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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ftzbwkiel:oai:econstor.eu:10419/241896 2023-12-10T09:49:56+01:00 Estimating the fraction of unreported infections in epidemics with a known epicenter: An application to COVID-19 Hortaçsu, Ali Liu, Jiarui Schwieg, Timothy 2020 http://hdl.handle.net/10419/241896 https://doi.org/10.1920/wp.cem.2020.2120 eng eng London: Centre for Microdata Methods and Practice (cemmap) Series: cemmap working paper No. CWP21/20 gbv-ppn:1698614403 doi:10.1920/wp.cem.2020.2120 http://hdl.handle.net/10419/241896 RePEc:ifs:cemmap:21/20 http://www.econstor.eu/dspace/Nutzungsbedingungen ddc:330 Coronavirus Sterblichkeit Messung Schätzung USA doc-type:workingPaper 2020 ftzbwkiel https://doi.org/10.1920/wp.cem.2020.2120 2023-11-13T00:43:16Z 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. Report Iceland EconStor (German National Library of Economics, ZBW) |
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EconStor (German National Library of Economics, ZBW) |
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English |
topic |
ddc:330 Coronavirus Sterblichkeit Messung Schätzung USA |
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ddc:330 Coronavirus Sterblichkeit Messung Schätzung USA 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 |
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 |
Report |
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 |
http://hdl.handle.net/10419/241896 https://doi.org/10.1920/wp.cem.2020.2120 |
genre |
Iceland |
genre_facet |
Iceland |
op_relation |
Series: cemmap working paper No. CWP21/20 gbv-ppn:1698614403 doi:10.1920/wp.cem.2020.2120 http://hdl.handle.net/10419/241896 RePEc:ifs:cemmap:21/20 |
op_rights |
http://www.econstor.eu/dspace/Nutzungsbedingungen |
op_doi |
https://doi.org/10.1920/wp.cem.2020.2120 |
_version_ |
1784894711654252544 |