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      Study on the COVID-19 epidemic in mainland China between November 2022 and January 2023, with prediction of its tendency

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          Abstract

          The prediction system EpiSIX was used to study the COVID-19 epidemic in mainland China between November 2022 and January 2023, based on reported data from December 9, 2022, to January 30, 2023, released by The Chinese Center for Disease Control and Prevention on February 1, 2023. Three kinds of reported data were used for model fitting: the daily numbers of positive nucleic acid tests and deaths, and the daily number of hospital beds taken by COVID-19 patients. It was estimated that the overall infection rate was 87.54% and the overall case fatality rate was 0.078%–0.116% (median 0.100%). Assuming that a new COVID-19 epidemic outbreak would start in March or April of 2023, induced by a slightly more infectious mutant strain, we predicted a possible large rebound between September and October 2023, with a peak demand of between 800,000 and 900,000 inpatient beds. If no such new outbreak was induced by other variants, then the current COVID-19 epidemic course in mainland China would remain under control until the end of 2023. However, it is suggested that the necessary medical resources be prepared to manage possible COVID-19 epidemic emergencies in the near future, especially for the period between September and October 2023.

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          A new SEIR epidemic model with applications to the theory of eradication and control of diseases, and to the calculation of R0.

          We present a novel SEIR (susceptible-exposure-infective-recovered) model that is suitable for modeling the eradication of diseases by mass vaccination or control of diseases by case isolation combined with contact tracing, incorporating the vaccine efficacy or the control efficacy into the model. Moreover, relying on this novel SEIR model and some probabilistic arguments, we have found four formulas that are suitable for estimating the basic reproductive numbers R(0) in terms of the ratio of the mean infectious period to the mean latent period of a disease. The ranges of R(0) for most known diseases, that are calculated by our formulas, coincide very well with the values of R(0) estimated by the usual method of fitting the models to observed data.
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            Studies of the strategies for controlling the COVID-19 epidemic in China: Estimation of control efficacy and suggestions for policy makers

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              [Progress in research of etiology and epidemiology of 2019-nCoV Omicron variant].

              2019-nCoV Omicron variant has become predominant in the world. New subvariants with further mutations in their spike proteins are continuously emerging. Compared with the wild type and other variants of concern, Omicron variant exhibits altered etiological and epidemiological characteristics, with weakened pathogenicity and toxicity in laboratory mice and hamsters as well as enhanced immune escape capacity. The human infections are more likely to be asymptomatic and mild characterized by upper respiratory tract symptoms with reduced risk of hospitalization and death. In addition, Omicron variant can transmit more rapidly and shows shorter incubation period to cause infection, and the variant is more likely to transmit through contamination of object surfaces and aerosols spread. This paper summarizes the etiological and epidemiological characteristics of Omicron variant to provide a reference for the effective prevention and control of Omicron variant infection.
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                Author and article information

                Journal
                J Biosaf Biosecur
                J Biosaf Biosecur
                Journal of Biosafety and Biosecurity
                Published by Elsevier B.V. on behalf of KeAi Communications Co., Ltd.
                2588-9338
                22 March 2023
                22 March 2023
                Affiliations
                [a ]Xi’an Center for Disease Control and Prevention, Xi’an 710054, Shaanxi, China
                [b ]Department of Biostatistics, School of Public Health, Nanjing Medical University, Nanjing 211166, Jiangsu, China
                [c ]School of Mathematics and Statistics, Fuzhou University, Fuzhou 350116, Fujian, China
                [d ]Complex Systems Research Center, Key Discipline of Computer Science and Technology of “Double-First-Class” Project of Shanxi Province, Shanxi University, Taiyuan 030006, Shanxi, China
                [e ]Nankai Institute of Economics, Binhai Development Institute, Nankai University, Tianjin 300071, China
                [f ]Children’s Hospital of Chongqing Medical University, Chongqing 400010, China
                [g ]Jinzhou Center for Disease Control and Prevention, Jinzhou 121000, Liaoning, China
                [h ]School of Statistics and Data Science, Nankai University, Tianjin 300071, China
                [i ]Suzhou Center for Disease Control and Prevention, Suzhou 215004, Jiangsu, China
                [j ]State Key Laboratory of Communicable Disease Prevention and Control, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing 102206, China
                [k ]Institute of Public Health, Nankai University, Tianjin 300071, China
                Author notes
                [* ]Corresponding author at:(s) at School of Statistics and Data Science, ZhiYing Research Center for Health Data, Nankai University, Tianjin 300071, China
                [l]

                School of Statistics and Data Science, ZhiYing Research Center for Health Data, Nankai University, Tianjin 300071, China

                Article
                S2588-9338(23)00012-2
                10.1016/j.jobb.2023.03.001
                10030260
                36992708
                3f8ba8b3-80d7-4e6f-9292-2a45f2bae6ec
                © 2023 Published by Elsevier B.V. on behalf of KeAi Communications Co., Ltd.

                Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.

                History
                : 3 March 2023
                : 12 March 2023
                : 13 March 2023
                Categories
                Research Article

                covid-19,epidemic,infection rate,case fatality rate,prediction

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