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      The COVID-19 outbreak in Sichuan, China: Epidemiology and impact of interventions

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          Abstract

          In January 2020, a COVID-19 outbreak was detected in Sichuan Province of China. Six weeks later, the outbreak was successfully contained. The aim of this work is to characterize the epidemiology of the Sichuan outbreak and estimate the impact of interventions in limiting SARS-CoV-2 transmission. We analyzed patient records for all laboratory-confirmed cases reported in the province for the period of January 21 to March 16, 2020. To estimate the basic and daily reproduction numbers, we used a Bayesian framework. In addition, we estimated the number of cases averted by the implemented control strategies. The outbreak resulted in 539 confirmed cases, lasted less than two months, and no further local transmission was detected after February 27. The median age of local cases was 8 years older than that of imported cases. We estimated R 0 at 2.4 (95% CI: 1.6–3.7). The epidemic was self-sustained for about 3 weeks before going below the epidemic threshold 3 days after the declaration of a public health emergency by Sichuan authorities. Our findings indicate that, were the control measures be adopted four weeks later, the epidemic could have lasted 49 days longer (95% CI: 31–68 days), causing 9,216 more cases (95% CI: 1,317–25,545).

          Author summary

          Since its emergence in Wuhan, SARS-CoV-2 rapidly started its spread across China. On January 21, 2020 the first COVID-19 case was detected in the Sichuan Province of China and led to an outbreak of local transmission. Less than two months later, the outbreak was over with the last reported case on March 4, 2020. In this study, we analyzed patient records for all laboratory-confirmed cases reported in Sichuan to provide an epidemiological characterization of the outbreak, to estimate SARS-CoV-2 transmission potential, and to assess the impact of the adopted interventions. We estimated that, during the initial exponential growth phase of the outbreak, each COVID-19 case has generated a mean of 2.4 secondary cases (95% CI: 1.6–3.7). Moreover, we estimated that, were the Sichuan strict containment measures implemented four weeks later, the outbreak would have caused 9,216 more cases (95% CI: 1,317–25,545). Our findings suggest the key role of a quick response to COVID-19 outbreaks and the importance of an adequate surveillance and monitoring system.

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          Most cited references29

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          Characteristics of and Important Lessons From the Coronavirus Disease 2019 (COVID-19) Outbreak in China: Summary of a Report of 72 314 Cases From the Chinese Center for Disease Control and Prevention

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            Early Transmission Dynamics in Wuhan, China, of Novel Coronavirus–Infected Pneumonia

            Abstract Background The initial cases of novel coronavirus (2019-nCoV)–infected pneumonia (NCIP) occurred in Wuhan, Hubei Province, China, in December 2019 and January 2020. We analyzed data on the first 425 confirmed cases in Wuhan to determine the epidemiologic characteristics of NCIP. Methods We collected information on demographic characteristics, exposure history, and illness timelines of laboratory-confirmed cases of NCIP that had been reported by January 22, 2020. We described characteristics of the cases and estimated the key epidemiologic time-delay distributions. In the early period of exponential growth, we estimated the epidemic doubling time and the basic reproductive number. Results Among the first 425 patients with confirmed NCIP, the median age was 59 years and 56% were male. The majority of cases (55%) with onset before January 1, 2020, were linked to the Huanan Seafood Wholesale Market, as compared with 8.6% of the subsequent cases. The mean incubation period was 5.2 days (95% confidence interval [CI], 4.1 to 7.0), with the 95th percentile of the distribution at 12.5 days. In its early stages, the epidemic doubled in size every 7.4 days. With a mean serial interval of 7.5 days (95% CI, 5.3 to 19), the basic reproductive number was estimated to be 2.2 (95% CI, 1.4 to 3.9). Conclusions On the basis of this information, there is evidence that human-to-human transmission has occurred among close contacts since the middle of December 2019. Considerable efforts to reduce transmission will be required to control outbreaks if similar dynamics apply elsewhere. Measures to prevent or reduce transmission should be implemented in populations at risk. (Funded by the Ministry of Science and Technology of China and others.)
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              Estimating the effects of non-pharmaceutical interventions on COVID-19 in Europe

              Following the detection of the new coronavirus1 severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and its spread outside of China, Europe has experienced large epidemics of coronavirus disease 2019 (COVID-19). In response, many European countries have implemented non-pharmaceutical interventions, such as the closure of schools and national lockdowns. Here we study the effect of major interventions across 11 European countries for the period from the start of the COVID-19 epidemics in February 2020 until 4 May 2020, when lockdowns started to be lifted. Our model calculates backwards from observed deaths to estimate transmission that occurred several weeks previously, allowing for the time lag between infection and death. We use partial pooling of information between countries, with both individual and shared effects on the time-varying reproduction number (Rt). Pooling allows for more information to be used, helps to overcome idiosyncrasies in the data and enables more-timely estimates. Our model relies on fixed estimates of some epidemiological parameters (such as the infection fatality rate), does not include importation or subnational variation and assumes that changes in Rt are an immediate response to interventions rather than gradual changes in behaviour. Amidst the ongoing pandemic, we rely on death data that are incomplete, show systematic biases in reporting and are subject to future consolidation. We estimate that-for all of the countries we consider here-current interventions have been sufficient to drive Rt below 1 (probability Rt < 1.0 is greater than 99%) and achieve control of the epidemic. We estimate that across all 11 countries combined, between 12 and 15 million individuals were infected with SARS-CoV-2 up to 4 May 2020, representing between 3.2% and 4.0% of the population. Our results show that major non-pharmaceutical interventions-and lockdowns in particular-have had a large effect on reducing transmission. Continued intervention should be considered to keep transmission of SARS-CoV-2 under control.
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                Author and article information

                Contributors
                Role: ConceptualizationRole: Formal analysisRole: InvestigationRole: MethodologyRole: SoftwareRole: ValidationRole: VisualizationRole: Writing – review & editing
                Role: InvestigationRole: Writing – original draft
                Role: Data curationRole: Formal analysis
                Role: Data curation
                Role: Data curation
                Role: Investigation
                Role: ConceptualizationRole: InvestigationRole: Writing – review & editing
                Role: Investigation
                Role: InvestigationRole: Writing – review & editing
                Role: Data curationRole: Writing – review & editing
                Role: Funding acquisitionRole: InvestigationRole: Writing – review & editing
                Role: ConceptualizationRole: Formal analysisRole: Funding acquisitionRole: InvestigationRole: MethodologyRole: SoftwareRole: SupervisionRole: ValidationRole: VisualizationRole: Writing – original draft
                Role: Editor
                Journal
                PLoS Comput Biol
                PLoS Comput Biol
                plos
                ploscomp
                PLoS Computational Biology
                Public Library of Science (San Francisco, CA USA )
                1553-734X
                1553-7358
                28 December 2020
                December 2020
                : 16
                : 12
                : e1008467
                Affiliations
                [1 ] College of Computer Science, Sichuan University, Chengdu, China
                [2 ] Department of Epidemiology and Biostatistics, School of Public Health, Indiana University Bloomington, Bloomington, Indiana, United States of America
                [3 ] Department of Engineering and Computer Science, New York University Shanghai, Shanghai, China
                [4 ] Institution of New Economic Development, Chengdu, China
                [5 ] Bruno Kessler Foundation, Trento, Italy
                [6 ] Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, Massachusetts United States of America
                [7 ] ISI Foundation, Turin, Italy
                [8 ] School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China
                [9 ] West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China
                [10 ] Big Data Research Center, University of Electronic Science and Technology of China, Chengdu, China
                [11 ] Tianfu Complexity Science Research Center, Chengdu, China
                University of Notre Dame, UNITED STATES
                Author notes

                H.Y. has received research funding from Sanofi Pasteur, GlaxoSmithKline, Yichang HEC Changjiang Pharmaceutical Company, and Shanghai Roche Pharmaceutical Company. A.V. reports grants from Metabiota inc., outside the submitted work. M.A. has received research funding from Seqirus. None of those research funding is related to COVID-19. All other authors report no competing interests.

                ‡ These authors are joint senior authors on this work.

                Author information
                https://orcid.org/0000-0001-6642-6854
                https://orcid.org/0000-0001-7728-007X
                https://orcid.org/0000-0003-1136-6801
                https://orcid.org/0000-0002-5965-1743
                https://orcid.org/0000-0002-5117-0611
                https://orcid.org/0000-0002-6335-5648
                https://orcid.org/0000-0003-0561-2316
                https://orcid.org/0000-0003-1753-4749
                Article
                PCOMPBIOL-D-20-01325
                10.1371/journal.pcbi.1008467
                7794025
                33370263
                bf6e00e2-4959-42dc-9033-0df5e11dc559
                © 2020 Liu et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 24 July 2020
                : 26 October 2020
                Page count
                Figures: 3, Tables: 2, Pages: 14
                Funding
                Funded by: funder-id http://dx.doi.org/10.13039/501100001809, National Natural Science Foundation of China;
                Award ID: 62003230
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/501100010822, Chengdu Science and Technology Bureau;
                Award ID: 2020-YF05-00073-SN
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/501100012226, Fundamental Research Funds for the Central Universities;
                Award ID: 1082204112289
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/501100004829, Department of Science and Technology of Sichuan Province;
                Award ID: 2020YFS0009
                Award Recipient :
                Funded by: Special Funds for Prevention and Control of COVID-19 of Sichuan University
                Award ID: 0082604151026
                Award Recipient :
                Funded by: National Science Fund for Distinguished Young Scholars
                Award ID: 81525023
                Award Recipient :
                Funded by: National Science and Technology Major Project of China (
                Award ID: 2018ZX10713001-007,2017ZX10103009-005,2018ZX10201001-010
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/501100004829, Department of Science and Technology of Sichuan Province;
                Award ID: 2020YFS0007
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/501100001809, National Natural Science Foundation of China;
                Award ID: 11975071, 61673085
                Award Recipient :
                Q.-H.L. has received funding from the National Natural Science Foundation of China ( http://www.nsfc.gov.cn/, No. 62003230), Chengdu Science and Technology Bureau ( http://cdst.chengdu.gov.cn/, No. 2020-YF05-00073-SN), the Fundamental Research Funds for the Central Universities ( http://www.moe.gov.cn/, No. 1082204112289), the Science and Technology Department of Sichuan Province ( http://kjt.sc.gov.cn/, No. 2020YFS0009), the Special Funds for Prevention and Control of COVID-19 of Sichuan University ( http://www.scu.edu.cn/, No. 0082604151026). H.Y. has received funding from the National Science Fund for Distinguished Young Scholars ( http://www.nsfc.gov.cn/, No. 81525023) and National Science and Technology Major Project of China ( http://www.nsfc.gov.cn/, No. 2018ZX10713001-007, No. 2017ZX10103009-005, No.2018ZX10201001-010). W.Z. has received funding from the Science and Technology Department of Sichuan Province ( http://kjt.sc.gov.cn/, No. 2020YFS0007). T.Z. has received funding from the National Natural Science Foundation of China ( http://www.nsfc.gov.cn/No.11975071, No. 61673085). The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.
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                All data is in the manuscript and the supporting files. Code and data needed to replicate the results of our analyses are available from GitHub at: https://github.com/QH-Liu/Sichuan-COVID-19.
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