Estimating the fraction of unreported infections in epidemics with a known epicenter: An application to COVID-19
We develop an analytically tractable 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 U.S. during the first half of March 2020. Our method utilizes the covariation in initial reported infecti...
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London: Centre for Microdata Methods and Practice (cemmap)
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Online Access: | https://doi.org/10.1920/wp.cem.2020.2120 http://hdl.handle.net/10419/241896 |
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fttriple:oai:gotriple.eu:oai:econstor.eu:10419/241896 2023-05-15T16:48:57+02:00 Estimating the fraction of unreported infections in epidemics with a known epicenter: An application to COVID-19 Hortaçsu, Ali Liu, Jiarui Schwieg, Timothy 2020-01-01 https://doi.org/10.1920/wp.cem.2020.2120 http://hdl.handle.net/10419/241896 en eng London: Centre for Microdata Methods and Practice (cemmap) gbv-ppn:1698614403 doi:10.1920/wp.cem.2020.2120 RePEc:ifs:cemmap:21/20 http://hdl.handle.net/10419/241896 other ddc:330 Coronavirus Sterblichkeit Messung Schätzung USA archeo geo Journal Article https://vocabularies.coar-repositories.org/resource_types/c_6501/ 2020 fttriple https://doi.org/10.1920/wp.cem.2020.2120 2023-01-22T19:09:03Z We develop an analytically tractable 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 U.S. during the first half of March 2020. Our method utilizes the covariation in initial reported infections across U.S. regions and the number of travelers to these regions from the epicenter, along with the results of an early randomized testing study in Iceland. Using our estimates of the number of unreported infections, which are substantially larger than the number of reported infections, we also provide estimates for the infection fatality rate using data on reported COVID-19 fatalities from U.S. counties. Article in Journal/Newspaper Iceland Unknown |
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English |
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ddc:330 Coronavirus Sterblichkeit Messung Schätzung USA archeo geo |
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ddc:330 Coronavirus Sterblichkeit Messung Schätzung USA archeo geo Hortaçsu, Ali Liu, Jiarui Schwieg, Timothy Estimating the fraction of unreported infections in epidemics with a known epicenter: An application to COVID-19 |
topic_facet |
ddc:330 Coronavirus Sterblichkeit Messung Schätzung USA archeo geo |
description |
We develop an analytically tractable 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 U.S. during the first half of March 2020. Our method utilizes the covariation in initial reported infections across U.S. regions and the number of travelers to these regions from the epicenter, along with the results of an early randomized testing study in Iceland. Using our estimates of the number of unreported infections, which are substantially larger than the number of reported infections, we also provide estimates for the infection fatality rate using data on reported COVID-19 fatalities from U.S. counties. |
format |
Article in Journal/Newspaper |
author |
Hortaçsu, Ali Liu, Jiarui Schwieg, Timothy |
author_facet |
Hortaçsu, Ali Liu, Jiarui Schwieg, Timothy |
author_sort |
Hortaçsu, Ali |
title |
Estimating the fraction of unreported infections in epidemics with a known epicenter: An application to COVID-19 |
title_short |
Estimating the fraction of unreported infections in epidemics with a known epicenter: An application to COVID-19 |
title_full |
Estimating the fraction of unreported infections in epidemics with a known epicenter: An application to COVID-19 |
title_fullStr |
Estimating the fraction of unreported infections in epidemics with a known epicenter: An application to COVID-19 |
title_full_unstemmed |
Estimating the fraction of unreported infections in epidemics with a known epicenter: An application to COVID-19 |
title_sort |
estimating the fraction of unreported infections in epidemics with a known epicenter: an application to covid-19 |
publisher |
London: Centre for Microdata Methods and Practice (cemmap) |
publishDate |
2020 |
url |
https://doi.org/10.1920/wp.cem.2020.2120 http://hdl.handle.net/10419/241896 |
genre |
Iceland |
genre_facet |
Iceland |
op_relation |
gbv-ppn:1698614403 doi:10.1920/wp.cem.2020.2120 RePEc:ifs:cemmap:21/20 http://hdl.handle.net/10419/241896 |
op_rights |
other |
op_doi |
https://doi.org/10.1920/wp.cem.2020.2120 |
_version_ |
1766039031257235456 |