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Linking key intervention timing to rapid decline of the COVID-19 effective reproductive number to quantify lessons from mainland China

Linking key intervention timing to rapid decline of the COVID-19 effective reproductive number to quantify lessons from mainland China

Xiao, Yanni, Tang, Biao, Wu, Jianhong, Cheke, Robert A. ORCID: 0000-0002-7437-1934 and Tang, Sanyi (2020) Linking key intervention timing to rapid decline of the COVID-19 effective reproductive number to quantify lessons from mainland China. International Journal of Infectious Diseases, 97. pp. 296-298. ISSN 1201-9712 (Print), 1878-3511 (Online) (doi:https://doi.org/10.1016/j.ijid.2020.06.030)

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Abstract

Effective reproductive numbers (Rt) were calculated from data on the COVID-19 outbreak in China and linked to dates in 2020 when different interventions were enacted. From a maximum of 3.98 before the lockdown in Wuhan City, the values of Rt declined to below 1 by the second week of February, after the construction of hospitals dedicated to COVID-19 patients. The Rt continued to decline following additional measures in line with the policy of “early detection, early report, early quarantine, and early treatment.” The results provide quantitative evaluations of how intervention measures and their timings succeeded, from which lessons can be learned by other countries dealing with future outbreaks.

Item Type: Article
Additional Information: © 2020 The Author(s). Published by Elsevier Ltd on behalf of International Society for Infectious Diseases. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Uncontrolled Keywords: Effective reproductive number, Intervention timing, COVID-19, Lockdown
Subjects: S Agriculture > S Agriculture (General)
Faculty / Department / Research Group: Faculty of Engineering & Science
Faculty of Engineering & Science > Natural Resources Institute
Faculty of Engineering & Science > Natural Resources Institute > Agriculture, Health & Environment Department
Last Modified: 17 Jul 2020 18:10
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: None
Selected for GREAT 2019: None
Selected for REF2021: None
URI: http://gala.gre.ac.uk/id/eprint/28862

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