the results of a randomized testing study in Iceland. We estimate that 4%-14% (1.5%-10%) of actual infections had been reported in US up to March 16, accounting for an assumed reporting lag of 8 (5) days.
has issue date	2020-04-17(xsd:dateTime)
bibo:doi	10.1101/2020.04.13.20063511
has license	medrxiv
sha1sum (hex)	3edd3f356476cebe0e1298127331fc93e73a150a
schema:url	https://doi.org/10.1101/2020.04.13.20063511
resource representing a document's title	Estimating the Fraction of Unreported Infections in Epidemics with a Known Epicenter: an Application to COVID-19
resource representing a document's body	covid:3edd3f356476cebe0e1298127331fc93e73a150a#body_text
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