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Li Y, Jiang X, Qiu Y, Gao F, Xin H, Li D, Qin Y, Li Z. Latent and incubation periods of Delta, BA.1, and BA.2 variant cases and associated factors: a cross-sectional study in China. BMC Infect Dis 2024; 24:294. [PMID: 38448822 PMCID: PMC10916204 DOI: 10.1186/s12879-024-09158-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2024] [Accepted: 02/20/2024] [Indexed: 03/08/2024] Open
Abstract
BACKGROUND The latent and incubation periods characterize the transmission of infectious viruses and are the basis for the development of outbreak prevention and control strategies. However, systematic studies on the latent period and associated factors with the incubation period for SAS-CoV-2 variants are still lacking. We inferred the two durations of Delta, BA.1, and BA.2 cases and analyzed the associated factors. METHODS The Delta, BA.1, and BA.2 (and its lineages BA.2.2 and BA.2.76) cases with clear transmission chains and infectors from 10 local SAS-CoV-2 epidemics in China were enrolled. The latent and incubation periods were fitted by the Gamma distribution, and associated factors were analyzed using the accelerated failure time model. RESULTS The mean latent period for 672 Delta, 208 BA.1, and 677 BA.2 cases was 4.40 (95%CI: 4.24 ~ 4.63), 2.50 (95%CI: 2.27 ~ 2.76), and 2.58 (95%CI: 2.48 ~ 2.69) days, respectively, with 85.65% (95%CI: 83.40 ~ 87.77%), 97.80% (95%CI: 96.35 ~ 98.89%), and 98.87% (95%CI: 98.40 ~ 99.27%) of them starting to shed viruses within 7 days after exposure. In 405 Delta, 75 BA.1, and 345 BA.2 symptomatic cases, the mean latent period was 0.76, 1.07, and 0.79 days shorter than the mean incubation period [5.04 (95%CI: 4.83 ~ 5.33), 3.42 (95%CI: 3.00 ~ 3.89), and 3.39 (95%CI: 3.24 ~ 3.55) days], respectively. No significant difference was observed in the two durations between BA.1 and BA.2 cases. After controlling for the sex, clinical severity, vaccination history, number of infectors, the length of exposure window and shedding window, the latent period [Delta: exp(β) = 0.81, 95%CI: 0.66 ~ 0.98, p = 0.034; Omicron: exp(β) = 0.82, 95%CI: 0.71 ~ 0.94, p = 0.004] and incubation period [Delta: exp(β) = 0.69, 95%CI: 0.55 ~ 0.86, p < 0.001; Omicron: exp(β) = 0.83, 95%CI: 0.72 ~ 0.96, p = 0.013] were significantly shorter in 18 ~ 49 years but did not change significantly in ≥ 50 years compared with 0 ~ 17 years. CONCLUSION Pre-symptomatic transmission can occur in Delta, BA.1, and BA.2 cases. The latent and incubation periods between BA.1 and BA.2 were similar but shorter compared with Delta. Age may be associated with the latent and incubation periods of SARS-CoV-2.
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Affiliation(s)
- Yu Li
- Division of Infectious Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, 102206, China
| | - Xinli Jiang
- Division of Infectious Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, 102206, China
| | - Yan Qiu
- Division of Infectious Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, 102206, China
| | - Feng Gao
- Division of Infectious Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, 102206, China
| | - Hualei Xin
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, Hong Kong, China
- School of Population Medicine and Public Health, Chinese Academy of Medical Science (CAMS), Peking Union Medical College (PUMC), No. 9, Dongdan Santiao, Dongcheng District, Beijing, 100730, China
| | - Dan Li
- Division of Infectious Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, 102206, China
| | - Ying Qin
- Division of Infectious Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, 102206, China
| | - Zhongjie Li
- Division of Infectious Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, 102206, China.
- School of Population Medicine and Public Health, Chinese Academy of Medical Science (CAMS), Peking Union Medical College (PUMC), No. 9, Dongdan Santiao, Dongcheng District, Beijing, 100730, China.
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Zhang W, Yue Y, Hu M, Du C, Wang C, Tuo X, Jiang X, Fan S, Chen Z, Chen H, Liang X, Luan R. Epidemiological characteristics and quarantine assessment of imported international COVID-19 cases, March to December 2020, Chengdu, China. Sci Rep 2022; 12:21132. [PMID: 36477091 PMCID: PMC9729223 DOI: 10.1038/s41598-022-20712-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Accepted: 09/16/2022] [Indexed: 12/12/2022] Open
Abstract
International flights have accelerated the global spread of Coronavirus Disease 2019 (COVID-19). Determination of the optimal quarantine period for international travelers is crucial to prevent the local spread caused by imported COVID-19 cases. We performed a retrospective epidemiological study using 491 imported COVID-19 cases in Chengdu, China, to describe the characteristic of the cases and estimate the time from arrival to confirmation for international travelers using nonparametric survival methods. Among the 491 imported COVID-19 cases, 194 (39.5%) were asymptomatic infections. The mean age was 35.6 years (SD = 12.1 years) and 83.3% were men. The majority (74.1%) were screened positive for SARS-CoV-2, conducted by Chengdu Customs District, the People's Republic of China. Asymptomatic cases were younger than presymptomatic or symptomatic cases (P < 0.01). The daily number of imported COVID-19 cases displayed jagged changes. 95% of COVID-19 cases were confirmed by PT-PCR within 14 days (95% CI 13-15) after arriving in Chengdu. A 14-day quarantine measure can ensure non-infection among international travelers with a 95% probability. Policymakers may consider an extension of the quarantine period to minimize the negative consequences of the COVID-19 confinement and prevent the international spread of COVID-19. Nevertheless, the government should consider the balance between COVID-19 and socioeconomic development, which may cause more serious social and health crises.
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Affiliation(s)
- Wenqiang Zhang
- grid.506261.60000 0001 0706 7839Chengdu Workstation for Emerging Infectious Disease Control and Prevention, Chinese Academy of Medical Sciences, Chengdu, 610041 Sichuan China ,grid.13291.380000 0001 0807 1581Department of Epidemiology and Health Statistics, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, 610041 Sichuan China
| | - Yong Yue
- grid.506261.60000 0001 0706 7839Chengdu Workstation for Emerging Infectious Disease Control and Prevention, Chinese Academy of Medical Sciences, Chengdu, 610041 Sichuan China ,grid.507966.bChengdu Center for Disease Control and Prevention, Chengdu, 610041 Sichuan China
| | - Min Hu
- grid.506261.60000 0001 0706 7839Chengdu Workstation for Emerging Infectious Disease Control and Prevention, Chinese Academy of Medical Sciences, Chengdu, 610041 Sichuan China ,grid.507966.bChengdu Center for Disease Control and Prevention, Chengdu, 610041 Sichuan China
| | - Changhui Du
- grid.506261.60000 0001 0706 7839Chengdu Workstation for Emerging Infectious Disease Control and Prevention, Chinese Academy of Medical Sciences, Chengdu, 610041 Sichuan China ,grid.507966.bChengdu Center for Disease Control and Prevention, Chengdu, 610041 Sichuan China
| | - Cheng Wang
- grid.506261.60000 0001 0706 7839Chengdu Workstation for Emerging Infectious Disease Control and Prevention, Chinese Academy of Medical Sciences, Chengdu, 610041 Sichuan China ,grid.507966.bChengdu Center for Disease Control and Prevention, Chengdu, 610041 Sichuan China
| | - Xiaoli Tuo
- grid.506261.60000 0001 0706 7839Chengdu Workstation for Emerging Infectious Disease Control and Prevention, Chinese Academy of Medical Sciences, Chengdu, 610041 Sichuan China ,grid.507966.bChengdu Center for Disease Control and Prevention, Chengdu, 610041 Sichuan China
| | - Xiaoman Jiang
- grid.506261.60000 0001 0706 7839Chengdu Workstation for Emerging Infectious Disease Control and Prevention, Chinese Academy of Medical Sciences, Chengdu, 610041 Sichuan China ,grid.507966.bChengdu Center for Disease Control and Prevention, Chengdu, 610041 Sichuan China
| | - Shuangfeng Fan
- grid.506261.60000 0001 0706 7839Chengdu Workstation for Emerging Infectious Disease Control and Prevention, Chinese Academy of Medical Sciences, Chengdu, 610041 Sichuan China ,grid.507966.bChengdu Center for Disease Control and Prevention, Chengdu, 610041 Sichuan China
| | - Zhenhua Chen
- grid.506261.60000 0001 0706 7839Chengdu Workstation for Emerging Infectious Disease Control and Prevention, Chinese Academy of Medical Sciences, Chengdu, 610041 Sichuan China ,grid.507966.bChengdu Center for Disease Control and Prevention, Chengdu, 610041 Sichuan China
| | - Heng Chen
- grid.506261.60000 0001 0706 7839Chengdu Workstation for Emerging Infectious Disease Control and Prevention, Chinese Academy of Medical Sciences, Chengdu, 610041 Sichuan China ,grid.507966.bChengdu Center for Disease Control and Prevention, Chengdu, 610041 Sichuan China
| | - Xian Liang
- grid.506261.60000 0001 0706 7839Chengdu Workstation for Emerging Infectious Disease Control and Prevention, Chinese Academy of Medical Sciences, Chengdu, 610041 Sichuan China ,grid.507966.bChengdu Center for Disease Control and Prevention, Chengdu, 610041 Sichuan China
| | - Rongsheng Luan
- grid.506261.60000 0001 0706 7839Chengdu Workstation for Emerging Infectious Disease Control and Prevention, Chinese Academy of Medical Sciences, Chengdu, 610041 Sichuan China ,grid.13291.380000 0001 0807 1581Department of Epidemiology and Health Statistics, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, 610041 Sichuan China
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Song Q, Asghar N, Ullah A, Liang B, Long M, Hu T, Zhou X. Two-Dose Vaccination Significantly Prolongs the Duration from Symptom Onset to Death: A Retrospective Study Based on 173,894 SARS-CoV-2 Cases in Khyber Pakhtunkhwa, Pakistan. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:11531. [PMID: 36141802 PMCID: PMC9517462 DOI: 10.3390/ijerph191811531] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/19/2022] [Revised: 09/08/2022] [Accepted: 09/09/2022] [Indexed: 06/16/2023]
Abstract
This research was carried out to quantify the duration from symptom onset to recovery/death (SOR/SOD) during the first four waves and the Alpha/Delta period of the epidemic in Khyber Pakhtunkhwa, Pakistan, and identify the associated factors. A total of 173,894 COVID-19 cases were admitted between 16 March 2020 and 30 November 2021, including 458 intensive care unit (ICU) cases. The results showed that the case fatality rate (CFR) increased with age, and females had a higher CFR. The median SOR of ICU cases was longer than that of non-ICU cases (27.6 vs. 17.0 days), while the median SOD was much shorter (6.9 vs. 8.4 days). The SOR and SOD in the Delta period were slightly shortened than the Alpha period. Age, cardiovascular diseases, chronic lung disease, diabetes, fever, breathing issues, and ICU admission were risk factors that were significantly associated with SOD (p < 0.001). A control measure, in-home quarantine, was found to be significantly associated with longer SOD (odds ratio = 9.49, p < 0.001). Infected vaccinated individuals had longer SOD than unvaccinated individuals, especially for cases that had received two vaccine doses (p < 0.001). Finally, an advice on getting full-dose vaccination is given specifically to individuals aged 20-59 years.
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Affiliation(s)
- Qianqian Song
- Department of Biostatistics, Peking University, Beijing 100191, China
| | - Naseem Asghar
- Department of Statistics, Abdul Wali Khan University, Mardan 23200, Pakistan
| | - Ata Ullah
- Division of Life Science, Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China
| | - Baosheng Liang
- Department of Biostatistics, Peking University, Beijing 100191, China
| | - Mengping Long
- Department of Pathology, Peking University Cancer Hospital, Beijing 100083, China
| | - Taobo Hu
- Chongqing Research Institute of Big Data, Peking University, Chongqing 401121, China
- Department of Breast Surgery, Peking University People’s Hospital, Beijing 100044, China
| | - Xiaohua Zhou
- Department of Biostatistics, Peking University, Beijing 100191, China
- Chongqing Research Institute of Big Data, Peking University, Chongqing 401121, China
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Rodriguez J, Price O, Jennings R, Creel A, Eaton S, Chesnutt J, McClellan G, Batni SR. A Novel Framework for Modeling Person-to-Person Transmission of Respiratory Diseases. Viruses 2022; 14:1567. [PMID: 35891547 PMCID: PMC9322782 DOI: 10.3390/v14071567] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2022] [Revised: 06/27/2022] [Accepted: 07/08/2022] [Indexed: 11/16/2022] Open
Abstract
From the beginning of the COVID-19 pandemic, researchers assessed the impact of the disease in terms of loss of life, medical load, economic damage, and other key metrics of resiliency and consequence mitigation; these studies sought to parametrize the critical components of a disease transmission model and the resulting analyses were informative but often lacked critical parameters or a discussion of parameter sensitivities. Using SARS-CoV-2 as a case study, we present a robust modeling framework that considers disease transmissibility from the source through transport and dispersion and infectivity. The framework is designed to work across a range of particle sizes and estimate the generation rate, environmental fate, deposited dose, and infection, allowing for end-to-end analysis that can be transitioned to individual and population health models. In this paper, we perform sensitivity analysis on the model framework to demonstrate how it can be used to advance and prioritize research efforts by highlighting critical parameters for further analyses.
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Affiliation(s)
- Jason Rodriguez
- Applied Research Associates, Inc. (ARA), 4300 San Mateo Blvd NE, Suite A220, Albuquerque, NM 87110, USA; (J.R.); (O.P.); (R.J.); (A.C.); (S.E.); (J.C.); (G.M.)
| | - Owen Price
- Applied Research Associates, Inc. (ARA), 4300 San Mateo Blvd NE, Suite A220, Albuquerque, NM 87110, USA; (J.R.); (O.P.); (R.J.); (A.C.); (S.E.); (J.C.); (G.M.)
| | - Rachel Jennings
- Applied Research Associates, Inc. (ARA), 4300 San Mateo Blvd NE, Suite A220, Albuquerque, NM 87110, USA; (J.R.); (O.P.); (R.J.); (A.C.); (S.E.); (J.C.); (G.M.)
| | - Amy Creel
- Applied Research Associates, Inc. (ARA), 4300 San Mateo Blvd NE, Suite A220, Albuquerque, NM 87110, USA; (J.R.); (O.P.); (R.J.); (A.C.); (S.E.); (J.C.); (G.M.)
| | - Sarah Eaton
- Applied Research Associates, Inc. (ARA), 4300 San Mateo Blvd NE, Suite A220, Albuquerque, NM 87110, USA; (J.R.); (O.P.); (R.J.); (A.C.); (S.E.); (J.C.); (G.M.)
| | - Jennifer Chesnutt
- Applied Research Associates, Inc. (ARA), 4300 San Mateo Blvd NE, Suite A220, Albuquerque, NM 87110, USA; (J.R.); (O.P.); (R.J.); (A.C.); (S.E.); (J.C.); (G.M.)
| | - Gene McClellan
- Applied Research Associates, Inc. (ARA), 4300 San Mateo Blvd NE, Suite A220, Albuquerque, NM 87110, USA; (J.R.); (O.P.); (R.J.); (A.C.); (S.E.); (J.C.); (G.M.)
| | - Sweta R. Batni
- Defense Threat Reduction Agency (DTRA), 8725 John J. Kingman Road #6201, Fort Belvoir, VA 22060, USA
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Silenou BC, Verset C, Kaburi BB, Leuci O, Ghozzi S, Duboudin C, Krause G. A Novel Tool for Real-Time Estimation of Epidemiological Parameters of Communicable Diseases Using Contact-Tracing Data: Development and Deployment. JMIR Public Health Surveill 2022; 8:e34438. [PMID: 35486812 PMCID: PMC9159465 DOI: 10.2196/34438] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2021] [Revised: 03/06/2022] [Accepted: 04/26/2022] [Indexed: 12/28/2022] Open
Abstract
Background The Surveillance Outbreak Response Management and Analysis System (SORMAS) contains a management module to support countries in their epidemic response. It consists of the documentation, linkage, and follow-up of cases, contacts, and events. To allow SORMAS users to visualize data, compute essential surveillance indicators, and estimate epidemiological parameters from such network data in real-time, we developed the SORMAS Statistics (SORMAS-Stats) application. Objective This study aims to describe the essential visualizations, surveillance indicators, and epidemiological parameters implemented in the SORMAS-Stats application and illustrate the application of SORMAS-Stats in response to the COVID-19 outbreak. Methods Based on findings from a rapid review and SORMAS user requests, we included the following visualization and estimation of parameters in SORMAS-Stats: transmission network diagram, serial interval (SI), time-varying reproduction number R(t), dispersion parameter k, and additional surveillance indicators presented in graphs and tables. We estimated SI by fitting lognormal, gamma, and Weibull distributions to the observed distribution of the number of days between symptom onset dates of infector-infectee pairs. We estimated k by fitting a negative binomial distribution to the observed number of infectees per infector. Furthermore, we applied the Markov Chain Monte Carlo approach and estimated R(t) using the incidence data and the observed SI computed from the transmission network data. Results Using COVID-19 contact-tracing data of confirmed cases reported between July 31 and October 29, 2021, in the Bourgogne-Franche-Comté region of France, we constructed a network diagram containing 63,570 nodes. The network comprises 1.75% (1115/63,570) events, 19.59% (12,452/63,570) case persons, and 78.66% (50,003/63,570) exposed persons, including 1238 infector-infectee pairs and 3860 transmission chains with 24.69% (953/3860) having events as the index infector. The distribution with the best fit to the observed SI data was a lognormal distribution with a mean of 4.30 (95% CI 4.09-4.51) days. We estimated a dispersion parameter k of 21.11 (95% CI 7.57-34.66) and an effective reproduction number R of 0.9 (95% CI 0.58-0.60). The weekly estimated R(t) values ranged from 0.80 to 1.61. Conclusions We provide an application for real-time estimation of epidemiological parameters, which is essential for informing outbreak response strategies. The estimates are commensurate with findings from previous studies. The SORMAS-Stats application could greatly assist public health authorities in the regions using SORMAS or similar tools by providing extensive visualizations and computation of surveillance indicators.
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Affiliation(s)
- Bernard C Silenou
- Epidemiology Department, Helmholtz Centre for Infection Research, Inhoffenstraße 7, Braunschweig, DE.,PhD Programme Epidemiology, Braunschweig-Hannover, Braunschweig, DE
| | - Carolin Verset
- Agence Régionale de Santé de Bourgogne Franche-Comté (ARS), Dijon, FR
| | - Basil B Kaburi
- Epidemiology Department, Helmholtz Centre for Infection Research, Inhoffenstraße 7, Braunschweig, DE.,PhD Programme Epidemiology, Braunschweig-Hannover, Braunschweig, DE
| | - Olivier Leuci
- Agence Régionale de Santé de Bourgogne Franche-Comté (ARS), Dijon, FR
| | - Stéphane Ghozzi
- Epidemiology Department, Helmholtz Centre for Infection Research, Inhoffenstraße 7, Braunschweig, DE
| | - Cédric Duboudin
- Agence Régionale de Santé de Bourgogne Franche-Comté (ARS), Dijon, FR
| | - Gérard Krause
- Epidemiology Department, Helmholtz Centre for Infection Research, Inhoffenstraße 7, Braunschweig, DE.,German Center for Infection Research, Braunschweig, DE.,Hanover Medical School, Hannover, DE
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Patterson B, Wang J. HOW DOES THE LATENCY PERIOD IMPACT THE MODELING OF COVID-19 TRANSMISSION DYNAMICS? MATHEMATICS IN APPLIED SCIENCES AND ENGINEERING 2022; 3:60-85. [PMID: 35873089 PMCID: PMC9302022 DOI: 10.5206/mase/14537] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
We introduce two mathematical models based on systems of differential equations to investigate the relationship between the latency period and the transmission dynamics of COVID-19. We analyze the equilibrium and stability properties of these models, and perform an asymptotic study in terms of small and large latency periods. We fit the models to the COVID-19 data in the U.S. state of Tennessee. Our numerical results demonstrate the impact of the latency period on the dynamical behaviors of the solutions, on the value of the basic reproduction numbers, and on the accuracy of the model predictions.
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Affiliation(s)
- Ben Patterson
- Department of Mathematics, University of Tennessee at Chattanooga, Chattanooga, TN 37403, USA
| | - Jin Wang
- Department of Mathematics, University of Tennessee at Chattanooga, Chattanooga, TN 37403, USA
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Gil-Jardiné C, Chenais G, Pradeau C, Tentillier E, Revel P, Combes X, Galinski M, Tellier E, Lagarde E. Surveillance of COVID-19 using a keyword search for symptoms in reports from emergency medical communication centers in Gironde, France: a 15 year retrospective cross-sectional study. Intern Emerg Med 2022; 17:603-608. [PMID: 34324146 PMCID: PMC8319585 DOI: 10.1007/s11739-021-02818-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/03/2020] [Accepted: 07/23/2021] [Indexed: 11/28/2022]
Abstract
During periods such as the COVID-19 crisis, there is a need for responsive public health surveillance indicators related to the epidemic. To determine the performance of keyword-search algorithm in call reports to emergency medical communication centers (EMCC) to describe trends in symptoms during the COVID-19 crisis. We retrospectively retrieved all free text call reports from the EMCC of the Gironde department (SAMU 33), France, between 2005 and 2020 and classified them with a simple keyword-based algorithm to identify symptoms relevant to COVID-19. A validation was performed using a sample of manually coded call reports. The six selected symptoms were fever, cough, muscle soreness, dyspnea, ageusia and anosmia. We retrieved 38,08,243 call reports from January 2005 to October 2020. A total of 8539 reports were manually coded for validation and Cohen's kappa statistics ranged from 75 (keyword anosmia) to 59% (keyword dyspnea). There was an unprecedented peak in the number of daily calls mentioning fever, cough, muscle soreness, anosmia, ageusia, and dyspnea during the COVID-19 epidemic, compared to the past 15 years. Calls mentioning cough, fever and muscle soreness began to increase from February 21, 2020. The number of daily calls reporting cough reached 208 on March 3, 2020, a level higher than any in the previous 15 years, and peaked on March 15, 2020, 2 days before lockdown. Calls referring to dyspnea, anosmia and ageusia peaked 12 days later and were concomitant with the daily number of emergency room admissions. Trends in symptoms cited in calls to EMCC during the COVID-19 crisis provide insights into the natural history of COVID-19. The content of calls to EMCC is an efficient epidemiological surveillance data source and should be integrated into the national surveillance system.
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Affiliation(s)
- Cédric Gil-Jardiné
- Inserm, ISPED, Bordeaux Population Health Research Center Inserm U1219 Injury Epidemiology Transport Occupation Team, Bordeaux cedex, France
- Inserm, ISPED, Bordeaux Population Health Research Center Inserm U1219 Injury Epidemiology Transport Occupation Team, Pole of Emergency Medicine, University Hospital of Bordeaux, Bordeaux cedex, France
| | - Gabrielle Chenais
- Inserm, ISPED, Bordeaux Population Health Research Center Inserm U1219 Injury Epidemiology Transport Occupation Team, Bordeaux cedex, France
- Inserm, ISPED, Bordeaux Population Health Research Center Inserm U1219 Injury Epidemiology Transport Occupation Team, Pole of Emergency Medicine, University Hospital of Bordeaux, Bordeaux cedex, France
| | - Catherine Pradeau
- Inserm, ISPED, Bordeaux Population Health Research Center Inserm U1219 Injury Epidemiology Transport Occupation Team, Bordeaux cedex, France
- Inserm, ISPED, Bordeaux Population Health Research Center Inserm U1219 Injury Epidemiology Transport Occupation Team, Pole of Emergency Medicine, University Hospital of Bordeaux, Bordeaux cedex, France
| | - Eric Tentillier
- Inserm, ISPED, Bordeaux Population Health Research Center Inserm U1219 Injury Epidemiology Transport Occupation Team, Pole of Emergency Medicine, University Hospital of Bordeaux, Bordeaux cedex, France
| | - Phillipe Revel
- Inserm, ISPED, Bordeaux Population Health Research Center Inserm U1219 Injury Epidemiology Transport Occupation Team, Bordeaux cedex, France
- Inserm, ISPED, Bordeaux Population Health Research Center Inserm U1219 Injury Epidemiology Transport Occupation Team, Pole of Emergency Medicine, University Hospital of Bordeaux, Bordeaux cedex, France
| | - Xavier Combes
- Inserm, ISPED, Bordeaux Population Health Research Center Inserm U1219 Injury Epidemiology Transport Occupation Team, Bordeaux cedex, France
- Inserm, ISPED, Bordeaux Population Health Research Center Inserm U1219 Injury Epidemiology Transport Occupation Team, Pole of Emergency Medicine, University Hospital of Bordeaux, Bordeaux cedex, France
| | - Michel Galinski
- Inserm, ISPED, Bordeaux Population Health Research Center Inserm U1219 Injury Epidemiology Transport Occupation Team, Bordeaux cedex, France
- Inserm, ISPED, Bordeaux Population Health Research Center Inserm U1219 Injury Epidemiology Transport Occupation Team, Pole of Emergency Medicine, University Hospital of Bordeaux, Bordeaux cedex, France
| | - Eric Tellier
- Inserm, ISPED, Bordeaux Population Health Research Center Inserm U1219 Injury Epidemiology Transport Occupation Team, Bordeaux cedex, France
- Inserm, ISPED, Bordeaux Population Health Research Center Inserm U1219 Injury Epidemiology Transport Occupation Team, Pole of Emergency Medicine, University Hospital of Bordeaux, Bordeaux cedex, France
| | - Emmanuel Lagarde
- Inserm, ISPED, Bordeaux Population Health Research Center Inserm U1219 Injury Epidemiology Transport Occupation Team, Bordeaux cedex, France.
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8
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Zhu W, Zhang M, Pan J, Yao Y, Wang W. Effects of prolonged incubation period and centralized quarantine on the COVID-19 outbreak in Shijiazhuang, China: a modeling study. BMC Med 2021; 19:308. [PMID: 34872559 PMCID: PMC8648499 DOI: 10.1186/s12916-021-02178-z] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/24/2021] [Accepted: 11/02/2021] [Indexed: 01/08/2023] Open
Abstract
BACKGROUND From 2 January to 14 February 2021, a local outbreak of COVID-19 occurred in Shijiazhuang, the capital city of Hebei Province, with a population of 10 million. We analyzed the characteristics of the local outbreak of COVID-19 in Shijiazhuang and evaluated the effects of serial interventions. METHODS Publicly available data, which included age, sex, date of diagnosis, and other patient information, were used to analyze the epidemiological characteristics of the COVID-19 outbreak in Shijiazhuang. The maximum likelihood method and Hamiltonian Monte Carlo method were used to estimate the serial interval and incubation period, respectively. The impact of incubation period and different interventions were simulated using a well-fitted SEIR+q model. RESULTS From 2 January to 14 February 2021, there were 869 patients with symptomatic COVID-19 in Shijiazhuang, and most cases (89.6%) were confirmed before 20 January. Overall, 40.2% of the cases were male, 16.3% were aged 0 to 19 years, and 21.9% were initially diagnosed as asymptomatic but then became symptomatic. The estimated incubation period was 11.6 days (95% CI 10.6, 12.7 days) and the estimated serial interval was 6.6 days (0.025th, 0.975th: 0.6, 20.0 days). The results of the SEIR+q model indicated that a longer incubation period led to a longer epidemic period. If the comprehensive quarantine measures were reduced by 10%, then the nucleic acid testing would need to increase by 20% or more to minimize the cumulative number of cases. CONCLUSIONS Incubation period was longer than serial interval suggested that more secondary transmission may occur before symptoms onset. The long incubation period made it necessary to extend the isolation period to control the outbreak. Timely contact tracing and implementation of a centralized quarantine quickly contained this epidemic in Shijiazhuang. Large-scale nucleic acid testing also helped to identify cases and reduce virus transmission.
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Affiliation(s)
- Wenlong Zhu
- School of Public Health, Shanghai Institute of Infectious Disease and Biosecurity, Fudan University, 138 Yi Xue Yuan Road, Shanghai, 200032, China
| | - Mengxi Zhang
- School of Public Health, Shanghai Institute of Infectious Disease and Biosecurity, Fudan University, 138 Yi Xue Yuan Road, Shanghai, 200032, China
| | - Jinhua Pan
- School of Public Health, Shanghai Institute of Infectious Disease and Biosecurity, Fudan University, 138 Yi Xue Yuan Road, Shanghai, 200032, China
| | - Ye Yao
- School of Public Health, Shanghai Institute of Infectious Disease and Biosecurity, Fudan University, 138 Yi Xue Yuan Road, Shanghai, 200032, China.
| | - Weibing Wang
- School of Public Health, Shanghai Institute of Infectious Disease and Biosecurity, Fudan University, 138 Yi Xue Yuan Road, Shanghai, 200032, China. .,Key Laboratory of Public Health Safety of Ministry of Education, Fudan University, 138 Yi Xue Yuan Road, Shanghai, 200032, China. .,Department of Epidemiology, School of Public Health; Shanghai Institute of Infectious Disease and Biosecurity; Key Laboratory of Public Health Safety (Ministry of Education), Fudan University, 138 Yi Xue Yuan Road, Shanghai, 200032, China.
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9
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Wang X, Dobnikar J, Frenkel D. Effect of social distancing on super-spreading diseases: why pandemics modelling is more challenging than molecular simulation. Mol Phys 2021. [DOI: 10.1080/00268976.2021.1936247] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Affiliation(s)
- Xipeng Wang
- Institute of Physics, Chinese Academy of Sciences, Beijing, People's Republic of China
- Yusuf Hamied Department of Chemistry, University of Cambridge, Cambridge, UK
| | - Jure Dobnikar
- Institute of Physics, Chinese Academy of Sciences, Beijing, People's Republic of China
- Yusuf Hamied Department of Chemistry, University of Cambridge, Cambridge, UK
- Songshan Lake Materials Laboratory, Dongguan, Guangdong, People's Republic of China
- School of Physical Sciences, University of Chinese Academy of Sciences, Beijing, People's Republic of China
| | - Daan Frenkel
- Yusuf Hamied Department of Chemistry, University of Cambridge, Cambridge, UK
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10
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Capon A, Houston J, Rockett R, Sheppeard V, Chaverot S, Arnott A, Parashko T, Ferson M. Risk factors leading to COVID-19 cases in a Sydney restaurant. Aust N Z J Public Health 2021; 45:512-516. [PMID: 34181305 PMCID: PMC8441771 DOI: 10.1111/1753-6405.13135] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/01/2021] [Revised: 04/01/2021] [Accepted: 05/01/2021] [Indexed: 01/12/2023] Open
Abstract
OBJECTIVE To explore the factors associated with the transmission of SARS-CoV-2 to patrons of a restaurant. METHODS A retrospective cohort design was undertaken, with spatial examination and genomic sequencing of cases. The cohort included all patrons who attended the restaurant on Saturday 25 July 2020. A case was identified as a person who tested positive to a validated specific Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) nucleic acid test. Associations were tested using chi-squared analysis of case versus non-case behaviours. RESULTS Twenty cases were epidemiologically linked to exposure at the restaurant on 25 July 2020. All cases dined indoors. All cases able to be genomic sequenced were found to have the same unique mutational profile. Factors tested for an association to the outcome included attentiveness by staff, drink consumption, bathroom use and payment by credit card. No significant results were found. CONCLUSION Indoor dining was identified as a key factor in SARS-CoV-2 transmission, and outdoor dining as a way to limit transmission. Implications for public health: This investigation provides empirical evidence to support public health policies regarding indoor dining.
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Affiliation(s)
- Adam Capon
- South Eastern Sydney Local Health District, South Eastern Sydney Public Health Unit, New South Wales,School of Public Health, Faculty of Medicine and Health, The University of Sydney, New South Wales,Correspondence to: Dr Adam Capon, South Eastern Sydney Local Health District, South Eastern Sydney Public Health Unit, Locked Bag 88 Randwick NSW 2031
| | - Jody Houston
- South Eastern Sydney Local Health District, South Eastern Sydney Public Health Unit, New South Wales
| | - Rebecca Rockett
- Westmead Hospital, Centre for Infectious Diseases and Microbiology, Public Health, New South Wales,Marie Bashir Institute for Infectious Diseases and Biosecurity, The University of Sydney, New South Wales
| | - Vicky Sheppeard
- South Eastern Sydney Local Health District, South Eastern Sydney Public Health Unit, New South Wales,School of Public Health, Faculty of Medicine and Health, The University of Sydney, New South Wales
| | - Sandra Chaverot
- South Eastern Sydney Local Health District, South Eastern Sydney Public Health Unit, New South Wales
| | - Alicia Arnott
- Westmead Hospital, Centre for Infectious Diseases and Microbiology, Public Health, New South Wales,Institute of Clinical Pathology and Medical Research, Centre for Infectious Diseases and Microbiology Laboratory Services, New South Wales
| | - Tiana Parashko
- South Eastern Sydney Local Health District, South Eastern Sydney Public Health Unit, New South Wales
| | - Mark Ferson
- South Eastern Sydney Local Health District, South Eastern Sydney Public Health Unit, New South Wales,School of Public Health and Community Medicine, University of New South Wales
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Wang Z, Whittington J, Yuan HY, Miao H, Tian H, Stenseth NC. Evaluating the effectiveness of control measures in multiple regions during the early phase of the COVID-19 pandemic in 2020. BIOSAFETY AND HEALTH 2021; 3:264-275. [PMID: 34541485 PMCID: PMC8436421 DOI: 10.1016/j.bsheal.2021.09.002] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2021] [Revised: 09/01/2021] [Accepted: 09/06/2021] [Indexed: 01/03/2023] Open
Abstract
The number of COVID-19 confirmed cases rapidly grew since the SARS-CoV-2 virus was identified in late 2019. Due to the high transmissibility of this virus, more countries are experiencing the repeated waves of the COVID-19 pandemic. However, with limited manufacturing and distribution of vaccines, control measures might still be the most critical measures to contain outbreaks worldwide. Therefore, evaluating the effectiveness of various control measures is necessary to inform policymakers and improve future preparedness. In addition, there is an ongoing need to enhance our understanding of the epidemiological parameters and the transmission patterns for a better response to the COVID-19 pandemic. This review focuses on how various models were applied to guide the COVID-19 response by estimating key epidemiologic parameters and evaluating the effectiveness of control measures. We also discuss the insights obtained from the prediction of COVID-19 trajectories under different control measures scenarios.
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Affiliation(s)
- Zengmiao Wang
- State Key Laboratory of Remote Sensing Science, Center for Global Change and Public Health, College of Global Change and Earth System Science, Beijing Normal University, Beijing 100091, China,Corresponding authors: State Key Laboratory of Remote Sensing Science, Center for Global Change and Public Health, College of Global Change and Earth System Science, Beijing Normal University, Beijing 100091, China (Zengmiao Wang); Centre for Ecological and Evolutionary Synthesis (CEES), Department of Biosciences, University of Oslo, Oslo N-0315, Norway (Nils Chr. Stenseth)
| | - Jason Whittington
- Centre for Ecological and Evolutionary Synthesis (CEES), Department of Biosciences, University of Oslo, Oslo N-0315, Norway
| | - Hsiang-Yu Yuan
- Department of Biomedical Sciences, Jockey Club College of Veterinary Medicine and Life Sciences, City University of Hong Kong, Hong Kong 999077, China
| | - Hui Miao
- Department of Statistics, College of Art and Science, Ohio State University, Columbus, OH 43210, USA
| | - Huaiyu Tian
- State Key Laboratory of Remote Sensing Science, Center for Global Change and Public Health, College of Global Change and Earth System Science, Beijing Normal University, Beijing 100091, China
| | - Nils Chr. Stenseth
- Centre for Ecological and Evolutionary Synthesis (CEES), Department of Biosciences, University of Oslo, Oslo N-0315, Norway,Corresponding authors: State Key Laboratory of Remote Sensing Science, Center for Global Change and Public Health, College of Global Change and Earth System Science, Beijing Normal University, Beijing 100091, China (Zengmiao Wang); Centre for Ecological and Evolutionary Synthesis (CEES), Department of Biosciences, University of Oslo, Oslo N-0315, Norway (Nils Chr. Stenseth)
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12
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Cheng C, Zhang D, Dang D, Geng J, Zhu P, Yuan M, Liang R, Yang H, Jin Y, Xie J, Chen S, Duan G. The incubation period of COVID-19: a global meta-analysis of 53 studies and a Chinese observation study of 11 545 patients. Infect Dis Poverty 2021; 10:119. [PMID: 34535192 PMCID: PMC8446477 DOI: 10.1186/s40249-021-00901-9] [Citation(s) in RCA: 34] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2021] [Accepted: 09/02/2021] [Indexed: 12/21/2022] Open
Abstract
BACKGROUND The incubation period is a crucial index of epidemiology in understanding the spread of the emerging Coronavirus disease 2019 (COVID-19). In this study, we aimed to describe the incubation period of COVID-19 globally and in the mainland of China. METHODS The searched studies were published from December 1, 2019 to May 26, 2021 in CNKI, Wanfang, PubMed, and Embase databases. A random-effect model was used to pool the mean incubation period. Meta-regression was used to explore the sources of heterogeneity. Meanwhile, we collected 11 545 patients in the mainland of China outside Hubei from January 19, 2020 to September 21, 2020. The incubation period fitted with the Log-normal model by the coarseDataTools package. RESULTS A total of 3235 articles were searched, 53 of which were included in the meta-analysis. The pooled mean incubation period of COVID-19 was 6.0 days (95% confidence interval [CI] 5.6-6.5) globally, 6.5 days (95% CI 6.1-6.9) in the mainland of China, and 4.6 days (95% CI 4.1-5.1) outside the mainland of China (P = 0.006). The incubation period varied with age (P = 0.005). Meanwhile, in 11 545 patients, the mean incubation period was 7.1 days (95% CI 7.0-7.2), which was similar to the finding in our meta-analysis. CONCLUSIONS For COVID-19, the mean incubation period was 6.0 days globally but near 7.0 days in the mainland of China, which will help identify the time of infection and make disease control decisions. Furthermore, attention should also be paid to the region- or age-specific incubation period.
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Affiliation(s)
- Cheng Cheng
- Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, No. 100 Kexue Avenue, Zhengzhou, 450001, Henan, People's Republic of China
| | - DongDong Zhang
- Department of Nutrition and Food Hygiene, College of Public Health, Zhengzhou University, No. 100 Kexue Avenue, Zhengzhou, 450001, Henan, People's Republic of China
| | - Dejian Dang
- Infection Prevention and Control Department, The Fifth Affiliated Hospital of Zhengzhou University, No.3 Kangfuqian Street, Zhengzhou, 450052, Henan, People's Republic of China
| | - Juan Geng
- Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, No. 100 Kexue Avenue, Zhengzhou, 450001, Henan, People's Republic of China
| | - Peiyu Zhu
- Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, No. 100 Kexue Avenue, Zhengzhou, 450001, Henan, People's Republic of China
| | - Mingzhu Yuan
- Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, No. 100 Kexue Avenue, Zhengzhou, 450001, Henan, People's Republic of China
| | - Ruonan Liang
- Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, No. 100 Kexue Avenue, Zhengzhou, 450001, Henan, People's Republic of China
| | - Haiyan Yang
- Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, No. 100 Kexue Avenue, Zhengzhou, 450001, Henan, People's Republic of China
| | - Yuefei Jin
- Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, No. 100 Kexue Avenue, Zhengzhou, 450001, Henan, People's Republic of China
| | - Jing Xie
- Henan Key Laboratory of Molecular Medicine, Zhengzhou University, No. 100 Kexue Avenue, Zhengzhou, 450001, Henan, People's Republic of China
- Centre for Biostatistics and Clinical Trials (BaCT), Peter MacCallum Cancer Centre, No. 305 Grattan Street, Melbourne, 3000, Victoria, Australia
| | - Shuaiyin Chen
- Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, No. 100 Kexue Avenue, Zhengzhou, 450001, Henan, People's Republic of China.
| | - Guangcai Duan
- Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, No. 100 Kexue Avenue, Zhengzhou, 450001, Henan, People's Republic of China.
- Henan Key Laboratory of Molecular Medicine, Zhengzhou University, No. 100 Kexue Avenue, Zhengzhou, 450001, Henan, People's Republic of China.
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The Crazy-Paving Pattern in Chest CT Imaging of COVID-19 Patients: An Alarming Sign for Hospitalization. IRANIAN JOURNAL OF RADIOLOGY 2021. [DOI: 10.5812/iranjradiol.113286] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
Background: The outbreak of coronavirus disease 2019 (COVID-19) has become a major threat to all humans. Objectives: To assess the association between the patients’ clinical and laboratory records, computed tomography (CT) findings, and epidemiological features of COVID-19 with the severity of the disease. Patients and Methods: In this retrospective case-control study conducted on the medical records of confirmed COVID-19 pneumonia patients on admission, we investigated the CT manifestations and clinical and laboratory risk factors for progression to severe COVID-19 pneumonia. The medical records and radiological CT features of confirmed COVID-19 patients were reviewed in one public hospital and one respiratory clinic in Qom, Iran, from August 1 to September 30, 2020. Results: Of 236 confirmed COVID-19 cases, 62 were infected with moderate to severe COVID-19 and required hospital admission, and 174 were followed-up on an outpatient basis. A significant difference was found in the mean age of the outpatient and hospitalized groups. The incidence of bilateral lung involvement, consolidations, linear opacities, crazy-paving pattern, air bronchogram, and number of lobes involved were significantly higher in the hospitalized group compared to the outpatient group. However, the crazy-paving pattern was only significantly associated with an oxygen saturation (SpO2) level < 90% and, coughing. Our findings indicated that the crazy-paving pattern was significantly associated with the inflammatory phase. The presence of this pattern on admission, SpO2 < 90%, older age, and diabetes were independent risk factors for progression to severe COVID-19. Conclusion: The crazy-paving pattern can predict the severity of COVID-19, which is of great importance in the management and follow-up of COVID-19 pneumonia patients. Clinical factors, such as aging, male gender, and diabetes, may be risk factors for the crazy-paving pattern. Severe cough is the most important clinical sign related to this pattern, along with an SpO2 < 90%, which is an important sign of COVID-19 severity.
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Monárrez-Espino J, Zubía-Nevárez CI, Reyes-Silva L, Castillo-Palencia JP, Castañeda-Delgado JE, Herrera van-Oostdam AS, López-Hernández Y. Clinical Factors Associated with COVID-19 Severity in Mexican Patients: Cross-Sectional Analysis from a Multicentric Hospital Study. Healthcare (Basel) 2021; 9:healthcare9070895. [PMID: 34356272 PMCID: PMC8307927 DOI: 10.3390/healthcare9070895] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2021] [Revised: 06/16/2021] [Accepted: 06/23/2021] [Indexed: 12/20/2022] Open
Abstract
(1) Background: Latin America has been harshly hit by SARS-CoV-2, but reporting from this region is still incomplete. This study aimed at identifying and comparing clinical characteristics of patients with COVID-19 at different stages of disease severity. (2) Methods: Cross-sectional multicentric study. Individuals with nasopharyngeal PCR were categorized into four groups: (1) negative, (2) positive, not hospitalized, (3) positive, hospitalized with/without supplementary oxygen, and (4) positive, intubated. Clinical and laboratory data were compared, using group 1 as the reference. Multivariate multinomial logistic regression was used to compare adjusted odds ratios. (3) Results: Nine variables remained in the model, explaining 76% of the variability. Men had increased odds, from 1.90 (95%CI 0.87–4.15) in the comparison of 2 vs. 1, to 3.66 (1.12–11.9) in 4 vs. 1. Diabetes and obesity were strong predictors. For diabetes, the odds for groups 2, 3, and 4 were 1.56 (0.29–8.16), 12.8 (2.50–65.8), and 16.1 (2.87–90.2); for obesity, these were 0.79 (0.31–2.05), 3.38 (1.04–10.9), and 4.10 (1.16–14.4), respectively. Fever, myalgia/arthralgia, cough, dyspnea, and neutrophilia were associated with the more severe COVID-19 group. Anosmia/dysgeusia were more likely to occur in group 2 (25.5; 2.51–259). (4) Conclusion: The results point to relevant differences in clinical and laboratory features of COVID-19 by level of severity that can be used in medical practice.
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Affiliation(s)
- Joel Monárrez-Espino
- Department of Health Research, Christus Muguerza del Parque Hospital, Chihuahua 31000, Mexico;
- Vice Presidency of Health Sciences, Medical Specialties Program, University of Monterrey, San Pedro Garza García 66238, Mexico;
- Correspondence: (J.M.-E.); (Y.L.-H.); Tel.: +52-614-4397-932 (J.M.-E.)
| | - Carolina Ivette Zubía-Nevárez
- Vice Presidency of Health Sciences, Medical Specialties Program, University of Monterrey, San Pedro Garza García 66238, Mexico;
| | - Lorena Reyes-Silva
- Department of Health Research, Christus Muguerza del Parque Hospital, Chihuahua 31000, Mexico;
| | - Juan Pablo Castillo-Palencia
- General Hospital of Soledad de Graciano Sánchez, San Luis Potosí Health Services, Soledad de Graciano Sánchez 78435, Mexico;
| | | | | | - Yamilé López-Hernández
- Metabolomics and Proteomics Laboratory, Mexican Council of Science and Technology, Zacatecas Autonomous University, Zacatecas 98000, Mexico
- Correspondence: (J.M.-E.); (Y.L.-H.); Tel.: +52-614-4397-932 (J.M.-E.)
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Ali ST, Yeung A, Shan S, Wang L, Gao H, Du Z, Xu XK, Wu P, Lau EHY, Cowling BJ. Serial intervals and case isolation delays for COVID-19: a systematic review and meta-analysis. Clin Infect Dis 2021; 74:685-694. [PMID: 34037748 PMCID: PMC8241473 DOI: 10.1093/cid/ciab491] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2021] [Indexed: 01/19/2023] Open
Abstract
Background Estimates of the serial interval distribution contribute to our understanding
of the transmission dynamics of coronavirus disease 2019 (COVID-19). Here,
we aimed to summarize the existing evidence on serial interval distributions
and delays in case isolation for COVID-19. Methods We conducted a systematic review of the published literature and preprints in
PubMed on two epidemiological parameters namely serial intervals and delay
intervals relating to isolation of cases for COVID-19 until 22 October, 2020
following predefined eligibility criteria. We assessed the variation in
these parameter estimates by correlation and regression analysis. Results Of 103 unique studies identified on serial intervals of COVID-19, 56 were
included providing 129 estimates and of 451 unique studies on isolation
delays, 18 studies were included providing 74 estimates. Serial interval
estimates varied from 1.0 to 9.9 days, while case isolation delays varied
from 1.0 to 12.5 days which were associated with spatial, methodological and
temporal factors. In mainland China, the pooled mean serial interval was 6.2
(range, 5.1-7.8) days before the epidemic peak and reduced to 4.9 (range,
1.9-6.5) days after the epidemic peak. Similarly, the pooled mean isolation
delay related intervals were 6.0 (range, 2.9-12.5) days and 2.4 (range,
2.0-2.7) days before and after the epidemic peak, respectively. There was a
positive association between serial interval and case isolation delay. Conclusions Temporal factors, such as different control measures and case isolation in
particular led to shorter serial interval estimates over time. Correcting
transmissibility estimates for these time-varying distributions could aid
mitigation efforts.
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Affiliation(s)
- Sheikh Taslim Ali
- WHO Collaborating Centre for Infectious Disease
Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of
Medicine, The University of Hong Kong, Hong Kong Special
Administrative Region, China
- Laboratory of Data Discovery for Health, Hong Kong Science
and Technology Park, Hong Kong Special Administrative Region,
China
| | - Amy Yeung
- WHO Collaborating Centre for Infectious Disease
Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of
Medicine, The University of Hong Kong, Hong Kong Special
Administrative Region, China
| | - Songwei Shan
- WHO Collaborating Centre for Infectious Disease
Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of
Medicine, The University of Hong Kong, Hong Kong Special
Administrative Region, China
- Laboratory of Data Discovery for Health, Hong Kong Science
and Technology Park, Hong Kong Special Administrative Region,
China
| | - Lin Wang
- Department of Genetics, University of
Cambridge, Cambridge CB2 3EH, UK
| | - Huizhi Gao
- WHO Collaborating Centre for Infectious Disease
Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of
Medicine, The University of Hong Kong, Hong Kong Special
Administrative Region, China
| | - Zhanwei Du
- WHO Collaborating Centre for Infectious Disease
Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of
Medicine, The University of Hong Kong, Hong Kong Special
Administrative Region, China
- Laboratory of Data Discovery for Health, Hong Kong Science
and Technology Park, Hong Kong Special Administrative Region,
China
| | - Xiao-Ke Xu
- College of Information and Communication Engineering,
Dalian Minzu University, Dalian 116600, China
| | - Peng Wu
- WHO Collaborating Centre for Infectious Disease
Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of
Medicine, The University of Hong Kong, Hong Kong Special
Administrative Region, China
- Laboratory of Data Discovery for Health, Hong Kong Science
and Technology Park, Hong Kong Special Administrative Region,
China
| | - Eric H Y Lau
- WHO Collaborating Centre for Infectious Disease
Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of
Medicine, The University of Hong Kong, Hong Kong Special
Administrative Region, China
- Laboratory of Data Discovery for Health, Hong Kong Science
and Technology Park, Hong Kong Special Administrative Region,
China
| | - Benjamin J Cowling
- WHO Collaborating Centre for Infectious Disease
Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of
Medicine, The University of Hong Kong, Hong Kong Special
Administrative Region, China
- Laboratory of Data Discovery for Health, Hong Kong Science
and Technology Park, Hong Kong Special Administrative Region,
China
- Corresponding author: Prof. Benjamin J Cowling, School of Public
Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, 7 Sassoon
Road, Pokfulam, Hong Kong. Tel: +852 3917 6711; Fax: +852 3520 1945;
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Lu QB, Zhang HY, Che TL, Zhao H, Chen X, Li R, Jiang WL, Zeng HL, Zhang XA, Long H, Wang Q, Wu MQ, Ward MP, Chen Y, Zhang ZJ, Yang Y, Fang LQ, Liu W. The differential demographic pattern of coronavirus disease 2019 fatality outside Hubei and from six hospitals in Hubei, China: a descriptive analysis. BMC Infect Dis 2021; 21:481. [PMID: 34039295 PMCID: PMC8153527 DOI: 10.1186/s12879-021-06187-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2020] [Accepted: 05/14/2021] [Indexed: 01/08/2023] Open
Abstract
BACKGROUND The coronavirus disease 2019 (COVID-19) epidemic has been largely controlled in China, to the point where case fatality rate (CFR) data can be comprehensively evaluated. METHODS Data on confirmed patients, with a final outcome reported as of 29 March 2020, were obtained from official websites and other internet sources. The hospitalized CFR (HCFR) was estimated, epidemiological features described, and risk factors for a fatal outcome identified. RESULTS The overall HCFR in China was estimated to be 4.6% (95% CI 4.5-4.8%, P < 0.001). It increased with age and was higher in males than females. Although the highest HCFR observed was in male patients ≥70 years old, the relative risks for death outcome by sex varied across age groups, and the greatest HCFR risk ratio for males vs. females was shown in the age group of 50-60 years, higher than age groups of 60-70 and ≥ 70 years. Differential age/sex HCFR patterns across geographical regions were found: the age effect on HCFR was greater in other provinces outside Hubei than in Wuhan. An effect of longer interval from symptom onset to admission was only observed outside Hubei, not in Wuhan. By performing multivariate analysis and survival analysis, the higher HCFR was associated with older age (both P < 0.001), and male sex (both P < 0.001). Only in regions outside Hubei, longer interval from symptom onset to admission, were associated with higher HCFR. CONCLUSIONS This up-to-date and comprehensive picture of COVID-19 HCFR and its drivers will help healthcare givers target limited medical resources to patients with high risk of fatality.
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Affiliation(s)
- Qing-Bin Lu
- Department of Laboratorial Science and Technology, School of Public Health, Peking University, Beijing, 100191, P. R. China
| | - Hai-Yang Zhang
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing, 100071, P. R. China
| | - Tian-Le Che
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing, 100071, P. R. China
| | - Han Zhao
- School of Mathematical Sciences, Beijing Normal University, Beijing, 100875, P.R. China
| | - Xi Chen
- Department of Thoracic and Vascular Surgery, Wuhan No. 1 Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, P. R. China
| | - Rui Li
- Department of Healthcare Management, School of Health Sciences, Wuhan University, Wuhan, 430071, P. R. China
- Global Health Institute, Wuhan University, Wuhan, 430072, P. R. China
| | - Wan-Li Jiang
- Department of Thoracic Surgery, Renmin Hospital of Wuhan University, Wuhan, 430060, P. R. China
| | - Hao-Long Zeng
- Department of Laboratory Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China
| | - Xiao-Ai Zhang
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing, 100071, P. R. China
| | - Hui Long
- Tianyou Hospital affiliated to Wuhan University of Science and Technology, Wuhan, 430064, P. R. China
| | - Qiang Wang
- Institute of Infection, Immunology and Tumor Microenvironent, Hubei Province Key Laboratory of Occupational Hazard Identification and Control, Medical College, Wuhan University of Science and Technology, Wuhan, 430065, P. R. China
| | - Ming-Qing Wu
- Tianyou Hospital affiliated to Wuhan University of Science and Technology, Wuhan, 430064, P. R. China
- Institute of Infection, Immunology and Tumor Microenvironent, Hubei Province Key Laboratory of Occupational Hazard Identification and Control, Medical College, Wuhan University of Science and Technology, Wuhan, 430065, P. R. China
| | - Michael P Ward
- Sydney School of Veterinary Science, The University of Sydney, Camden, NSW, Australia
| | - Yue Chen
- Department of Epidemiology and Community Medicine, Faculty of Medicine, University of Ottawa, 451 Smyth Rd, Ottawa, Ontario, Canada
| | - Zhi-Jie Zhang
- Department of Epidemiology and Health Statistics, School of Public Health, Fudan University, Shanghai, P. R. China.
| | - Yang Yang
- Department of Biostatistics, College of Public Health and Health Professions, and Emerging Pathogens Institute, University of Florida, Gainesville, Florida, USA.
| | - Li-Qun Fang
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing, 100071, P. R. China.
| | - Wei Liu
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing, 100071, P. R. China.
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17
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Cimolai N. In pursuit of the right tail for the COVID-19 incubation period. Public Health 2021; 194:149-155. [PMID: 33915459 PMCID: PMC7997403 DOI: 10.1016/j.puhe.2021.03.011] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2020] [Revised: 02/24/2021] [Accepted: 03/09/2021] [Indexed: 01/08/2023]
Abstract
Definition of the incubation period for COVID-19 is critical for implementing quarantine and thus infection control. Whereas the classical definition relies on the time from exposure to time of first symptoms, a more practical working definition is the time from exposure to time of first live virus excretion. For COVID-19, average incubation period times commonly span 5-7 days which are generally longer than for most typical other respiratory viruses. There is considerable variability reported however for the late right-hand statistical distribution. A small but yet epidemiologically important subset of patients may have the late end of the incubation period extend beyond the 14 days that is frequently assumed. Conservative assumptions of the right tail end distribution favor safety, but pragmatic working modifications may be required to accommodate high rates of infection and/or healthcare worker exposures. Despite the advent of effective vaccines, further attention and study in these regards are warranted. It is predictable that vaccine application will be associated with continued confusion over protection and its longevity. Measures for the application of infectivity will continue to be extremely relevant.
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Affiliation(s)
- Nevio Cimolai
- Faculty of Medicine, The University of British Columbia, Canada; Children's and Women's Health Centre of British Columbia, 4480 Oak Street, Vancouver, B.C, V6H3V4, Canada.
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18
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A Simulation Model for Forecasting COVID-19 Pandemic Spread: Analytical Results Based on the Current Saudi COVID-19 Data. SUSTAINABILITY 2021. [DOI: 10.3390/su13094888] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
The coronavirus pandemic (COVID-19) spreads worldwide during the first half of 2020. As is the case for all countries, the Kingdom of Saudi Arabia (KSA), where the number of reported cases reached more than 392 K in the first week of April 2021, was heavily affected by this pandemic. In this study, we introduce a new simulation model to examine the pandemic evolution in two major cities in KSA, namely, Riyadh (the capital city) and Jeddah (the second-largest city). Consequently, this study estimates and predicts the number of cases infected with COVID-19 in the upcoming months. The major advantage of this model is that it is based on real data for KSA, which makes it more realistic. Furthermore, this paper examines the parameters used to understand better and more accurately predict the shape of the infection curve, particularly in KSA. The obtained results show the importance of several parameters in reducing the pandemic spread: the infection rate, the social distance, and the walking distance of individuals. Through this work, we try to raise the awareness of the public and officials about the seriousness of future pandemic waves. In addition, we analyze the current data of the infected cases in KSA using a novel Gaussian curve fitting method. The results show that the expected pandemic curve is flattening, which is recorded in real data of infection. We also propose a new method to predict the new cases. The experimental results on KSA’s updated cases reveal that the proposed method outperforms some current prediction techniques, and therefore, it is more efficient in fighting possible future pandemics.
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19
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Li ZY, Zhang Y, Peng LQ, Gao RR, Jing JR, Wang JL, Ren BZ, Xu JG, Wang T. Demand for longer quarantine period among common and uncommon COVID-19 infections: a scoping review. Infect Dis Poverty 2021; 10:56. [PMID: 33902695 PMCID: PMC8072089 DOI: 10.1186/s40249-021-00847-y] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2021] [Accepted: 04/20/2021] [Indexed: 01/24/2023] Open
Abstract
BACKGROUND As one of the non-pharmacological interventions to control the transmission of COVID-19, determining the quarantine duration is mainly based on the accurate estimates of the incubation period. However, patients with coarse information of the exposure date, as well as infections other than the symptomatic, were not taken into account in previously published studies. Thus, by using the statistical method dealing with the interval-censored data, we assessed the quarantine duration for both common and uncommon infections. The latter type includes the presymptomatic, the asymptomatic and the recurrent test positive patients. METHODS As of 10 December 2020, information on cases have been collected from the English and Chinese databases, including Pubmed, Google scholar, CNKI (China National Knowledge Infrastructure) and Wanfang. Official websites and medias were also searched as data sources. All data were transformed into doubly interval-censored and the accelerated failure time model was applied. By estimating the incubation period and the time-to-event distribution of worldwide COVID-19 patients, we obtain the large percentiles for determining and suggesting the quarantine policies. For symptomatic and presymptomatic COVID-19 patients, the incubation time is the duration from exposure to symptom onset. For the asymptomatic, we substitute the date of first positive result of nucleic acid testing for that of symptom onset. Furthermore, the time from hospital discharge or getting negative test result to the positive recurrence has been calculated for recurrent positive patients. RESULTS A total of 1920 laboratory confirmed COVID-19 cases were included. Among all uncommon infections, 34.1% (n = 55) of them developed symptoms or were identified beyond fourteen days. Based on all collected cases, the 95th and 99th percentiles were estimated to be 16.2 days (95% CI 15.5-17.0) and 22.9 days (21.7‒24.3) respectively. Besides, we got similar estimates based on merely symptomatic and presymptomatic infections as 15.1 days (14.4‒15.7) and 21.1 days (20.0‒22.2). CONCLUSIONS There are a certain number of infected people who require longer quarantine duration. Our findings well support the current practice of the extended active monitoring. To further prevent possible transmissions induced and facilitated by such infectious outliers after the 14-days quarantine, properly prolonging the quarantine duration could be prudent for high-risk scenarios and in regions with insufficient test resources.
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Affiliation(s)
- Zhi-Yao Li
- Department of Health Statistics and Epidemiology, School of Public Health, Collaborative Innovation Center of Reverse Microbial Etiology, Shanxi Medical University, 56 Xinjiannanlu Street, Yingze District, Taiyuan, 030001, Shanxi, People's Republic of China
| | - Yu Zhang
- Department of Health Statistics and Epidemiology, School of Public Health, Collaborative Innovation Center of Reverse Microbial Etiology, Shanxi Medical University, 56 Xinjiannanlu Street, Yingze District, Taiyuan, 030001, Shanxi, People's Republic of China
| | - Liu-Qing Peng
- Department of Health Statistics and Epidemiology, School of Public Health, Collaborative Innovation Center of Reverse Microbial Etiology, Shanxi Medical University, 56 Xinjiannanlu Street, Yingze District, Taiyuan, 030001, Shanxi, People's Republic of China
| | - Rong-Rong Gao
- Department of Health Statistics and Epidemiology, School of Public Health, Collaborative Innovation Center of Reverse Microbial Etiology, Shanxi Medical University, 56 Xinjiannanlu Street, Yingze District, Taiyuan, 030001, Shanxi, People's Republic of China
| | - Jia-Rui Jing
- Department of Health Statistics and Epidemiology, School of Public Health, Collaborative Innovation Center of Reverse Microbial Etiology, Shanxi Medical University, 56 Xinjiannanlu Street, Yingze District, Taiyuan, 030001, Shanxi, People's Republic of China
| | - Jia-Le Wang
- Department of Health Statistics and Epidemiology, School of Public Health, Collaborative Innovation Center of Reverse Microbial Etiology, Shanxi Medical University, 56 Xinjiannanlu Street, Yingze District, Taiyuan, 030001, Shanxi, People's Republic of China
| | - Bin-Zhi Ren
- Shanxi Provincial Center for Disease Control and Prevention, Taiyuan, 030001, People's Republic of China
- Shanxi Provincial Key Laboratory of Major Infectious Disease Pandemic Response, Taiyuan, 030001, People's Republic of China
| | - Jian-Guo Xu
- State Key Laboratory of Infectious Disease Prevention and Control, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, 102206, People's Republic of China
- Institute of Public Health, Nankai University, Tianjing, 300350, People's Republic of China
| | - Tong Wang
- Department of Health Statistics and Epidemiology, School of Public Health, Collaborative Innovation Center of Reverse Microbial Etiology, Shanxi Medical University, 56 Xinjiannanlu Street, Yingze District, Taiyuan, 030001, Shanxi, People's Republic of China.
- Shanxi Provincial Key Laboratory of Major Infectious Disease Pandemic Response, Taiyuan, 030001, People's Republic of China.
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