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Bitsouni V, Gialelis N, Tsilidis V. A novel comparison framework for epidemiological strategies applied to age-based restrictions versus horizontal lockdowns. Infect Dis Model 2024; 9:1301-1328. [PMID: 39309400 PMCID: PMC11415861 DOI: 10.1016/j.idm.2024.07.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2024] [Revised: 06/20/2024] [Accepted: 07/16/2024] [Indexed: 09/25/2024] Open
Abstract
During an epidemic, such as the COVID-19 pandemic, policy-makers are faced with the decision of implementing effective, yet socioeconomically costly intervention strategies, such as school and workplace closure, physical distancing, etc. In this study, we propose a rigorous definition of epidemiological strategies. In addition, we develop a scheme for comparing certain epidemiological strategies, with the goal of providing policy-makers with a tool for their systematic comparison. Then, we put the suggested scheme to the test by employing an age-based epidemiological compartment model introduced in Bitsouni et al. (2024), coupled with data from the literature, in order to compare the effectiveness of age-based and horizontal interventions. In general, our findings suggest that these two are comparable, mainly at a low or medium level of intensity.
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Affiliation(s)
- Vasiliki Bitsouni
- Department of Mathematics, University of Patras, GR-26504, Rio Patras, Greece
| | - Nikolaos Gialelis
- Department of Mathematics, National and Kapodistrian University of Athens, GR-15784, Athens, Greece
- School of Medicine, National and Kapodistrian University of Athens, GR-11527, Athens, Greece
| | - Vasilis Tsilidis
- Department of Mathematics, University of Patras, GR-26504, Rio Patras, Greece
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2
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Martín-Sánchez M, Wu P, Adam DC, Yang B, Lim WW, Lin Y, Lau EH, Sullivan SG, Leung GM, Cowling BJ. An observational study on imported COVID-19 cases in Hong Kong during mandatory on-arrival hotel quarantine. PUBLIC HEALTH IN PRACTICE 2024; 8:100525. [PMID: 39050010 PMCID: PMC11267049 DOI: 10.1016/j.puhip.2024.100525] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2023] [Revised: 05/23/2024] [Accepted: 06/05/2024] [Indexed: 07/27/2024] Open
Abstract
Background Hong Kong enforced stringent travel restrictions during the COVID-19 pandemic. Understanding the characteristics of imported COVID-19 cases is important for establishing evidence-based control measures. Methods Retrospective cohort study summarising the characteristics of imported cases detected in Hong Kong between 13 November 2020 and 31 January 2022, when compulsory quarantine was implemented. Findings A total of 2269 imported COVID-19 cases aged 0-85 years were identified, of which 48.6 % detected on arrival. A shorter median delay from arrival to isolation was observed in Delta and Omicron cases (3 days) than in ancestral strain and other variants cases (12 days; p < 0.001). Lower Ct values at isolation were observed in Omicron cases than in ancestral strain or other variants cases. No Omicron cases were detected beyond 14 days after arrival. Cases detected after 14 days of quarantine (n=58, 2.6 %) were more likely asymptomatic at isolation and had higher Ct value during isolation, some of them indicating re-positivity or post-arrival infections. Conclusions Testing inbound travellers at arrival and during quarantine can detect imported cases early, but may not prevent all COVID-19 introductions into the community. Public health measures should be adapted in response to the emergence of SARS-CoV-2 variants based on evidence from ongoing surveillance.
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Affiliation(s)
- Mario Martín-Sánchez
- 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
| | - 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 Limited (D4H), Hong Kong Science and Technology Park, Hong Kong Special Administrative Region, China
| | - Dillon C. Adam
- 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
| | - Bingyi Yang
- 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
| | - Wey Wen Lim
- 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
| | - Yun Lin
- 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
| | - 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 Limited (D4H), Hong Kong Science and Technology Park, Hong Kong Special Administrative Region, China
| | - Sheena G. Sullivan
- WHO Collaborating Centre for Reference and Research on Influenza, Royal Melbourne Hospital, and Department of Infectious Diseases, University of Melbourne, at the Peter Doherty Institute for Infection and Immunity, Melbourne, Australia
- Department of Epidemiology, University of California, Los Angeles, USA
| | - Gabriel M. Leung
- 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 Limited (D4H), 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 Limited (D4H), Hong Kong Science and Technology Park, Hong Kong Special Administrative Region, China
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3
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Thippornchai N, Pengpanich S, Jaroenram W, Kosoltanapiwat N, Sukphopetch P, Kiatpathomchai W, Leaungwutiwong P. A colorimetric reverse-transcription loop-mediated isothermal amplification method targeting the L452R mutation to detect the Delta variant of SARS-CoV-2. Sci Rep 2024; 14:21961. [PMID: 39304686 DOI: 10.1038/s41598-024-72417-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2024] [Accepted: 09/06/2024] [Indexed: 09/22/2024] Open
Abstract
The rapid spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has triggered global difficulties for both individuals and economies, with new variants continuing to emerge. The Delta variant of SARS-CoV-2 remains most prevalent worldwide, and it affects the efficacy of coronavirus disease 2019 (COVID-19) vaccination. Expedited testing to detect the Delta variant of SARS-CoV-2 and monitor viral transmission is necessary. This study aimed to develop and evaluate a colorimetric reverse-transcription loop-mediated isothermal amplification (RT-LAMP) technique targeting the L452R mutation in the S gene for the specific detection of the Delta variant. In the test, positivity was indicated as a color change from purple to yellow. The assay's 95% limit of detection was 57 copies per reaction for the L452R (U1355G)-specific standard plasmid. Using 126 clinical samples, our assay displayed 100% specificity, 97.06% sensitivity, and 98.41% accuracy in identifying the Delta variant of SARS-CoV-2 compared to real-time RT-PCR. To our knowledge, this is the first colorimetric RT-LAMP assay that can differentiate the Delta variant from its generic SARS-CoV-2, enabling it as an approach for studying COVID-19 demography and facilitating proper effective control measure establishment to fight against the reemerging variants of SARS-CoV-2 in the future.
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Affiliation(s)
- Narin Thippornchai
- Department of Microbiology and Immunology, Faculty of Tropical Medicine, Mahidol University, Bangkok, 10400, Thailand
| | - Sukanya Pengpanich
- National Center for Genetic Engineering and Biotechnology (BIOTEC), National Science and Technology Development Agency (NSTDA), 111 Thailand Science Park, Phahonyothin Rd., Klong Neung, Klong Luang, Pathum Thani, 12120, Thailand
| | - Wansadaj Jaroenram
- National Center for Genetic Engineering and Biotechnology (BIOTEC), National Science and Technology Development Agency (NSTDA), 111 Thailand Science Park, Phahonyothin Rd., Klong Neung, Klong Luang, Pathum Thani, 12120, Thailand
| | - Nathamon Kosoltanapiwat
- Department of Microbiology and Immunology, Faculty of Tropical Medicine, Mahidol University, Bangkok, 10400, Thailand
| | - Passanesh Sukphopetch
- Department of Microbiology and Immunology, Faculty of Tropical Medicine, Mahidol University, Bangkok, 10400, Thailand
| | - Wansika Kiatpathomchai
- National Center for Genetic Engineering and Biotechnology (BIOTEC), National Science and Technology Development Agency (NSTDA), 111 Thailand Science Park, Phahonyothin Rd., Klong Neung, Klong Luang, Pathum Thani, 12120, Thailand.
| | - Pornsawan Leaungwutiwong
- Department of Microbiology and Immunology, Faculty of Tropical Medicine, Mahidol University, Bangkok, 10400, Thailand.
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Liu M, Ma R, Cao X, Zhang H, Zhou S, Jiang W, Jiang Y, Sun J, Yang Q, Li X, Sun Y, Shi L, Wang M, Song X, Chen F, Zhang X, Wei H, Yu S, Zhu D, Ba L, Cao Z, Xiao X, Wei X, Lin Z, Chen F, Shan C, Wang G, Ye J, Qu S, Zhao C, Wang Z, Li H, Liu F, Cui X, Ye S, Liu Z, Xu Y, Cai X, Huang W, Zhang R, Zhao Y, Yu G, Shi G, Lu M, Shen Y, Zhao Y, Pei J, Xie S, Yu L, Liu Y, Gu S, Yang Y, Cheng L, liu J. Incidence and prognosis of olfactory and gustatory dysfunctions related to SARS-CoV-2 Omicron strain infection in China: A national multicenter survey of 35,566 individuals. World J Otorhinolaryngol Head Neck Surg 2024; 10:113-120. [PMID: 38855290 PMCID: PMC11156687 DOI: 10.1002/wjo2.167] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2023] [Revised: 10/27/2023] [Accepted: 02/15/2024] [Indexed: 06/11/2024] Open
Abstract
Objective This cross-sectional study aimed to determine the epidemiology of olfactory and gustatory dysfunctions related to COVID-19 in China. Methods This study was conducted by 45 tertiary Grade-A hospitals in China. Online and offline questionnaire data were obtained from patients infected with COVID-19 between December 28, 2022, and February 21, 2023. The collected information included basic demographics, medical history, smoking and drinking history, vaccination history, changes in olfactory and gustatory functions before and after infection, and other postinfection symptoms, as well as the duration and improvement status of olfactory and gustatory disorders. Results Complete questionnaires were obtained from 35,566 subjects. The overall incidence of olfactory and taste dysfunction was 67.75%. Being female or being a cigarette smoker increased the likelihood of developing olfactory and taste dysfunction. Having received four doses of the vaccine or having good oral health or being a alcohol drinker decreased the risk of such dysfunction. Before infection, the average olfactory and taste VAS scores were 8.41 and 8.51, respectively; after infection, they decreased to 3.69 and 4.29 and recovered to 5.83 and 6.55 by the time of the survey. The median duration of dysosmia and dysgeusia was 15 and 12 days, respectively, with 0.5% of patients having symptoms lasting for more than 28 days. The overall self-reported improvement rate was 59.16%. Recovery was higher in males, never smokers, those who received two or three vaccine doses, and those that had never experienced dental health issues, or chronic accompanying symptoms. Conclusions The incidence of dysosmia and dysgeusia following infection with the SARS-CoV-2 virus is high in China. Incidence and prognosis are influenced by several factors, including sex, SARS-CoV-2 vaccination, history of head-facial trauma, nasal and oral health status, smoking and drinking history, and the persistence of accompanying symptoms.
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Affiliation(s)
- Meng‐Fan Liu
- Graduate School of Beijing University of Chinese MedicineBeijingChina
- Department of Otorhinolaryngology Head and Neck SurgeryChina‐Japan Friendship HospitalBeijingChina
| | - Rui‐Xia Ma
- Department of Otorhinolaryngology Head and Neck SurgeryThe First People′s Hospital of YinchuanYinchuanChina
| | - Xian‐Bao Cao
- Department of OtorhinolaryngologyThe First People′s Hospital of Yunnan ProvinceKunmingChina
| | - Hua Zhang
- Department of OtorhinolaryngologyThe First Affiliated Hospital of Xinjiang Medical UniversityUrumqiChina
| | - Shui‐Hong Zhou
- Department of Otorhinolaryngology Head and Neck SurgeryThe First Affiliated Hospital of Zhejiang University School of MedicineHangzhouChina
| | - Wei‐Hong Jiang
- Department of Otorhinolaryngology Head and Neck SurgeryXiangya Hospital Central South UniversityChangshaChina
| | - Yan Jiang
- Department of Otorhinolaryngology Head and Neck SurgeryThe Affiliated Hospital of Qingdao UniversityQingdaoChina
| | - Jing‐Wu Sun
- Department of Otorhinolaryngology Head and Neck SurgeryThe First Affiliated Hospital of USTCHefeiChina
| | - Qin‐Tai Yang
- Department of Otorhinolaryngology Head and Neck SurgeryThe Third Affiliated Hospital of Sun Yat‐sen UniversityGuangzhouChina
| | - Xue‐Zhong Li
- Department of Otorhinolaryngology Head and Neck SurgeryQilu Hospital of Shandong UniversityJinanChina
| | - Ya‐Nan Sun
- Department of Otorhinolaryngology Head and Neck SurgeryThe Second Affiliated Hospital of Harbin Medical UniversityHarbinChina
| | - Li Shi
- Department of Otolaryngology, The Second Hospital of Shandong UniversityShandong UniversityJinanChina
| | - Min Wang
- Department of Otorhinolaryngology Head and Neck SurgeryPeking University People′s HospitalBeijingChina
| | - Xi‐Cheng Song
- Department of Otorhinolaryngology Head and Neck Surgery, Yantai Yuhuangding HospitalQingdao UniversityYantaiChina
| | - Fu‐Quan Chen
- Department of Otorhinolaryngology Head and Neck Surgery, Xijing HospitalThe Fourth Military Medical UniversityXi′anChina
| | - Xiao‐Shu Zhang
- Gansu Provincial Center for Disease Control and PreventionLanzhouChina
| | - Hong‐Quan Wei
- Department of Otorhinolaryngology Head and Neck SurgeryThe First Affiliated Hospital of China Medical UniversityShenyangChina
| | - Shao‐Qing Yu
- Department of Otorhinolaryngology Head and Neck Surgery, Tongji HospitalTongji Medical UniversityShanghaiChina
| | - Dong‐Dong Zhu
- Department of Otorhinolaryngology Head and Neck SurgeryChina‐Japan Union Hospital of Jilin UniversityChangchunChina
| | - Luo Ba
- Department of Otorhinolaryngology Head and Neck SurgeryXizang Autonomous Region People′s HospitalLasaChina
| | - Zhi‐Wei Cao
- Department of Otorhinolaryngology Head and Neck SurgeryShengjing Hospital of China Medical UniversityShenyangChina
| | - Xu‐Ping Xiao
- Department of Otorhinolaryngology Head and Neck SurgeryHunan Provincial People′s HospitalChangshaChina
| | - Xin Wei
- Department of Otorhinolaryngology Head and Neck SurgeryHainan General HospitalHaikouChina
| | - Zhi‐Hong Lin
- Department of Otorhinolaryngology Head and Neck Surgery, The Second Affiliated Hospital, College of MedicineZhejiang UniversityHangzhouChina
| | - Feng‐Hong Chen
- Department of Otorhinolaryngology, The First Affiliated HospitalSun Yat‐sen UniversityGuangzhouChina
| | - Chun‐Guang Shan
- Department of Otorhinolaryngology Head and Neck SurgeryThe Second Hospital of Hebei Medical UniversityShijiazhuangChina
| | - Guang‐Ke Wang
- Department of Otorhinolaryngology Head and Neck SurgeryHenan Provincial People′s HospitalZhengzhouChina
| | - Jing Ye
- Department of Otorhinolaryngology Head and Neck SurgeryThe First Affiliated Hospital of Nanchang UniversityNanchangChina
| | - Shen‐Hong Qu
- Department of Otorhinolaryngology Head and Neck SurgeryGuangxi Zhuang Autonomous Region People′s HospitalNanningChina
| | - Chang‐Qing Zhao
- Department of Otorhinolaryngology Head and Neck SurgeryShanxi Medical University Affiliated Second HospitalTaiyuanChina
| | - Zhen‐Lin Wang
- Department of Otorhinolaryngology Head and Neck Surgery, XuanWu HospitalCapital Medical UniversityBeijingChina
| | - Hua‐Bin Li
- Department of Otorhinolaryngology Head and Neck Surgery, Eye, Ear, Nose and Throat Hospital, Shanghai Medical CollegeFudan UniversityShanghaiChina
| | - Feng Liu
- Department of Otorhinolaryngology Head and Neck Surgery, West China HospitalSichuan UniversityChengduChina
| | - Xiao‐Bo Cui
- Department of Otorhinolaryngology Head and Neck SurgeryAffiliated Hospital of Inner Mongolia Medical UniversityHohhotChina
| | - Sheng‐Nan Ye
- Department of Otorhinolaryngology Head and Neck SurgeryThe First Affiliated Hospital of Fujian Medical UniversityFuzhouChina
| | - Zheng Liu
- Department of Otorhinolaryngology Head and Neck Surgery, Tongji Hospital, Tongji Medical CollegeHuazhong University of Science and TechnologyWuhanChina
| | - Yu Xu
- Department of Otorhinolaryngology Head and Neck SurgeryRenmin Hospital of Wuhan UniversityWuhanChina
| | - Xiao Cai
- Department of Otorhinolaryngology Head and Neck SurgeryQinghai Provincial People′s HospitalXiningChina
| | - Wei Huang
- Department of Otorhinolaryngology Head and Neck SurgeryTianjin Huanhu HospitalTianjinChina
| | - Ru‐Xin Zhang
- Department of Otorhinolaryngology Head and Neck SurgeryHuadong Hospital Affiliated to Fudan UniversityShanghaiChina
| | - Yu‐Lin Zhao
- Department of Otorhinolaryngology Head and Neck SurgeryThe First Affiliated Hospital of Zhengzhou UniversityZhengzhouChina
| | - Guo‐Dong Yu
- Department of Otorhinolaryngology Head and Neck SurgeryAffiliated Hospital of Guizhou Medical UniversityGuiyangChina
| | - Guang‐Gang Shi
- Department of Otorhinolaryngology Head and Neck Surgery, Shandong Provincial HospitalAffiliated to Shandong First Medical UniversityJinanChina
| | - Mei‐Ping Lu
- Department of Otorhinolaryngology & Clinical Allergy Center, The First Affiliated HospitalNanjing Medical UniversityNanjingChina
| | - Yang Shen
- Department of Otorhinolaryngology Head and Neck SurgeryThe First Affiliated Hospital of Chongqing Medical UniversityChongqingChina
| | - Yu‐Tong Zhao
- Department of Otorhinolaryngology Head and Neck SurgeryThe First People′s Hospital of YinchuanYinchuanChina
| | - Jia‐Hong Pei
- Department of OtorhinolaryngologyThe First People′s Hospital of Yunnan ProvinceKunmingChina
| | - Shao‐Bing Xie
- Department of Otorhinolaryngology Head and Neck SurgeryXiangya Hospital Central South UniversityChangshaChina
| | - Long‐Gang Yu
- Department of Otorhinolaryngology Head and Neck SurgeryThe Affiliated Hospital of Qingdao UniversityQingdaoChina
| | - Ye‐Hai Liu
- Department of Otorhinolaryngology Head and Neck SurgeryThe First Affiliated Hospital of Anhui Medical UniversityHefeiChina
| | - Shao‐Wei Gu
- Department of Otorhinolaryngology Head and Neck SurgeryQilu Hospital of Shandong UniversityJinanChina
| | - Yu‐Cheng Yang
- Department of Otorhinolaryngology Head and Neck SurgeryThe First Affiliated Hospital of Chongqing Medical UniversityChongqingChina
| | - Lei Cheng
- Department of Otorhinolaryngology & Clinical Allergy Center, The First Affiliated HospitalNanjing Medical UniversityNanjingChina
| | - Jian‐Feng liu
- Department of Otorhinolaryngology Head and Neck SurgeryChina‐Japan Friendship HospitalBeijingChina
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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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Pung R, Russell TW, Kucharski AJ. Detecting changes in generation and serial intervals under varying pathogen biology, contact patterns and outbreak response. PLoS Comput Biol 2024; 20:e1011967. [PMID: 38517931 PMCID: PMC10990235 DOI: 10.1371/journal.pcbi.1011967] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2023] [Revised: 04/03/2024] [Accepted: 03/04/2024] [Indexed: 03/24/2024] Open
Abstract
The epidemiological characteristics of SARS-CoV-2 transmission have changed over the pandemic due to emergence of new variants. A decrease in the generation or serial intervals would imply a shortened transmission timescale and, hence, outbreak response measures would need to expand at a faster rate. However, there are challenges in measuring these intervals. Alongside epidemiological changes, factors like varying delays in outbreak response, social contact patterns, dependence on the growth phase of an outbreak, and effects of exposure to multiple infectors can also influence measured generation or serial intervals. To guide real-time interpretation of variant data, we simulated concurrent changes in the aforementioned factors and estimated the statistical power to detect a change in the generation and serial interval. We compared our findings to the reported decrease or lack thereof in the generation and serial intervals of different SARS-CoV-2 variants. Our study helps to clarify contradictory outbreak observations and informs the required sample sizes under certain outbreak conditions to ensure that future studies of generation and serial intervals are adequately powered.
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Affiliation(s)
- Rachael Pung
- Ministry of Health, Singapore, Singapore
- Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Timothy W. Russell
- Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Adam J. Kucharski
- Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, United Kingdom
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7
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Lee HY, Park YJ, Lee SE, Yoo HN, Kim IH, No JS, Kim EJ, Yu J, Bae S, Yu M. Risk factors for SARS-CoV-2 transmission during a movie theater outbreak in Incheon in the Republic of Korea, November 2021: a retrospective study. Osong Public Health Res Perspect 2024; 15:45-55. [PMID: 38481049 PMCID: PMC10982657 DOI: 10.24171/j.phrp.2023.0269] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2023] [Revised: 11/19/2023] [Accepted: 11/26/2023] [Indexed: 04/04/2024] Open
Abstract
BACKGROUND We examined factors contributing to the transmission of an acute respiratory virus within multi-use facilities, focusing on an outbreak of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in a movie theater in the Republic of Korea. METHODS This retrospective cohort study involved a descriptive analysis of 48 confirmed cases. Logistic regression was applied to a cohort of 80 theater attendees to identify risk factors for infection. The infection source and transmission route were determined through gene sequencing data analysis. RESULTS Of the 48 confirmed cases, 35 were theater attendees (72.9%), 10 were family members of attendees (20.8%), 2 were friends (4.2%), and 1 was an employee (2.1%). Among the 80 individuals who attended the 3rd to 5th screenings of the day, 35 became infected, representing a 43.8% attack rate. Specifically, 28 of the 33 third-screening attendees developed confirmed SARSCoV-2, constituting an 84.8% attack rate. Furthermore, 11 of the 12 cases epidemiologically linked to the theater outbreak were clustered monophyletically within the AY.69 lineage. At the time of the screening, 35 individuals (72.9%) had received 2 vaccine doses. However, vaccination status did not significantly influence infection risk. Multivariate analysis revealed that close contacts had a 15.9-fold higher risk of infection (95% confidence interval, 4.37-78.39) than casual contacts. CONCLUSION SARS-CoV-2 transmission occurred within the theater, and extended into the community, via a moviegoer who attended the 3rd screening during the viral incubation period after contracting the virus from a family member. This study emphasizes the importance of adequate ventilation in theaters.
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Affiliation(s)
- Hye Young Lee
- Division of Epidemiological Investigation Analysis, Bureau of Public Health Emergency Preparedness Korea Disease Control and Prevention Agency, Cheongju, Republic of Korea
- Team of Epidemiological Investigation, Korea Disease Control and Prevention Agency, Cheongju, Republic of Korea
| | - Young-Joon Park
- Division of Epidemiological Investigation Analysis, Bureau of Public Health Emergency Preparedness Korea Disease Control and Prevention Agency, Cheongju, Republic of Korea
- Team of Epidemiological Investigation, Korea Disease Control and Prevention Agency, Cheongju, Republic of Korea
| | - Sang-Eun Lee
- Team of Epidemiological Investigation, Korea Disease Control and Prevention Agency, Cheongju, Republic of Korea
| | - Han-Na Yoo
- Department of Infectious Disease Control, Bureau of Health & Sports, Incheon Metropolitan Government, Incheon, Republic of Korea
| | - Il-Hwan Kim
- Division of Emerging Infectious Diseases, Bureau of Infectious Disease Diagnosis Control, Korea Disease Control and Prevention Agency, Cheongju, Republic of Korea
| | - Jin Sun No
- Division of Emerging Infectious Diseases, Bureau of Infectious Disease Diagnosis Control, Korea Disease Control and Prevention Agency, Cheongju, Republic of Korea
| | - Eun-Jin Kim
- Division of Emerging Infectious Diseases, Bureau of Infectious Disease Diagnosis Control, Korea Disease Control and Prevention Agency, Cheongju, Republic of Korea
| | - Jungyeon Yu
- Department of Building Research, Korea Institute of Civil Engineering and Building Technology, Goyang, Republic of Korea
| | - Sanghwan Bae
- Department of Building Research, Korea Institute of Civil Engineering and Building Technology, Goyang, Republic of Korea
| | - Mi Yu
- Division of Epidemiological Investigation Analysis, Bureau of Public Health Emergency Preparedness Korea Disease Control and Prevention Agency, Cheongju, Republic of Korea
- Team of Epidemiological Investigation, Korea Disease Control and Prevention Agency, Cheongju, Republic of Korea
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8
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He X, Liao Y, Liang Y, Yu J, Gao W, Wan J, Liao Y, Su J, Zou X, Tang S. Transmission characteristics and inactivated vaccine effectiveness against transmission of the SARS-CoV-2 Omicron BA.2 variant in Shenzhen, China. Front Immunol 2024; 14:1290279. [PMID: 38259438 PMCID: PMC10800792 DOI: 10.3389/fimmu.2023.1290279] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2023] [Accepted: 12/13/2023] [Indexed: 01/24/2024] Open
Abstract
We conducted a retrospective cohort study to evaluate the transmission risk of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Omicron BA.2 variant and the effectiveness of inactivated COVID-19 vaccine boosters in Shenzhen during a BA.2 outbreak period from 1 February to 21 April 2022. A total of 1,248 individuals were infected with the BA.2 variant, and 7,855 close contacts were carefully investigated. The risk factors for the high secondary attack rate of SARS-CoV-2 infection were household contacts [adjusted odds ratio (aOR): 1.748; 95% confidence interval (CI): 1.448, 2.110], younger individuals aged 0-17 years (aOR: 2.730; 95% CI: 2.118, 3.518), older persons aged ≥60 years (aOR: 1.342; 95% CI: 1.135, 1.588), women (aOR: 1.442; 95% CI: 1.210, 1.718), and the subjects exposed to the post-onset index cases (aOR: 8.546; 95% CI: 6.610, 11.050), respectively. Compared with the unvaccinated and partially vaccinated individuals, a relatively low risk of secondary attack was found for the individuals who received booster vaccination (aOR: 0.871; 95% CI: 0.761, 0.997). Moreover, a high transmission risk was found for the index cases aged ≥60 years (aOR: 1.359; 95% CI: 1.132, 1.632), whereas a relatively low transmission risk was observed for the index cases who received full vaccination (aOR: 0.642; 95% CI: 0.490, 0.841) and booster vaccination (aOR: 0.676; 95% CI: 0.594, 0.770). Compared with full vaccination, booster vaccination of inactivated COVID-19 vaccine showed an effectiveness of 24.0% (95% CI: 7.0%, 37.9%) against BA.2 transmission for the adults ≥18 years and 93.7% (95% CI: 72.4%, 98.6%) for the adults ≥60 years, whereas the effectiveness was 51.0% (95% CI: 21.9%, 69.3%) for the individuals of 14 days to 179 days after booster vaccination and 51.2% (95% CI: 37.5%, 61.9%) for the non-household contacts. The estimated mean values of the generation interval, serial interval, incubation period, latent period, and viral shedding period were 2.7 days, 3.2 days, 2.4 days, 2.1 days, and 17.9 days, respectively. In summary, our results confirmed that the main transmission route of Omicron BA.2 subvariant was household contact, and booster vaccination of the inactivated vaccines was relatively effective against BA.2 subvariant transmission in older people.
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Affiliation(s)
- Xiaofeng He
- Department of Epidemiology, School of Public Health, Southern Medical University, Guangzhou, China
- Institute of Evidence-Based Medicine, Heping Hospital Affiliated to Changzhi Medical College, Changzhi, China
| | - Yuxue Liao
- Office of Emergency, Shenzhen Center for Disease Control and Prevention, Shenzhen, China
| | - Yuanhao Liang
- Department of Epidemiology, School of Public Health, Southern Medical University, Guangzhou, China
| | - Jiexin Yu
- Third Class of 2019 of Clinical Medicine, Suzhou Medical College, Soochow University, Suzhou, Jiangsu, China
| | - Wei Gao
- Office of Emergency, Shenzhen Center for Disease Control and Prevention, Shenzhen, China
| | - Jia Wan
- Office of Emergency, Shenzhen Center for Disease Control and Prevention, Shenzhen, China
| | - Yi Liao
- Office of Emergency, Shenzhen Center for Disease Control and Prevention, Shenzhen, China
| | - Jiao Su
- Department of Biochemistry, Changzhi Medical College, Changzhi, China
| | - Xuan Zou
- Office of Emergency, Shenzhen Center for Disease Control and Prevention, Shenzhen, China
| | - Shixing Tang
- Department of Epidemiology, School of Public Health, Southern Medical University, Guangzhou, China
- Department of Infectious Diseases, Nanfang Hospital, Southern Medical University, Guangzhou, China
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9
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Zahmatkesh A, Salmasi E, Gholizadeh R. Interaction of toll-like receptors and ACE-2 with different variants of SARS-CoV-2: A computational analysis. BIOIMPACTS : BI 2024; 14:30150. [PMID: 39104618 PMCID: PMC11298020 DOI: 10.34172/bi.2024.30150] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/12/2023] [Revised: 12/03/2023] [Accepted: 12/12/2023] [Indexed: 08/07/2024]
Abstract
Introduction Computational studies were performed to investigate the unknown status of endosomal and cell surface receptors in SARS-CoV-2 infection. The interactions between Toll-like receptors (TLRs)- 4/7/8/9 or ACE2 receptor and different SARS-CoV-2 variants were investigated. Methods The RNA motifs for TLR7, TLR8 and a CpG motif for TLR9 were analyzed in different variants. Molecular docking and molecular dynamics (MD) simulations were performed to investigate receptor-ligand interactions. Results The number of motifs recognized by TLR7/8/9 in the Alpha, Delta and Iranian variants was lower than in the wild type (WT). Docking analysis revealed that the Alpha, Delta and some Iranian spike variants had a higher affinity for ACE2 and TLR4 than the WT, which may account for their higher transmission rate. The MD simulation also showed differences in stability and structure size between the variants and the WT, indicating potential variations in viral load. Conclusion It appears that Alpha and some Iranian isolates are the variants of concern due to their higher transmissibility and rapid spread. The Delta mutant is also a variant of concern, not only because of its closer interaction with ACE2, but also with TLR4. Our results emphasize the importance of ACE2 and TLR4, rather than endosomal TLRs, in mediating the effects of different viral mutations and suggest their potential therapeutic applications.
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Affiliation(s)
- Azadeh Zahmatkesh
- Department of Anaerobic Bacterial Vaccines Research and Production, Razi Vaccine and Serum Research Institute, Agricultural Research, Education and Extension Organization, Karaj, Iran
| | - Elham Salmasi
- Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, PR China
| | - Reza Gholizadeh
- Department of Catalysis and Chemical Reaction Engineering, National Institute of Chemistry, Hajdrihova 19, SI-1001 Ljubljana, Slovenia
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10
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Sheng WH, Hsieh SM, Chang SC. Achievements of COVID-19 vaccination programs: Taiwanese perspective. J Formos Med Assoc 2024; 123 Suppl 1:S70-S76. [PMID: 37142477 PMCID: PMC10133881 DOI: 10.1016/j.jfma.2023.04.017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2023] [Revised: 03/06/2023] [Accepted: 04/23/2023] [Indexed: 05/06/2023] Open
Abstract
The coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is a global health crisis. The specific characteristics of aerosol transmission in the latent period and the contagiousness of SARS-CoV-2 lead to rapid spread of infection in the community. Vaccination is the most effective method for preventing infection and severe outcomes. As of December 1, 2022, 88% of the Taiwanese population had received at least two doses of COVID-19 vaccines. Heterologous vaccination with ChAdOx1-mRNA-based or ChAdOx1-protein-based vaccines has been found to elicit higher immunogenicity than homologous vaccination with ChAdOx1-ChAdOx1 vaccines. A longitudinal cohort study revealed that 8-12-week intervals between the two heterologous vaccine doses of the primary series led to good immunogenicity and that the vaccines were safe. A third booster dose of mRNA vaccine is being encouraged to evoke effective immune responses against variants of concern. A novel domestic recombinant protein subunit vaccine (MVC-COV1901) was manufactured and authorized for emergency use in Taiwan. It has shown a good safety profile, with promising neutralizing antibody titers against SARS-CoV-2. Given the global pandemic due to emerging novel variants of SARS-CoV-2, booster COVID-19 vaccines and appropriate intervals between booster doses need to be investigated.
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Affiliation(s)
- Wang-Huei Sheng
- Department of Internal Medicine, National Taiwan University Hospital, Taipei City, Taiwan; School of Medicine, National Taiwan University College of Medicine, Taipei City, Taiwan
| | - Szu-Min Hsieh
- Department of Internal Medicine, National Taiwan University Hospital, Taipei City, Taiwan; School of Medicine, National Taiwan University College of Medicine, Taipei City, Taiwan
| | - Shan-Chwen Chang
- Department of Internal Medicine, National Taiwan University Hospital, Taipei City, Taiwan; School of Medicine, National Taiwan University College of Medicine, Taipei City, Taiwan.
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11
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He X, Zeng B, Wang Y, Pang Y, Zhang M, Hu T, Liang Y, Kang M, Tang S. Effectiveness of booster vaccination with inactivated COVID-19 vaccines against SARS-CoV-2 Omicron BA.2 infection in Guangdong, China: a cohort study. Front Immunol 2023; 14:1257360. [PMID: 37915583 PMCID: PMC10616523 DOI: 10.3389/fimmu.2023.1257360] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2023] [Accepted: 09/29/2023] [Indexed: 11/03/2023] Open
Abstract
The effectiveness of COVID-19 vaccines wanes over time and the emergence of the SARS-CoV-2 Omicron variant led to the accelerated expansion of efforts for booster vaccination. However, the effect and contribution of booster vaccination with inactivated COVID-19 vaccines remain to be evaluated. We conducted a retrospective close contacts cohort study to analyze the epidemiological characteristics and Omicron infection risk, and to evaluate the effectiveness of booster vaccination with inactivated COVID-19 vaccines against SARS-CoV-2 infection, symptomatic COVID-19, and COVID-19 pneumonia during the outbreaks of Omicron BA.2 infection from 1 February to 31 July 2022 in Guangdong, China. A total of 46,547 close contacts were identified while 6.3% contracted Omicron BA.2 infection, 1.8% were asymptomatic infection, 4.1% developed mild COVID-19, and 0.3% had COVID-19 pneumonia. We found that females and individuals aged 0-17 or ≥ 60 years old were more prone to SARS-CoV-2 infection. The vaccinated individuals showed lower infection risk when compared with the unvaccinated people. The effectiveness of booster vaccination with inactivated COVID-19 vaccines against SARS-CoV-2 infection and symptomatic COVID-19 was 28.6% (95% CI: 11.6%, 35.0%) and 39.6% (95% CI: 30.0, 47.9) among adults aged ≥ 18 years old, respectively when compared with full vaccination. Booster vaccination provided a moderate level of protection against SARS-CoV-2 infection (VE: 49.9%, 95% CI: 22.3%-67.7%) and symptomatic COVID-19 (VE: 62.6%, 95% CI: 36.2%-78.0%) among adults aged ≥ 60 years old. Moreover, the effectiveness of booster vaccination was 52.2% (95% CI: 21.3%, 70.9%) and 83.8% (95% CI: 28.1%, 96.3%) against COVID-19 pneumonia in adults aged ≥ 18 and ≥ 60 years old, respectively. The reduction of absolute risk rate of COVID-19 pneumonia in the booster vaccination group was 0·96% (95% CI: 0.33%, 1.11%), and the number needed to vaccinate to prevent one case of COVID-19 pneumonia was 104 (95% CI: 91, 303) in adults aged ≥ 60 years old. In summary, booster vaccination with inactivated COVID-19 vaccines provides a low level of protection against infection and symptomatic in adults of 18-59 years old, and a moderate level of protection in older adults of more than 60 years old, but a high level of protection against COVID-19 pneumonia in older adults.
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Affiliation(s)
- Xiaofeng He
- Department of Epidemiology, School of Public Health, Southern Medical University, Guangzhou, China
- Institute of Evidence-Based Medicine, Heping Hospital Affiliated to Changzhi Medical College, Changzhi, China
| | - Biao Zeng
- Institute of Infectious Disease Control and Prevention, Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, China
| | - Ye Wang
- Department of Epidemiology, School of Medicine, Jinan University, Guangzhou, China
| | - Yulian Pang
- Department of Epidemiology, School of Public Health, Southern Medical University, Guangzhou, China
| | - Meng Zhang
- Institute of Infectious Disease Control and Prevention, Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, China
| | - Ting Hu
- Institute of Infectious Disease Control and Prevention, Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, China
| | - Yuanhao Liang
- Department of Epidemiology, School of Public Health, Southern Medical University, Guangzhou, China
| | - Min Kang
- Institute of Infectious Disease Control and Prevention, Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, China
- Department of Epidemiology, School of Medicine, Jinan University, Guangzhou, China
| | - Shixing Tang
- Department of Epidemiology, School of Public Health, Southern Medical University, Guangzhou, China
- Department of Infectious Diseases, Nanfang Hospital, Southern Medical University, Guangzhou, China
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12
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Xu X, Wu Y, Kummer AG, Zhao Y, Hu Z, Wang Y, Liu H, Ajelli M, Yu H. Assessing changes in incubation period, serial interval, and generation time of SARS-CoV-2 variants of concern: a systematic review and meta-analysis. BMC Med 2023; 21:374. [PMID: 37775772 PMCID: PMC10541713 DOI: 10.1186/s12916-023-03070-8] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Accepted: 09/05/2023] [Indexed: 10/01/2023] Open
Abstract
BACKGROUND After the first COVID-19 wave caused by the ancestral lineage, the pandemic has been fueled from the continuous emergence of new SARS-CoV-2 variants. Understanding key time-to-event periods for each emerging variant of concern is critical as it can provide insights into the future trajectory of the virus and help inform outbreak preparedness and response planning. Here, we aim to examine how the incubation period, serial interval, and generation time have changed from the ancestral SARS-CoV-2 lineage to different variants of concern. METHODS We conducted a systematic review and meta-analysis that synthesized the estimates of incubation period, serial interval, and generation time (both realized and intrinsic) for the ancestral lineage, Alpha, Beta, and Omicron variants of SARS-CoV-2. RESULTS Our study included 280 records obtained from 147 household studies, contact tracing studies, or studies where epidemiological links were known. With each emerging variant, we found a progressive shortening of each of the analyzed key time-to-event periods, although we did not find statistically significant differences between the Omicron subvariants. We found that Omicron BA.1 had the shortest pooled estimates for the incubation period (3.49 days, 95% CI: 3.13-4.86 days), Omicron BA.5 for the serial interval (2.37 days, 95% CI: 1.71-3.04 days), and Omicron BA.1 for the realized generation time (2.99 days, 95% CI: 2.48-3.49 days). Only one estimate for the intrinsic generation time was available for Omicron subvariants: 6.84 days (95% CrI: 5.72-8.60 days) for Omicron BA.1. The ancestral lineage had the highest pooled estimates for each investigated key time-to-event period. We also observed shorter pooled estimates for the serial interval compared to the incubation period across the virus lineages. When pooling the estimates across different virus lineages, we found considerable heterogeneities (I2 > 80%; I2 refers to the percentage of total variation across studies that is due to heterogeneity rather than chance), possibly resulting from heterogeneities between the different study populations (e.g., deployed interventions, social behavior, demographic characteristics). CONCLUSIONS Our study supports the importance of conducting contact tracing and epidemiological investigations to monitor changes in SARS-CoV-2 transmission patterns. Our findings highlight a progressive shortening of the incubation period, serial interval, and generation time, which can lead to epidemics that spread faster, with larger peak incidence, and harder to control. We also consistently found a shorter serial interval than incubation period, suggesting that a key feature of SARS-CoV-2 is the potential for pre-symptomatic transmission. These observations are instrumental to plan for future COVID-19 waves.
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Affiliation(s)
- Xiangyanyu Xu
- School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China
| | - Yanpeng Wu
- Shanghai Institute of Infectious Disease and Biosecurity, Fudan University, Shanghai, China
| | - Allisandra G Kummer
- Laboratory of Computational Epidemiology and Public Health, Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomington, IN, USA
| | - Yuchen Zhao
- Shanghai Institute of Infectious Disease and Biosecurity, Fudan University, Shanghai, China
| | - Zexin Hu
- Shanghai Institute of Infectious Disease and Biosecurity, Fudan University, Shanghai, China
| | - Yan Wang
- School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China
| | - Hengcong Liu
- School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China
| | - Marco Ajelli
- Laboratory of Computational Epidemiology and Public Health, Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomington, IN, USA.
| | - Hongjie Yu
- School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China.
- Shanghai Institute of Infectious Disease and Biosecurity, Fudan University, Shanghai, China.
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13
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He Y, Martinez L, Ge Y, Feng Y, Chen Y, Tan J, Westbrook A, Li C, Cheng W, Ling F, Cheng H, Wu S, Zhong W, Handel A, Huang H, Sun J, Shen Y. Social Mixing and Network Characteristics of COVID-19 Patients Before and After Widespread Interventions: A Population-based Study. Epidemiol Infect 2023; 151:1-38. [PMID: 37577939 PMCID: PMC10540215 DOI: 10.1017/s0950268823001292] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2023] [Revised: 06/28/2023] [Accepted: 07/31/2023] [Indexed: 08/15/2023] Open
Abstract
SARS-CoV-2 rapidly spreads among humans via social networks, with social mixing and network characteristics potentially facilitating transmission. However, limited data on topological structural features has hindered in-depth studies. Existing research is based on snapshot analyses, preventing temporal investigations of network changes. Comparing network characteristics over time offers additional insights into transmission dynamics. We examined confirmed COVID-19 patients from an eastern Chinese province, analyzing social mixing and network characteristics using transmission network topology before and after widespread interventions. Between the two time periods, the percentage of singleton networks increased from 38.9 to 62.8 ; the average shortest path length decreased from 1.53 to 1.14 ; the average betweenness reduced from 0.65 to 0.11 ; the average cluster size dropped from 4.05 to 2.72 ; and the out-degree had a slight but nonsignificant decline from 0.75 to 0.63 Results show that nonpharmaceutical interventions effectively disrupted transmission networks, preventing further disease spread. Additionally, we found that the networks’ dynamic structure provided more information than solely examining infection curves after applying descriptive and agent-based modeling approaches. In summary, we investigated social mixing and network characteristics of COVID-19 patients during different pandemic stages, revealing transmission network heterogeneities.
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Affiliation(s)
- Yuncong He
- School of Mathematics, Sun Yat-sen University, Guangzhou, China
| | - Leonardo Martinez
- Department of Epidemiology, School of Public Health, Boston University, Boston, USA
| | - Yang Ge
- School of Health Professions, University of Southern Mississippi, Hattiesburg, USA
| | - Yan Feng
- Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, China
| | - Yewen Chen
- Department of Epidemiology and Biostatistics, College of Public Health, University of Georgia, Athens, USA
| | - Jianbin Tan
- School of Mathematics, Sun Yat-sen University, Guangzhou, China
| | - Adrianna Westbrook
- Department of Epidemiology and Biostatistics, College of Public Health, University of Georgia, Athens, USA
| | - Changwei Li
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, USA
| | - Wei Cheng
- Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, China
| | - Feng Ling
- Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, China
| | - Huimin Cheng
- Department of Statistics, University of Georgia, Athens, USA
| | - Shushan Wu
- Department of Statistics, University of Georgia, Athens, USA
| | - Wenxuan Zhong
- Department of Statistics, University of Georgia, Athens, USA
| | - Andreas Handel
- Department of Epidemiology and Biostatistics, College of Public Health, University of Georgia, Athens, USA
| | - Hui Huang
- Center for Applied Statistics and School of Statistics, Renmin University of China, Beijing, China
| | - Jimin Sun
- Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, China
| | - Ye Shen
- Department of Epidemiology and Biostatistics, College of Public Health, University of Georgia, Athens, USA
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14
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Merling MR, Williams A, Mahfooz NS, Ruane-Foster M, Smith J, Jahnes J, Ayers LW, Bazan JA, Norris A, Norris Turner A, Oglesbee M, Faith SA, Quam MB, Robinson RT. The emergence of SARS-CoV-2 lineages and associated saliva antibody responses among asymptomatic individuals in a large university community. PLoS Pathog 2023; 19:e1011596. [PMID: 37603565 PMCID: PMC10470930 DOI: 10.1371/journal.ppat.1011596] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Revised: 08/31/2023] [Accepted: 08/02/2023] [Indexed: 08/23/2023] Open
Abstract
SARS-CoV-2 (CoV2) infected, asymptomatic individuals are an important contributor to COVID transmission. CoV2-specific immunoglobulin (Ig)-as generated by the immune system following infection or vaccination-has helped limit CoV2 transmission from asymptomatic individuals to susceptible populations (e.g. elderly). Here, we describe the relationships between COVID incidence and CoV2 lineage, viral load, saliva Ig levels (CoV2-specific IgM, IgA and IgG), and ACE2 binding inhibition capacity in asymptomatic individuals between January 2021 and May 2022. These data were generated as part of a large university COVID monitoring program in Ohio, United States of America, and demonstrate that COVID incidence among asymptomatic individuals occurred in waves which mirrored those in surrounding regions, with saliva CoV2 viral loads becoming progressively higher in our community until vaccine mandates were established. Among the unvaccinated, infection with each CoV2 lineage (pre-Omicron) resulted in saliva Spike-specific IgM, IgA, and IgG responses, the latter increasing significantly post-infection and being more pronounced than N-specific IgG responses. Vaccination resulted in significantly higher Spike-specific IgG levels compared to unvaccinated infected individuals, and uninfected vaccinees' saliva was more capable of inhibiting Spike function. Vaccinees with breakthrough Delta infections had Spike-specific IgG levels comparable to those of uninfected vaccinees; however, their ability to inhibit Spike binding was diminished. These data are consistent with COVID vaccines having achieved hoped-for effects in our community, including the generation of mucosal antibodies that inhibit Spike and lower community viral loads, and suggest breakthrough Delta infections were not due to an absence of vaccine-elicited Ig, but instead limited Spike binding activity in the face of high community viral loads.
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Affiliation(s)
- Marlena R. Merling
- Department of Microbial Infection & Immunity, The Ohio State University, Columbus, Ohio, United States of America
| | - Amanda Williams
- Department of Microbial Infection & Immunity, The Ohio State University, Columbus, Ohio, United States of America
- Infectious Disease Institute, The Ohio State University, Columbus, Ohio, United States of America
| | - Najmus S. Mahfooz
- Department of Microbial Infection & Immunity, The Ohio State University, Columbus, Ohio, United States of America
| | - Marisa Ruane-Foster
- Department of Microbial Infection & Immunity, The Ohio State University, Columbus, Ohio, United States of America
| | - Jacob Smith
- Infectious Disease Institute, The Ohio State University, Columbus, Ohio, United States of America
| | - Jeff Jahnes
- Infectious Disease Institute, The Ohio State University, Columbus, Ohio, United States of America
| | - Leona W. Ayers
- Department of Pathology, The Ohio State University, Columbus, Ohio, United States of America
| | - Jose A. Bazan
- Division of Infectious Disease, Department of Internal Medicine, The Ohio State University, Columbus, Ohio, United States of America
| | - Alison Norris
- Division of Infectious Disease, Department of Internal Medicine, The Ohio State University, Columbus, Ohio, United States of America
- Department of Epidemiology, The Ohio State University, Columbus, Ohio, United States of America
| | - Abigail Norris Turner
- Division of Infectious Disease, Department of Internal Medicine, The Ohio State University, Columbus, Ohio, United States of America
| | - Michael Oglesbee
- Infectious Disease Institute, The Ohio State University, Columbus, Ohio, United States of America
| | - Seth A. Faith
- Infectious Disease Institute, The Ohio State University, Columbus, Ohio, United States of America
| | - Mikkel B. Quam
- Department of Epidemiology, The Ohio State University, Columbus, Ohio, United States of America
| | - Richard T. Robinson
- Department of Microbial Infection & Immunity, The Ohio State University, Columbus, Ohio, United States of America
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15
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Kim JE, Choi H, Lee M, Lee CH. The effect of shortening the quarantine period and lifting the indoor mask mandate on the spread of COVID-19: a mathematical modeling approach. Front Public Health 2023; 11:1166528. [PMID: 37546304 PMCID: PMC10401846 DOI: 10.3389/fpubh.2023.1166528] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Accepted: 07/06/2023] [Indexed: 08/08/2023] Open
Abstract
In this paper, we present a mathematical model to assess the impact of reducing the quarantine period and lifting the indoor mask mandate on the spread of Coronavirus Disease 2019 (COVID-19) in Korea. The model incorporates important epidemiological parameters, such as transmission rates and mortality rates, to simulate the transmission of the virus under different scenarios. Our findings reveal that the impact of mask wearing fades in the long term, which highlights the crucial role of quarantine in controlling the spread of the disease. In addition, balancing the confirmed cases and costs, the lifting of mandatory indoor mask wearing is cost-effective; however, maintaining the quarantine period remains essential. A relationship between the disease transmission rate and vaccine efficiency was also apparent, with higher transmission rates leading to a greater impact of the vaccine efficiency. Moreover, our findings indicate that a higher disease transmission rate exacerbates the consequences of early quarantine release.
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Affiliation(s)
- Jung Eun Kim
- Department of Mathematics and Computer Science, Korea Science Academy of KAIST, Busan, Republic of Korea
| | - Heejin Choi
- Department of Mathematical Sciences, Ulsan National Institute of Science and Technology, Ulsan, Republic of Korea
| | - Minji Lee
- Department of Mathematical Sciences, Ulsan National Institute of Science and Technology, Ulsan, Republic of Korea
| | - Chang Hyeong Lee
- Department of Mathematical Sciences, Ulsan National Institute of Science and Technology, Ulsan, Republic of Korea
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16
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Demers J, Fagan WF, Potluri S, Calabrese JM. The relationship between controllability, optimal testing resource allocation, and incubation-latent period mismatch as revealed by COVID-19. Infect Dis Model 2023; 8:514-538. [PMID: 37250860 PMCID: PMC10186984 DOI: 10.1016/j.idm.2023.04.007] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2023] [Revised: 04/21/2023] [Accepted: 04/25/2023] [Indexed: 05/31/2023] Open
Abstract
The severe shortfall in testing supplies during the initial COVID-19 outbreak and ensuing struggle to manage the pandemic have affirmed the critical importance of optimal supply-constrained resource allocation strategies for controlling novel disease epidemics. To address the challenge of constrained resource optimization for managing diseases with complications like pre- and asymptomatic transmission, we develop an integro partial differential equation compartmental disease model which incorporates realistic latent, incubation, and infectious period distributions along with limited testing supplies for identifying and quarantining infected individuals. Our model overcomes the limitations of typical ordinary differential equation compartmental models by decoupling symptom status from model compartments to allow a more realistic representation of symptom onset and presymptomatic transmission. To analyze the influence of these realistic features on disease controllability, we find optimal strategies for reducing total infection sizes that allocate limited testing resources between 'clinical' testing, which targets symptomatic individuals, and 'non-clinical' testing, which targets non-symptomatic individuals. We apply our model not only to the original, delta, and omicron COVID-19 variants, but also to generically parameterized disease systems with varying mismatches between latent and incubation period distributions, which permit varying degrees of presymptomatic transmission or symptom onset before infectiousness. We find that factors that decrease controllability generally call for reduced levels of non-clinical testing in optimal strategies, while the relationship between incubation-latent mismatch, controllability, and optimal strategies is complicated. In particular, though greater degrees of presymptomatic transmission reduce disease controllability, they may increase or decrease the role of non-clinical testing in optimal strategies depending on other disease factors like transmissibility and latent period length. Importantly, our model allows a spectrum of diseases to be compared within a consistent framework such that lessons learned from COVID-19 can be transferred to resource constrained scenarios in future emerging epidemics and analyzed for optimality.
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Affiliation(s)
- Jeffery Demers
- Center for Advanced Systems Understanding (CASUS), Helmholtz-Zentrum Dresden-Rosendorf (HZDR), Görlitz, Germany
- Dept. of Biology, University of Maryland, College Park, MD, USA
| | - William F Fagan
- Dept. of Biology, University of Maryland, College Park, MD, USA
| | - Sriya Potluri
- Dept. of Biology, University of Maryland, College Park, MD, USA
| | - Justin M Calabrese
- Center for Advanced Systems Understanding (CASUS), Helmholtz-Zentrum Dresden-Rosendorf (HZDR), Görlitz, Germany
- Dept. of Biology, University of Maryland, College Park, MD, USA
- Dept. of Ecological Modelling, Helmholtz Centre for Environmental Research-UFZ, Leipzig, Germany
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17
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Jarvie MM, Reed-Lukomski M, Southwell B, Wright D, Nguyen TNT. Monitoring of COVID-19 in wastewater across the Eastern Upper Peninsula of Michigan. ENVIRONMENTAL ADVANCES 2023; 11:100326. [PMID: 36471702 PMCID: PMC9714184 DOI: 10.1016/j.envadv.2022.100326] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/12/2022] [Revised: 11/01/2022] [Accepted: 11/29/2022] [Indexed: 05/12/2023]
Abstract
Wastewater-based epidemiology is being used as a tool to monitor the spread of COVID-19 and provide an early warning for the presence or increase of clinical cases in a community. The majority of wastewater-based epidemiology for COVID-19 tracking has been utilized in sewersheds that service populations in the tens-to-hundreds of thousands. Few studies have been conducted to assess the usefulness of wastewater in predicting COVID-19 clinical cases specifically in rural areas. This study collected samples from 16 locations across the Eastern Upper Peninsula of Michigan from June to December 2021. Sampling locations included 12 rural municipalities, a Tribal housing community and casino, a public university, three municipalities that also contained a prison, and a small island with heavy tourist traffic. Samples were analyzed for SARS-CoV-2 N1, N2, and variant gene copies using reverse transcriptase droplet digital polymerase chain reaction (RT-ddPCR). Wastewater N1 and N2 gene copies and clinical case counts were correlated to determine if wastewater results were predictive of clinical cases. Significant correlation between N1 and N2 gene copies and clinical cases was found for all sites (⍴= 0.89 to 0.48). N1 and N2 wastewater results were predictive of clinical case trends within 0-7 days. The Delta variant was detected in the Pickford and St. Ignace samples more than 12-days prior to the first reported Delta clinical cases in their respective counties. Locations with low correlation could be attributed to their high rates of tourism. This is further supported by the high correlation seen in the public university, which is a closed population. Long-term wastewater monitoring over a large, rural geographic area is useful for informing the public of potential outbreaks in the community regardless of asymptomatic cases and access to clinical testing.
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Affiliation(s)
- Michelle M Jarvie
- School of Science and Medicine, Lake Superior State University, 650 W. Easterday Ave., Sault Ste, Marie, MI 49783, USA
| | - Moriah Reed-Lukomski
- School of Science and Medicine, Lake Superior State University, 650 W. Easterday Ave., Sault Ste, Marie, MI 49783, USA
| | - Benjamin Southwell
- School of Science and Medicine, Lake Superior State University, 650 W. Easterday Ave., Sault Ste, Marie, MI 49783, USA
| | - Derek Wright
- School of Natural Resources and Environment, Lake Superior State University, 650 W. Easterday Ave., Sault Ste. Marie, MI 49783, USA
| | - Thu N T Nguyen
- School of Science and Medicine, Lake Superior State University, 650 W. Easterday Ave., Sault Ste, Marie, MI 49783, USA
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18
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Peter OJ, Panigoro HS, Abidemi A, Ojo MM, Oguntolu FA. Mathematical Model of COVID-19 Pandemic with Double Dose Vaccination. Acta Biotheor 2023; 71:9. [PMID: 36877326 PMCID: PMC9986676 DOI: 10.1007/s10441-023-09460-y] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2022] [Accepted: 02/14/2023] [Indexed: 03/07/2023]
Abstract
This paper is concerned with the formulation and analysis of an epidemic model of COVID-19 governed by an eight-dimensional system of ordinary differential equations, by taking into account the first dose and the second dose of vaccinated individuals in the population. The developed model is analyzed and the threshold quantity known as the control reproduction number [Formula: see text] is obtained. We investigate the equilibrium stability of the system, and the COVID-free equilibrium is said to be locally asymptotically stable when the control reproduction number is less than unity, and unstable otherwise. Using the least-squares method, the model is calibrated based on the cumulative number of COVID-19 reported cases and available information about the mass vaccine administration in Malaysia between the 24th of February 2021 and February 2022. Following the model fitting and estimation of the parameter values, a global sensitivity analysis was performed by using the Partial Rank Correlation Coefficient (PRCC) to determine the most influential parameters on the threshold quantities. The result shows that the effective transmission rate [Formula: see text], the rate of first vaccine dose [Formula: see text], the second dose vaccination rate [Formula: see text] and the recovery rate due to the second dose of vaccination [Formula: see text] are the most influential of all the model parameters. We further investigate the impact of these parameters by performing a numerical simulation on the developed COVID-19 model. The result of the study shows that adhering to the preventive measures has a huge impact on reducing the spread of the disease in the population. Particularly, an increase in both the first and second dose vaccination rates reduces the number of infected individuals, thus reducing the disease burden in the population.
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Affiliation(s)
- Olumuyiwa James Peter
- Department of Mathematical and Computer Sciences, University of Medical Sciences, Ondo City, Ondo State, Nigeria. .,Department of Epidemiology and Biostatistics, School of Public Health, University of Medical Sciences, Ondo City, Ondo State, Nigeria.
| | - Hasan S Panigoro
- Department of Mathematics, State University of Gorontalo, Bone Bolango, 96119, Indonesia
| | - Afeez Abidemi
- Department of Mathematical Sciences, Federal University of Technology, Akure, Ondo State, Nigeria.,Department of Mathematical Sciences, Universiti Teknologi Malaysia, Johor Bahru, Johor, Malaysia
| | - Mayowa M Ojo
- Department of Mathematical Sciences, University of South Africa, Florida, South Africa.,Microbiology Division, Thermo Fisher Scientific, Lenexa, KS, USA
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19
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Guo Z, Zhao S, Lee SS, Hung CT, Wong NS, Chow TY, Yam CHK, Wang MH, Wang J, Chong KC, Yeoh EK. A statistical framework for tracking the time-varying superspreading potential of COVID-19 epidemic. Epidemics 2023; 42:100670. [PMID: 36709540 PMCID: PMC9872564 DOI: 10.1016/j.epidem.2023.100670] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2022] [Revised: 10/29/2022] [Accepted: 01/17/2023] [Indexed: 01/26/2023] Open
Abstract
Timely detection of an evolving event of an infectious disease with superspreading potential is imperative for territory-wide disease control as well as preventing future outbreaks. While the reproduction number (R) is a commonly-adopted metric for disease transmissibility, the transmission heterogeneity quantified by dispersion parameter k, a metric for superspreading potential is seldom tracked. In this study, we developed an estimation framework to track the time-varying risk of superspreading events (SSEs) and demonstrated the method using the three epidemic waves of COVID-19 in Hong Kong. Epidemiological contact tracing data of the confirmed COVID-19 cases from 23 January 2020 to 30 September 2021 were obtained. By applying branching process models, we jointly estimated the time-varying R and k. Individual-based outbreak simulations were conducted to compare the time-varying assessment of the superspreading potential with the typical non-time-varying estimate of k over a period of time. We found that the COVID-19 transmission in Hong Kong exhibited substantial superspreading during the initial phase of the epidemics, with only 1 % (95 % Credible interval [CrI]: 0.6-2 %), 5 % (95 % CrI: 3-7 %) and 10 % (95 % CrI: 8-14 %) of the most infectious cases generated 80 % of all transmission for the first, second and third epidemic waves, respectively. After implementing local public health interventions, R estimates dropped gradually and k estimates increased thereby reducing the risk of SSEs to approaching zero. Outbreak simulations indicated that the non-time-varying estimate of k may overlook the possibility of large outbreaks. Hence, an estimation of the time-varying k as a compliment of R as a monitoring of both disease transmissibility and superspreading potential, particularly when public health interventions were relaxed is crucial for minimizing the risk of future outbreaks.
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Affiliation(s)
- Zihao Guo
- Jockey Club School of Public Health and Primary Care, Faculty of Medicine, The Chinese University of Hong Kong, Shatin, Hong Kong, China
| | - Shi Zhao
- Jockey Club School of Public Health and Primary Care, Faculty of Medicine, The Chinese University of Hong Kong, Shatin, Hong Kong, China; Centre for Health Systems and Policy Research, Faculty of Medicine, The Chinese University of Hong Kong, Shatin, Hong Kong, China
| | - Shui Shan Lee
- Jockey Club School of Public Health and Primary Care, Faculty of Medicine, The Chinese University of Hong Kong, Shatin, Hong Kong, China; Stanley Ho Centre for Emerging Infectious Diseases, Faculty of Medicine, The Chinese University of Hong Kong, Shatin, Hong Kong, China
| | - Chi Tim Hung
- Jockey Club School of Public Health and Primary Care, Faculty of Medicine, The Chinese University of Hong Kong, Shatin, Hong Kong, China; Centre for Health Systems and Policy Research, Faculty of Medicine, The Chinese University of Hong Kong, Shatin, Hong Kong, China
| | - Ngai Sze Wong
- Jockey Club School of Public Health and Primary Care, Faculty of Medicine, The Chinese University of Hong Kong, Shatin, Hong Kong, China; Stanley Ho Centre for Emerging Infectious Diseases, Faculty of Medicine, The Chinese University of Hong Kong, Shatin, Hong Kong, China
| | - Tsz Yu Chow
- Jockey Club School of Public Health and Primary Care, Faculty of Medicine, The Chinese University of Hong Kong, Shatin, Hong Kong, China; Centre for Health Systems and Policy Research, Faculty of Medicine, The Chinese University of Hong Kong, Shatin, Hong Kong, China
| | - Carrie Ho Kwan Yam
- Jockey Club School of Public Health and Primary Care, Faculty of Medicine, The Chinese University of Hong Kong, Shatin, Hong Kong, China; Centre for Health Systems and Policy Research, Faculty of Medicine, The Chinese University of Hong Kong, Shatin, Hong Kong, China
| | - Maggie Haitian Wang
- Jockey Club School of Public Health and Primary Care, Faculty of Medicine, The Chinese University of Hong Kong, Shatin, Hong Kong, China
| | - Jingxuan Wang
- Jockey Club School of Public Health and Primary Care, Faculty of Medicine, The Chinese University of Hong Kong, Shatin, Hong Kong, China
| | - Ka Chun Chong
- Jockey Club School of Public Health and Primary Care, Faculty of Medicine, The Chinese University of Hong Kong, Shatin, Hong Kong, China; Centre for Health Systems and Policy Research, Faculty of Medicine, The Chinese University of Hong Kong, Shatin, Hong Kong, China.
| | - Eng Kiong Yeoh
- Jockey Club School of Public Health and Primary Care, Faculty of Medicine, The Chinese University of Hong Kong, Shatin, Hong Kong, China; Centre for Health Systems and Policy Research, Faculty of Medicine, The Chinese University of Hong Kong, Shatin, Hong Kong, China
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20
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Sequence analysis of SARS-CoV-2 Delta variant isolated from Makassar, South Sulawesi, Indonesia. Heliyon 2023; 9:e13382. [PMID: 36744069 PMCID: PMC9886429 DOI: 10.1016/j.heliyon.2023.e13382] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2022] [Revised: 01/20/2023] [Accepted: 01/29/2023] [Indexed: 02/01/2023] Open
Abstract
Introduction This study aimed to perform mutation and phylogenetic analyses of the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Delta variants and analyze the characteristic signs and symptoms of patients infected with SARS-CoV-2 Delta variant originated from Makassar during the Delta outbreak.Methods: We collected samples from patients who were infected with coronavirus disease 2019 (COVID-19) between June and October 2021. We selected the Quantitative Reverse Transcription-Polymerase Chain Reaction (PCR)-positive samples with a cycle threshold value of <30 for whole genome sequencing. Total viral ribonucleic acid (RNA) was isolated from 34 PCR-positive nasopharyngeal swab samples, and whole genome sequencing was performed using the Oxford Nanopore GridlON sequencer. Phylogenetic and maximum clade credibility analyses were performed using the Bayesian Markov chain Monte Carlo method. Results It was found that 33 patients were infected with the SARS-CoV-2 Delta variant in this cohort study, among whom 63.6% (21) patients were female. According to the clinical data, 24 (72.7%), 7 (21.2%), and 2 (6.1%) patients had mild, moderate, and severe COVID-19 infections. Phylogenetic analysis based on the spike and RNA-dependent RNA polymerase (RdRp) genes showed that the collected samples were clustered in the main lineage of B.1.617.2 (Delta variant). The Delta variants had a high frequency of distinct mutations in the spike protein region, including T19R (94.12%), L452R (88.23%), T478K (91.17%), D614G (97%), P681R (97%), and D950 N (97%). Other unique mutations found in a smaller frequency in our samples were present in the N-terminal domain, including A27T (2.94%) and A222V (14.70%), and in the receptor-binding domain, including Q414K (5.88%), G446V (2.94%), and T470 N (2.94%). Conclusion This study revealed the unique mutations in the S protein region of Delta variants. T19R, L452R, T478K/T478R, D614G, P681R, and D950 N were the most common substitutions in Makassar's Delta variant.
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21
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Luo K, Wu Y, Wang Y, Liu Z, Yi L, Zhao S, Yan X, Yang H, Sun K, Ajelli M, Hu S, Yu H, Yu H. Transmission Dynamics and Epidemiological Characteristics of the SARS-CoV-2 Delta Variant - Hunan Province, China, 2021. China CDC Wkly 2023; 5:56-62. [PMID: 36776461 PMCID: PMC9902755 DOI: 10.46234/ccdcw2023.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Accepted: 01/03/2023] [Indexed: 01/22/2023] Open
Abstract
What is already known about this topic? Little is known about the epidemiology, natural history, and transmission patterns of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Delta variant. Monitoring the evolution of viral fitness of SARS-CoV-2 in the host population is key for preparedness and response planning. What is added by this report? We analyzed a successfully contained local outbreak of Delta that took place in Hunan, China, and provided estimates of time-to-key event periods, infectiousness over time, and risk factors for SARS-CoV-2 infection and transmission for a still poorly understood variant. What are the implications for public health practice? Our findings simultaneously shed light on both the characteristics of the Delta variant, by identifying key age groups, risk factors, and transmission pathways, and planning a future response effort against SARS-CoV-2.
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Affiliation(s)
- Kaiwei Luo
- Hunan Provincial Center for Disease Control and Prevention;Workstation for Emerging Infectious Disease Control and Prevention, Chinese Academy of Medical Sciences, Changsha City, Hunan Province, China
| | - Yanpeng Wu
- Shanghai Institute of Infectious Disease and Biosecurity, Fudan University, Shanghai Municipality, China
| | - Yan Wang
- School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai Municipality, China
| | - Ziyan Liu
- Hunan Provincial Center for Disease Control and Prevention;Workstation for Emerging Infectious Disease Control and Prevention, Chinese Academy of Medical Sciences, Changsha City, Hunan Province, China
| | - Lan Yi
- Shanghai Institute of Infectious Disease and Biosecurity, Fudan University, Shanghai Municipality, China
| | - Shanlu Zhao
- Hunan Provincial Center for Disease Control and Prevention;Workstation for Emerging Infectious Disease Control and Prevention, Chinese Academy of Medical Sciences, Changsha City, Hunan Province, China
| | - Xuemei Yan
- School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai Municipality, China
| | - Hao Yang
- Hunan Provincial Center for Disease Control and Prevention;Workstation for Emerging Infectious Disease Control and Prevention, Chinese Academy of Medical Sciences, Changsha City, Hunan Province, China
| | - Kaiyuan Sun
- Division of International Epidemiology and Population Studies, Fogarty International Center, National Institutes of Health, Bethesda, MD, USA
| | - Marco Ajelli
- Laboratory of Computational Epidemiology and Public Health, Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomington, IN, USA
| | - Shixiong Hu
- Hunan Provincial Center for Disease Control and Prevention;Workstation for Emerging Infectious Disease Control and Prevention, Chinese Academy of Medical Sciences, Changsha City, Hunan Province, China,Shixiong Hu,
| | - Hongjie Yu
- Shanghai Institute of Infectious Disease and Biosecurity, Fudan University, Shanghai Municipality, China,School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai Municipality, China,Hongjie Yu,
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22
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Ma R, Zhang Y, Zhang Y, Li X, Ji Z. The Relationship between the Transmission of Different SARS-CoV-2 Strains and Air Quality: A Case Study in China. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:ijerph20031943. [PMID: 36767307 PMCID: PMC9916065 DOI: 10.3390/ijerph20031943] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/13/2022] [Revised: 01/07/2023] [Accepted: 01/17/2023] [Indexed: 06/11/2023]
Abstract
Coronavirus Disease 2019 (COVID-19) has been a global public health concern for almost three years, and the transmission characteristics vary among different virus variants. Previous studies have investigated the relationship between air pollutants and COVID-19 infection caused by the original strain of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). However, it is unclear whether individuals might be more susceptible to COVID-19 due to exposure to air pollutants, with the SARS-CoV-2 mutating faster and faster. This study aimed to explore the relationship between air pollutants and COVID-19 infection caused by three major SARS-CoV-2 strains (the original strain, Delta variant, and Omicron variant) in China. A generalized additive model was applied to investigate the associations of COVID-19 infection with six air pollutants (PM2.5, PM10, SO2, CO, NO2, and O3). A positive correlation might be indicated between air pollutants (PM2.5, PM10, and NO2) and confirmed cases of COVID-19 caused by different SARS-CoV-2 strains. It also suggested that the mutant variants appear to be more closely associated with air pollutants than the original strain. This study could provide valuable insight into control strategies that limit the concentration of air pollutants at lower levels and would better control the spread of COVID-19 even as the virus continues to mutate.
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Affiliation(s)
- Ruiqing Ma
- School of Geography and Tourism, Shaanxi Normal University, Xi’an 710119, China
- International Joint Research Centre of Shaanxi Province for Pollutants Exposure and Eco-Environmental Health, Xi’an 710119, China
| | - Yeyue Zhang
- School of Geography and Tourism, Shaanxi Normal University, Xi’an 710119, China
- International Joint Research Centre of Shaanxi Province for Pollutants Exposure and Eco-Environmental Health, Xi’an 710119, China
| | - Yini Zhang
- School of Geography and Tourism, Shaanxi Normal University, Xi’an 710119, China
- International Joint Research Centre of Shaanxi Province for Pollutants Exposure and Eco-Environmental Health, Xi’an 710119, China
| | - Xi Li
- School of Geography and Tourism, Shaanxi Normal University, Xi’an 710119, China
- International Joint Research Centre of Shaanxi Province for Pollutants Exposure and Eco-Environmental Health, Xi’an 710119, China
| | - Zheng Ji
- School of Geography and Tourism, Shaanxi Normal University, Xi’an 710119, China
- International Joint Research Centre of Shaanxi Province for Pollutants Exposure and Eco-Environmental Health, Xi’an 710119, China
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23
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Xin H, Wang Z, Feng S, Sun Z, Yu L, Cowling BJ, Kong Q, Wu P. Transmission dynamics of SARS-CoV-2 Omicron variant infections in Hangzhou, Zhejiang, China, January-February 2022. Int J Infect Dis 2023; 126:132-135. [PMID: 36511336 PMCID: PMC9616478 DOI: 10.1016/j.ijid.2022.10.033] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2022] [Revised: 09/22/2022] [Accepted: 10/23/2022] [Indexed: 11/16/2022] Open
Abstract
OBJECTIVES We aimed to explore the transmission dynamics of the Omicron BA.1.1 variant in an outbreak in China. METHODS We constructed 113 transmission pairs based on the time of exposure and symptom onset for identified infectors and infectees, using the epidemiological data collected during an outbreak in Hangzhou, Zhejiang province, China, between January and February 2022. The key epidemiological parameters were estimated. RESULTS The mean estimates of the incubation period and latent period distributions were 3.8 days (95% credible interval: 3.5, 4.1) and 3.1 days (2.8, 3.5), respectively. The overall transmission risk peaked at symptom onset, and we estimated that 33.6% (24.8, 42.5) of transmission occurred before symptom onset. The forward generation time decreased from 5.2 days (4.7, 5.7) at the start of the outbreak to 2.2 days (2.0, 2.5) by the end. Allowing this variation over time in the generation time distribution, we estimated that the reproduction number dropped rapidly from 9.5 (3.5, 18.4) to 0.8 (0.3, 1.5) over the outbreak. CONCLUSION Shorter incubation period and latent period were estimated for the Omicron BA.1.1 variant. Stringent public health measures prevented a large epidemic by reducing transmission, as indicated by the shortened generation time.
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Affiliation(s)
- 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, China
| | - Zhe Wang
- Hangzhou Center for Disease Control and Prevention, Hangzhou, Zhejiang, China
| | - Shuang Feng
- Hangzhou Center for Disease Control and Prevention, Hangzhou, Zhejiang, China
| | - Zhou Sun
- Hangzhou Center for Disease Control and Prevention, Hangzhou, Zhejiang, China
| | - Lele Yu
- Xixi Hospital of Hangzhou, Hangzhou, Zhejiang, 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 Limited, Hong Kong Science Park, New Territories, Hong Kong Special Administrative Region, China
| | - Qingxin Kong
- Hangzhou Center for Disease Control and Prevention, Hangzhou, Zhejiang, China,Corresponding authors:
| | - 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 Limited, Hong Kong Science Park, New Territories, Hong Kong Special Administrative Region, China
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24
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Wang C, Huang X, Lau EHY, Cowling BJ, Tsang TK. Association Between Population-Level Factors and Household Secondary Attack Rate of SARS-CoV-2: A Systematic Review and Meta-analysis. Open Forum Infect Dis 2023; 10:ofac676. [PMID: 36655186 PMCID: PMC9835764 DOI: 10.1093/ofid/ofac676] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2022] [Accepted: 12/14/2022] [Indexed: 12/23/2022] Open
Abstract
Background Accurate estimation of household secondary attack rate (SAR) is crucial to understand the transmissibility of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The impact of population-level factors, such as transmission intensity in the community, on SAR estimates is rarely explored. Methods In this study, we included articles with original data to compute the household SAR. To determine the impact of transmission intensity in the community on household SAR estimates, we explored the association between SAR estimates and the incidence rate of cases by country during the study period. Results We identified 163 studies to extract data on SARs from 326 031 cases and 2 009 859 household contacts. The correlation between the incidence rate of cases during the study period and SAR estimates was 0.37 (95% CI, 0.24-0.49). We found that doubling the incidence rate of cases during the study period was associated with a 1.2% (95% CI, 0.5%-1.8%) higher household SAR. Conclusions Our findings suggest that the incidence rate of cases during the study period is associated with higher SAR. Ignoring this factor may overestimate SARs, especially for regions with high incidences, which further impacts control policies and epidemiological characterization of emerging variants.
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Affiliation(s)
- Can Wang
- 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
| | - Xiaotong Huang
- 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
| | - 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 Limited, Hong Kong Science and Technology Park, New Territories, 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 Limited, Hong Kong Science and Technology Park, New Territories, Hong Kong Special Administrative Region, China
| | - Tim K Tsang
- 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 Limited, Hong Kong Science and Technology Park, New Territories, Hong Kong Special Administrative Region, China
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25
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Choi G, Lim AY, Choi S, Park K, Lee SY, Kim JH. Viral shedding patterns of symptomatic SARS-CoV-2 infections by periods of variant predominance and vaccination status in Gyeonggi Province, Korea. Epidemiol Health 2022; 45:e2023008. [PMID: 36596734 PMCID: PMC10581894 DOI: 10.4178/epih.e2023008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Accepted: 12/21/2022] [Indexed: 01/01/2023] Open
Abstract
OBJECTIVES We compared the viral cycle threshold (Ct) values of infected patients to better understand viral kinetics by vaccination status during different periods of variant predominance in Gyeonggi Province, Korea. METHODS We obtained case-specific data from the coronavirus disease 2019 (COVID-19) surveillance system, Gyeonggi in-depth epidemiological report system, and Health Insurance Review & Assessment Service from January 2020 to January 2022. We defined periods of variant predominance and explored Ct values by analyzing viral sequencing test results. Using a generalized additive model, we performed a nonlinear regression analysis to determine viral kinetics over time. RESULTS Cases in the Delta variant's period of predominance had higher viral shedding patterns than cases in other periods. The temporal change of viral shedding did not vary by vaccination status in the Omicron-predominant period, but viral shedding decreased in patients who had completed their third vaccination in the Delta-predominant period. During the Delta-predominant and Omicron-predominant periods, the time from symptom onset to peak viral shedding based on the E gene was approximately 2.4 days (95% confidence interval [CI], 2.2 to 2.5) and 2.1 days (95% CI, 2.0 to 2.1), respectively. CONCLUSIONS In one-time tests conducted to diagnose COVID-19 in a large population, although no adjustment for individual characteristics was conducted, it was confirmed that viral shedding differed by the predominant strain and vaccination history. These results show the value of utilizing hundreds of thousands of test data produced at COVID-19 screening test centers.
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Affiliation(s)
- Gawon Choi
- Gyeonggi Infectious Disease Control Center, Health Bureau, Gyeonggi Provincial Government, Suwon, Korea
| | - Ah-Young Lim
- Department of Social and Preventive Medicine, Sungkyunkwan University School of Medicine, Suwon, Korea
| | - Sojin Choi
- Gyeonggi Infectious Disease Control Center, Health Bureau, Gyeonggi Provincial Government, Suwon, Korea
| | - Kunhee Park
- Gyeonggi Infectious Disease Control Center, Health Bureau, Gyeonggi Provincial Government, Suwon, Korea
| | - Soon Young Lee
- Department of Preventive Medicine and Public Health, Ajou University School of Medicine, Suwon, Korea
| | - Jong-Hun Kim
- Gyeonggi Infectious Disease Control Center, Health Bureau, Gyeonggi Provincial Government, Suwon, Korea
- Department of Social and Preventive Medicine, Sungkyunkwan University School of Medicine, Suwon, Korea
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Zhang Q, Zhang M, Hu J, He G, Zhou Y, Chen X, Zhuang Y, Rong Z, Yin L, Zhao J, Huang Z, Zeng W, Li X, Zhu Z, Tang Y, Quan Y, Li Y, Zhang L, Fu D, Li Y, Xiao J. Spatial-temporal clustering of an outbreak of SARS-CoV-2 Delta VOC in Guangzhou, China in 2021. Front Public Health 2022; 10:1050096. [PMID: 36568757 PMCID: PMC9780675 DOI: 10.3389/fpubh.2022.1050096] [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: 09/21/2022] [Accepted: 11/23/2022] [Indexed: 12/13/2022] Open
Abstract
Background In May 2021, the SARS-CoV-2 Delta variant led to the first local outbreak in China in Guangzhou City. We explored the epidemiological characteristics and spatial-temporal clustering of this outbreak. Methods Based on the 153 cases in the SARS-CoV-2 Delta variant outbreak, the Knox test was used to analyze the spatial-temporal clustering of the outbreak. We further explored the spatial-temporal clustering by gender and age groups, as well as compared the changes of clustering strength (S) value between the two outbreaks in Guangzhou. Results The result of the Knox analysis showed that the areas at short distances and brief periods presented a relatively high risk. The strength of clustering of male-male pairs was higher. Age groups showed that clustering was concentrated in cases aged ≤ 18 years matched to 18-59 years and cases aged 60+ years. The strength of clustering of the outbreak declined after the implementation of public health measures. The change of strength of clustering at time intervals of 1-5 days decreased greater in 2021 (S = 129.19, change rate 38.87%) than that in 2020 (S = 83.81, change rate 30.02%). Conclusions The outbreak of SARS-CoV-2 Delta VOC in Guangzhou has obvious spatial-temporal clustering. The timely intervention measures are essential role to contain this outbreak of high transmission.
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Affiliation(s)
- Qian Zhang
- School of Public Health, Guangdong Pharmaceutical University, Guangzhou, China,Guangdong Provincial Institute of Public Health, Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, China
| | - Meng Zhang
- Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, China,Guangdong Workstation for Emerging Infectious Disease Control and Prevention, Guangzhou, China
| | - Jianxiong Hu
- Guangdong Provincial Institute of Public Health, Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, China
| | - Guanhao He
- Guangdong Provincial Institute of Public Health, Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, China
| | - Yan Zhou
- Guangdong Provincial Institute of Public Health, Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, China,School of Public Health, Southern Medical University, Guangzhou, China
| | - Xuguang Chen
- Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, China,Guangdong Workstation for Emerging Infectious Disease Control and Prevention, Guangzhou, China
| | - Yali Zhuang
- Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, China,Guangdong Workstation for Emerging Infectious Disease Control and Prevention, Guangzhou, China
| | - Zuhua Rong
- Guangdong Provincial Institute of Public Health, Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, China
| | - Lihua Yin
- Guangdong Provincial Institute of Public Health, Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, China
| | - Jianguo Zhao
- Guangdong Provincial Institute of Public Health, Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, China
| | - Zitong Huang
- Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, China,School of Public Health, Sun Yat-sen University, Guangzhou, China
| | - Weilin Zeng
- Guangdong Provincial Institute of Public Health, Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, China
| | - Xing Li
- Guangdong Provincial Institute of Public Health, Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, China
| | - Zhihua Zhu
- Guangdong Provincial Institute of Public Health, Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, China
| | - Yerong Tang
- School of Public Health, Sun Yat-sen University, Guangzhou, China,Arboviral Disease Prevention Department, Yunnan Institute of Parasitic Diseases, Puer, China
| | - Yi Quan
- Guangdong Provincial Institute of Public Health, Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, China,School of Public Health, Southern Medical University, Guangzhou, China
| | - Yihan Li
- Guangdong Provincial Institute of Public Health, Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, China,School of Public Health, Southern Medical University, Guangzhou, China
| | - Li Zhang
- Guangdong Provincial Institute of Public Health, Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, China,School of Public Health, Southern Medical University, Guangzhou, China
| | - Di Fu
- Department of Public Health and Preventive Medicine, School of Medicine, Jinan University, Guangzhou, China
| | - Yan Li
- Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, China,Guangdong Workstation for Emerging Infectious Disease Control and Prevention, Guangzhou, China,*Correspondence: Yan Li
| | - Jianpeng Xiao
- Guangdong Provincial Institute of Public Health, Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, China,Jianpeng Xiao
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27
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Altintop SE, Unalan-Altintop T, Cihangiroglu M, Onarer P, Milletli-Sezgin F, Gozukara M, Gozukara B, Zengin E. COVID-19 in elderly: Correlations of viral load, clinical course, laboratory parameters, among patients vaccinated with CoronaVac. Acta Microbiol Immunol Hung 2022; 69:277-282. [PMID: 36370367 DOI: 10.1556/030.2022.01849] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2022] [Accepted: 10/21/2022] [Indexed: 11/13/2022]
Abstract
SARS-CoV-2 virus was initially identified in Wuhan, China, in December 2019 and a global pandemic was declared in March 2020 by World Health Organization. COVID-19 disease is characterized with severe pneumonia and hypoxemia, especially in the elderly population. The elderly population was primarily vaccinated with CoronaVac, which is a whole virion inactivated vaccine (Sinovac Biotech, China) in Turkey. This study aimed to investigate the association of viral load and laboratory parameters with the severity of the disease and vaccination status in elderly (older than 60 years old) COVID-19 patients. The age range of the patients was 61-97 years old with a mean of 71.80. Vaccinated patients had a lower viral load (P = 0.253) in nasopharyngeal swabs during breakthrough COVID-19 infection compared to unvaccinated ones and were hospitalized for a shorter period of time in hospital wards (P = 0.035). A lower number of patients were vaccinated in both moderate (n = 33, 29.20%) and severe/critical group (n = 46, 34.07%) (P = 0.412). Only 17 (32.08%) vaccinated patients were hospitalized in an intensive care unit (ICU), whereas 36 (67.92%) of the ICU patients were unvaccinated (P = 0.931). Severe/critical patients had higher c-reactive protein (CRP), platelet-to-lymphocyte ratio (PLR), fibrinogen, ferritin, and lactate dehydrogenase (LDH) levels compared to the moderate group on the admission day (P < 0.05). Our study suggested that elderly patients vaccinated with CoronaVac had a shorter stay in hospitals and according to our results CRP, PLR, fibrinogen, ferritin, and LDH levels could be used to determine the severity of the infections.
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Affiliation(s)
| | - Tugce Unalan-Altintop
- 2Department of Medical Microbiology, Amasya University Sabuncuoglu Serefeddin Research and Training Hospital, Amasya, Turkey
| | - Mustafa Cihangiroglu
- 3Department of Infectious Disease and Clinical Microbiology, Faculty of Medicine, Amasya University, Amasya, Turkey
| | - Pelin Onarer
- 2Department of Medical Microbiology, Amasya University Sabuncuoglu Serefeddin Research and Training Hospital, Amasya, Turkey
| | | | - Melih Gozukara
- 5Amasya Provincial Directorate of Health, Amasya, Turkey
| | - Bilge Gozukara
- 1Suluova State Hospital, Department of Internal Medicine, Amasya, Turkey
| | - Erman Zengin
- 5Amasya Provincial Directorate of Health, Amasya, Turkey
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Safranek CW, Scheinker D. A computer modeling method to analyze rideshare data for the surveillance of novel strains of SARS-CoV-2. Ann Epidemiol 2022; 76:136-142. [PMID: 36087658 PMCID: PMC9452418 DOI: 10.1016/j.annepidem.2022.08.051] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2021] [Revised: 07/15/2022] [Accepted: 08/29/2022] [Indexed: 12/14/2022]
Abstract
PURPOSE No method is available to systematically study SARS-CoV-2 transmission dynamics using the data that rideshare companies share with government agencies. We developed a proof-of-concept method for the analysis of SARS-CoV-2 transmissions between rideshare passengers and drivers. METHOD To assess whether this method could enable hypothesis testing about SARS-CoV-2, we repeated ten 200-day agent-based simulations of SARS-CoV-2 propagation within the Los Angeles County rideshare network. Assuming data access for 25% of infections, we estimated an epidemiologist's ability to analyze the observable infection patterns to correctly identify a baseline viral variant A, as opposed to viral variant A with mask use (50% reduction in viral particle exchange), or a more infectious viral variant B (300% higher cumulative viral load). RESULTS Simulations had an average of 190,387 potentially infectious rideshare interactions, resulting in 409 average diagnosed infections. Comparison of the number of observed and expected passenger-to-driver infections under each hypothesis demonstrated our method's ability to consistently discern large infectivity differences (viral variant A vs. viral variant B) given partial data from one large city, and to discern smaller infectivity differences (viral variant A vs. viral variant A with masks) given partial data aggregated across multiple cities. CONCLUSIONS This novel statistical method suggests that, for the present and subsequent pandemics, government-facilitated analysis of rideshare data combined with diagnosis records may augment efforts to better understand viral transmission dynamics and to measure changes in infectivity associated with nonpharmaceutical interventions and emergent viral strains.
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Affiliation(s)
- Conrad W. Safranek
- Department of Biology, Computational Biology, Stanford University, CA,Department of Management Science and Engineering, Stanford University School of Engineering, CA
| | - David Scheinker
- Department of Management Science and Engineering, Stanford University School of Engineering, CA; Department of Pediatrics, Stanford University School of Medicine, CA; Clinical Excellence Research Center, Stanford University School of Medicine, CA.
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29
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Wang J, Ma T, Ding S, Xu K, Zhang M, Zhang Z, Dai Q, Tao S, Wang H, Cheng X, He M, Du X, Feng Z, Yang H, Wang R, Xie C, Xu Y, Liu L, Chen X, Li C, Wu W, Ye S, Yang S, Fan H, Zhou N, Ding J. Dynamic characteristics of a COVID-19 outbreak in Nanjing, Jiangsu province, China. Front Public Health 2022; 10:933075. [PMID: 36483256 PMCID: PMC9723226 DOI: 10.3389/fpubh.2022.933075] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2022] [Accepted: 09/21/2022] [Indexed: 12/13/2022] Open
Abstract
Objectives Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) lineage B.1.617.2 (also named the Delta variant) was declared as a variant of concern by the World Health Organization (WHO). This study aimed to describe the outbreak that occurred in Nanjing city triggered by the Delta variant through the epidemiological parameters and to understand the evolving epidemiology of the Delta variant. Methods We collected the data of all COVID-19 cases during the outbreak from 20 July 2021 to 24 August 2021 and estimated the distribution of serial interval, basic and time-dependent reproduction numbers (R0 and Rt), and household secondary attack rate (SAR). We also analyzed the cycle threshold (Ct) values of infections. Results A total of 235 cases have been confirmed. The mean value of serial interval was estimated to be 4.79 days with the Weibull distribution. The R0 was 3.73 [95% confidence interval (CI), 2.66-5.15] as estimated by the exponential growth (EG) method. The Rt decreased from 4.36 on 20 July 2021 to below 1 on 1 August 2021 as estimated by the Bayesian approach. We estimated the household SAR as 27.35% (95% CI, 22.04-33.39%), and the median Ct value of open reading frame 1ab (ORF1ab) genes and nucleocapsid protein (N) genes as 25.25 [interquartile range (IQR), 20.53-29.50] and 23.85 (IQR, 18.70-28.70), respectively. Conclusions The Delta variant is more aggressive and transmissible than the original virus types, so continuous non-pharmaceutical interventions are still needed.
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Affiliation(s)
- Junjun Wang
- Nanjing Municipal Center for Disease Control and Prevention, Nanjing, China,Chinese Field Epidemiology Training Program, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Tao Ma
- Department of Acute Infectious Diseases Control and Prevention, Nanjing Municipal Center for Disease Control and Prevention, Nanjing, China
| | - Songning Ding
- Department of Acute Infectious Diseases Control and Prevention, Nanjing Municipal Center for Disease Control and Prevention, Nanjing, China
| | - Ke Xu
- Department of Acute Infectious Diseases Control and Prevention, Jiangsu Provincial Center for Disease Control and Prevention, Nanjing, China
| | - Min Zhang
- Nanjing Municipal Center for Disease Control and Prevention, Nanjing, China
| | - Zhong Zhang
- Nanjing Municipal Center for Disease Control and Prevention, Nanjing, China
| | - Qigang Dai
- Department of Acute Infectious Diseases Control and Prevention, Jiangsu Provincial Center for Disease Control and Prevention, Nanjing, China
| | - Shilong Tao
- Jiangning District Center for Disease Control and Prevention, Nanjing, China
| | - Hengxue Wang
- Nanjing Municipal Center for Disease Control and Prevention, Nanjing, China
| | - Xiaoqing Cheng
- Chinese Field Epidemiology Training Program, Chinese Center for Disease Control and Prevention, Beijing, China,Department of Acute Infectious Diseases Control and Prevention, Jiangsu Provincial Center for Disease Control and Prevention, Nanjing, China
| | - Min He
- Nanjing Municipal Center for Disease Control and Prevention, Nanjing, China
| | - Xuefei Du
- Nanjing Municipal Center for Disease Control and Prevention, Nanjing, China
| | - Zhi Feng
- Jiangning District Center for Disease Control and Prevention, Nanjing, China
| | - Huafeng Yang
- Nanjing Municipal Center for Disease Control and Prevention, Nanjing, China
| | - Rong Wang
- Nanjing Municipal Center for Disease Control and Prevention, Nanjing, China
| | - Chaoyong Xie
- Nanjing Municipal Center for Disease Control and Prevention, Nanjing, China
| | - Yuanyuan Xu
- Nanjing Municipal Center for Disease Control and Prevention, Nanjing, China
| | - Li Liu
- Nanjing Municipal Center for Disease Control and Prevention, Nanjing, China
| | - Xupeng Chen
- Nanjing Municipal Center for Disease Control and Prevention, Nanjing, China
| | - Chen Li
- Nanjing Municipal Center for Disease Control and Prevention, Nanjing, China
| | - Wen Wu
- Nanjing Municipal Center for Disease Control and Prevention, Nanjing, China
| | - Sheng Ye
- Nanjing Municipal Center for Disease Control and Prevention, Nanjing, China
| | - Sheng Yang
- Nanjing Municipal Center for Disease Control and Prevention, Nanjing, China
| | - Huafeng Fan
- Nanjing Municipal Center for Disease Control and Prevention, Nanjing, China
| | - Nan Zhou
- Nanjing Municipal Center for Disease Control and Prevention, Nanjing, China,*Correspondence: Jie Ding
| | - Jie Ding
- Nanjing Municipal Center for Disease Control and Prevention, Nanjing, China,Nan Zhou
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30
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Wang Y, Sun K, Feng Z, Yi L, Wu Y, Liu H, Wang Q, Ajelli M, Viboud C, Yu H. Assessing the feasibility of sustaining SARS-CoV-2 local containment in China in the era of highly transmissible variants. BMC Med 2022; 20:442. [PMID: 36380354 PMCID: PMC9666984 DOI: 10.1186/s12916-022-02640-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/03/2022] [Accepted: 10/26/2022] [Indexed: 11/16/2022] Open
Abstract
BACKGROUND The SARS-CoV-2 containment strategy has been successful in mainland China prior to the emergence of Omicron. However, in the era of highly transmissible variants, whether it is possible for China to sustain a local containment policy and under what conditions China could transition away from it are of paramount importance at the current stage of the pandemic. METHODS We developed a spatially structured, fully stochastic, individual-based SARS-CoV-2 transmission model to evaluate the feasibility of sustaining SARS-CoV-2 local containment in mainland China considering the Omicron variants, China's current immunization level, and nonpharmaceutical interventions (NPIs). We also built a statistical model to estimate the overall disease burden under various hypothetical mitigation scenarios. RESULTS We found that due to high transmissibility, neither Omicron BA.1 nor BA.2 could be contained by China's pre-Omicron NPI strategies which were successful prior to the emergence of the Omicron variants. However, increased intervention intensity, such as enhanced population mobility restrictions and multi-round mass testing, could lead to containment success. We estimated that an acute Omicron epidemic wave in mainland China would result in significant number of deaths if China were to reopen under current vaccine coverage with no antiviral uptake, while increasing vaccination coverage and antiviral uptake could substantially reduce the disease burden. CONCLUSIONS As China's current vaccination has yet to reach high coverage in older populations, NPIs remain essential tools to maintain low levels of infection while building up protective population immunity, ensuring a smooth transition out of the pandemic phase while minimizing the overall disease burden.
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Affiliation(s)
- Yan Wang
- School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China
| | - Kaiyuan Sun
- Division of International Epidemiology and Population Studies, Fogarty International Center, National Institutes of Health, Bethesda, MD, USA
| | - Zhaomin Feng
- Beijing Center for Disease Prevention and Control (CDC), Beijing, China
| | - Lan Yi
- Shanghai Institute of Infectious Disease and Biosecurity, Fudan University, Shanghai, China
| | - Yanpeng Wu
- Shanghai Institute of Infectious Disease and Biosecurity, Fudan University, Shanghai, China
| | - Hengcong Liu
- School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China
| | - Quanyi Wang
- Beijing Center for Disease Prevention and Control (CDC), Beijing, China
| | - Marco Ajelli
- Laboratory of Computational Epidemiology and Public Health, Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomington, IN, USA
| | - Cécile Viboud
- Division of International Epidemiology and Population Studies, Fogarty International Center, National Institutes of Health, Bethesda, MD, USA
| | - Hongjie Yu
- School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China.
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31
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Goswami GG, Labib T. Modeling COVID-19 Transmission Dynamics: A Bibliometric Review. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:14143. [PMID: 36361019 PMCID: PMC9655715 DOI: 10.3390/ijerph192114143] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Revised: 10/15/2022] [Accepted: 10/26/2022] [Indexed: 06/16/2023]
Abstract
A good amount of research has evolved just in three years in COVID-19 transmission, mortality, vaccination, and some socioeconomic studies. A few bibliometric reviews have already been performed in the literature, especially on the broad theme of COVID-19, without any particular area such as transmission, mortality, or vaccination. This paper fills this gap by conducting a bibliometric review on COVID-19 transmission as the first of its kind. The main aim of this study is to conduct a bibliometric review of the literature in the area of COVID-19 transmission dynamics. We have conducted bibliometric analysis using descriptive and network analysis methods to review the literature in this area using RStudio, Openrefine, VOSviewer, and Tableau. We reviewed 1103 articles published in 2020-2022. The result identified the top authors, top disciplines, research patterns, and hotspots and gave us clear directions for classifying research topics in this area. New research areas are rapidly emerging in this area, which needs constant observation by researchers to combat this global epidemic.
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32
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Sararat C, Wangkanai J, Wilasang C, Chantanasaro T, Modchang C. Individual-based modeling reveals that the COVID-19 isolation period can be shortened by community vaccination. Sci Rep 2022; 12:17543. [PMID: 36266440 PMCID: PMC9583066 DOI: 10.1038/s41598-022-21645-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2022] [Accepted: 09/29/2022] [Indexed: 01/19/2023] Open
Abstract
The isolation of infected individuals and quarantine of their contacts are usually employed to mitigate the transmission of SARS-CoV-2. Although 14-day isolation of infected individuals could effectively reduce the risk of subsequent transmission, it also substantially impacts the patient's psychological and emotional well-being. It is, therefore, vital to investigate how the isolation duration could be shortened when effective vaccines are available. Here, an individual-based modeling approach was employed to estimate the likelihood of secondary infections and the likelihood of an outbreak following the isolation of a primary case for a range of isolation periods. Our individual-based model integrated the viral loads and infectiousness profiles of vaccinated and unvaccinated infected individuals. The effects of waning vaccine-induced immunity against infection were also considered. By simulating the transmission of the SARS-CoV-2 Delta (B.1.617.2) variant in a community, we found that in the baseline scenario in which all individuals were unvaccinated and nonpharmaceutical interventions were not used, there was an approximately 3% chance that an unvaccinated individual would lead to at least one secondary infection after being isolated for 14 days, and a sustained chain of transmission could occur with a less than 1% chance. With the outbreak risk equivalent to that of the 14-day isolation in the baseline scenario, we found that the isolation duration could be shortened to 7.33 days (95% CI 6.68-7.98) if 75% of people in the community were fully vaccinated with the BNT162b2 vaccine within the last three months. In the best-case scenario in which all individuals in the community are fully vaccinated, isolation of Delta variant-infected individuals may no longer be necessary. However, to keep the outbreak risk lower than 1%, a booster vaccination may be necessary three months after full vaccination.
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Affiliation(s)
- Chayanin Sararat
- Biophysics Group, Department of Physics, Faculty of Science, Mahidol University, Bangkok, 10400, Thailand
- Center for Disease Modeling, Faculty of Science, Mahidol University, Bangkok, 10400, Thailand
| | - Jidchanok Wangkanai
- Biophysics Group, Department of Physics, Faculty of Science, Mahidol University, Bangkok, 10400, Thailand
- Center for Disease Modeling, Faculty of Science, Mahidol University, Bangkok, 10400, Thailand
| | - Chaiwat Wilasang
- Biophysics Group, Department of Physics, Faculty of Science, Mahidol University, Bangkok, 10400, Thailand
- Center for Disease Modeling, Faculty of Science, Mahidol University, Bangkok, 10400, Thailand
| | - Tanakorn Chantanasaro
- Biophysics Group, Department of Physics, Faculty of Science, Mahidol University, Bangkok, 10400, Thailand
- Center for Disease Modeling, Faculty of Science, Mahidol University, Bangkok, 10400, Thailand
| | - Charin Modchang
- Biophysics Group, Department of Physics, Faculty of Science, Mahidol University, Bangkok, 10400, Thailand.
- Center for Disease Modeling, Faculty of Science, Mahidol University, Bangkok, 10400, Thailand.
- Centre of Excellence in Mathematics, MHESI, Bangkok, 10400, Thailand.
- Thailand Center of Excellence in Physics, Ministry of Higher Education, Science, Research and Innovation, 328 Si Ayutthaya Road, Bangkok, 10400, Thailand.
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33
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Kong L, Duan M, Shi J, Hong J, Zhou X, Yang X, Zhao Z, Huang J, Chen X, Yin Y, Li K, Liu Y, Liu J, Wang X, Zhang P, Xie X, Li F, Chang Z, Zhang Z. Optimization of COVID-19 prevention and control measures during the Beijing 2022 Winter Olympics: a model-based study. Infect Dis Poverty 2022; 11:95. [PMID: 36068625 PMCID: PMC9447360 DOI: 10.1186/s40249-022-01019-2] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2022] [Accepted: 08/24/2022] [Indexed: 11/10/2022] Open
Abstract
Background The continuous mutation of severe acute respiratory syndrome coronavirus 2 has made the coronavirus disease 2019 (COVID-19) pandemic complicated to predict and posed a severe challenge to the Beijing 2022 Winter Olympics and Winter Paralympics held in February and March 2022. Methods During the preparations for the Beijing 2022 Winter Olympics, we established a dynamic model with pulse detection and isolation effect to evaluate the effect of epidemic prevention and control measures such as entry policies, contact reduction, nucleic acid testing, tracking, isolation, and health monitoring in a closed-loop management environment, by simulating the transmission dynamics in assumed scenarios. We also compared the importance of each parameter in the combination of intervention measures through sensitivity analysis. Results At the assumed baseline levels, the peak of the epidemic reached on the 57th day. During the simulation period (100 days), 13,382 people infected COVID-19. The mean and peak values of hospitalized cases were 2650 and 6746, respectively. The simulation and sensitivity analysis showed that: (1) the most important measures to stop COVID-19 transmission during the event were daily nucleic acid testing, reducing contact among people, and daily health monitoring, with cumulative infections at 0.04%, 0.14%, and 14.92% of baseline levels, respectively (2) strictly implementing the entry policy and reducing the number of cases entering the closed-loop system could delay the peak of the epidemic by 9 days and provide time for medical resources to be mobilized; (3) the risk of environmental transmission was low. Conclusions Comprehensive measures under certain scenarios such as reducing contact, nucleic acid testing, health monitoring, and timely tracking and isolation could effectively prevent virus transmission. Our research results provided an important reference for formulating prevention and control measures during the Winter Olympics, and no epidemic spread in the closed-loop during the games indirectly proved the rationality of our research results. Graphical Abstract ![]()
Supplementary Information The online version contains supplementary material available at 10.1186/s40249-022-01019-2.
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Affiliation(s)
- Lingcai Kong
- Department of Mathematics and Physics, North China Electric Power University, Baoding, 071003, China.,Hebei Key Laboratory of Physics and Energy Technology, North China Electric Power University, Baoding, 071003, China
| | - Mengwei Duan
- Department of Mathematics and Physics, North China Electric Power University, Baoding, 071003, China.,Hebei Key Laboratory of Physics and Energy Technology, North China Electric Power University, Baoding, 071003, China
| | - Jin Shi
- Department of Epidemiology and Health Statistics, Fudan University, Shanghai, 200032, China
| | - Jie Hong
- Department of Epidemiology and Health Statistics, Fudan University, Shanghai, 200032, China
| | - Xuan Zhou
- Department of Mathematics and Physics, North China Electric Power University, Baoding, 071003, China.,Hebei Key Laboratory of Physics and Energy Technology, North China Electric Power University, Baoding, 071003, China
| | - Xinyi Yang
- Department of Mathematics and Physics, North China Electric Power University, Baoding, 071003, China.,Hebei Key Laboratory of Physics and Energy Technology, North China Electric Power University, Baoding, 071003, China
| | - Zheng Zhao
- Department of Epidemiology and Health Statistics, Fudan University, Shanghai, 200032, China
| | - Jiaqi Huang
- Department of Epidemiology and Health Statistics, Fudan University, Shanghai, 200032, China
| | - Xi Chen
- Department of Epidemiology and Health Statistics, Fudan University, Shanghai, 200032, China
| | - Yun Yin
- Department of Epidemiology and Health Statistics, Fudan University, Shanghai, 200032, China
| | - Ke Li
- Department of Epidemiology and Health Statistics, Fudan University, Shanghai, 200032, China
| | - Yuanhua Liu
- Department of Epidemiology and Health Statistics, Fudan University, Shanghai, 200032, China
| | - Jinggang Liu
- Department of Mathematics and Physics, North China Electric Power University, Baoding, 071003, China.,Hebei Key Laboratory of Physics and Energy Technology, North China Electric Power University, Baoding, 071003, China
| | - Xiaozhe Wang
- Department of Mathematics and Physics, North China Electric Power University, Baoding, 071003, China.,Hebei Key Laboratory of Physics and Energy Technology, North China Electric Power University, Baoding, 071003, China
| | - Po Zhang
- Department of Mathematics and Physics, North China Electric Power University, Baoding, 071003, China.,Hebei Key Laboratory of Physics and Energy Technology, North China Electric Power University, Baoding, 071003, China
| | - Xiyang Xie
- Department of Mathematics and Physics, North China Electric Power University, Baoding, 071003, China.,Hebei Key Laboratory of Physics and Energy Technology, North China Electric Power University, Baoding, 071003, China
| | - Fei Li
- Department of Power Engineering, North China Electric Power University, Baoding, 071003, China
| | - Zhaorui Chang
- Division of Infectious Disease, Key Laboratory of Surveillance and Early-Warning On Infectious Disease, Chinese Center for Disease Control and Prevention, Beijing, China.
| | - Zhijie Zhang
- Department of Epidemiology and Health Statistics, Fudan University, Shanghai, 200032, China.
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Wang J, Yang M, Lu L, Shao W. Does the "Delta Variant" affect the nonlinear dynamic characteristics of SARS-CoV-2 transmission? CHAOS, SOLITONS, AND FRACTALS 2022; 162:112382. [PMID: 35782523 PMCID: PMC9240093 DOI: 10.1016/j.chaos.2022.112382] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/17/2022] [Revised: 06/14/2022] [Accepted: 06/22/2022] [Indexed: 06/15/2023]
Abstract
In this paper, we analyzed the difference of nonlinear dynamic characteristics of SARS-CoV-2 transmission caused by 'Delta Variant'. We selected the daily new diagnostic data of SARS-CoV-2 from 15 countries. Four different kinds of complexity metrics such as Kolmogorov complexity, Higuchi's Hurst exponent, Shannon entropy, and multifractal degrees were selected to explore the features of information content, persistence, randomness, multifractal complexity. Afterwards, Student's t-tests were performed to assess the presence of differences of these nonlinear dynamic characteristics for periods before and after "Delta Variant" appearance. The results of two-tailed Student's t-test showed that for all the nonlinear dynamic characteristics, the null hypothesis of equality of mean values were strongly rejected for the two periods. In addition, by one-tailed Student's t-test, we concluded that time series in "Delta period" exhibit higher value of Kolmogorov complexity and Shannon entropy, indicating a higher level of information content and randomness. On the other hand, the Higuchi's Hurst exponent in "Delta period" was lower, which showed the weaker persistent in this period. Moreover, the multifractal specturm width after "Delta" emergence were reduced, representing a more stable multifractality. The sources for the formation of multifractal features are also investigated.
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Affiliation(s)
- Jian Wang
- School of Mathematics and Statistics, Nanjing University of Information Science and Technology, Nanjing 210044, China
| | - Mengdie Yang
- School of Mathematics and Statistics, Nanjing University of Information Science and Technology, Nanjing 210044, China
| | - Lin Lu
- Educational Economics and Management, University of International Business and Economics, Beijing 100029, China
| | - Wei Shao
- School of Economics, Nanjing University of Finance and Economics, Nanjing 210023, China
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35
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Garcia-Knight M, Anglin K, Tassetto M, Lu S, Zhang A, Goldberg SA, Catching A, Davidson MC, Shak JR, Romero M, Pineda-Ramirez J, Diaz-Sanchez R, Rugart P, Donohue K, Massachi J, Sans HM, Djomaleu M, Mathur S, Servellita V, McIlwain D, Gaudiliere B, Chen J, Martinez EO, Tavs JM, Bronstone G, Weiss J, Watson JT, Briggs-Hagen M, Abedi GR, Rutherford GW, Deeks SG, Chiu C, Saydah S, Peluso MJ, Midgley CM, Martin JN, Andino R, Kelly JD. Infectious viral shedding of SARS-CoV-2 Delta following vaccination: A longitudinal cohort study. PLoS Pathog 2022; 18:e1010802. [PMID: 36095030 PMCID: PMC9499220 DOI: 10.1371/journal.ppat.1010802] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Revised: 09/22/2022] [Accepted: 08/05/2022] [Indexed: 11/18/2022] Open
Abstract
The impact of vaccination on SARS-CoV-2 infectiousness is not well understood. We compared longitudinal viral shedding dynamics in unvaccinated and fully vaccinated adults. SARS-CoV-2-infected adults were enrolled within 5 days of symptom onset and nasal specimens were self-collected daily for two weeks and intermittently for an additional two weeks. SARS-CoV-2 RNA load and infectious virus were analyzed relative to symptom onset stratified by vaccination status. We tested 1080 nasal specimens from 52 unvaccinated adults enrolled in the pre-Delta period and 32 fully vaccinated adults with predominantly Delta infections. While we observed no differences by vaccination status in maximum RNA levels, maximum infectious titers and the median duration of viral RNA shedding, the rate of decay from the maximum RNA load was faster among vaccinated; maximum infectious titers and maximum RNA levels were highly correlated. Furthermore, amongst participants with infectious virus, median duration of infectious virus detection was reduced from 7.5 days (IQR: 6.0-9.0) in unvaccinated participants to 6 days (IQR: 5.0-8.0) in those vaccinated (P = 0.02). Accordingly, the odds of shedding infectious virus from days 6 to 12 post-onset were lower among vaccinated participants than unvaccinated participants (OR 0.42 95% CI 0.19-0.89). These results indicate that vaccination had reduced the probability of shedding infectious virus after 5 days from symptom onset.
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Affiliation(s)
- Miguel Garcia-Knight
- Department of Microbiology and Immunology, UCSF, California, United States of America
| | - Khamal Anglin
- Department of Epidemiology and Biostatistics, University of California, San Francisco, California, United States of America
- Institute for Global Health Sciences, University of California, San Francisco, California, United States of America
| | - Michel Tassetto
- Department of Microbiology and Immunology, UCSF, California, United States of America
| | - Scott Lu
- Department of Epidemiology and Biostatistics, University of California, San Francisco, California, United States of America
- Institute for Global Health Sciences, University of California, San Francisco, California, United States of America
| | - Amethyst Zhang
- Department of Microbiology and Immunology, UCSF, California, United States of America
| | - Sarah A. Goldberg
- Department of Epidemiology and Biostatistics, University of California, San Francisco, California, United States of America
- Institute for Global Health Sciences, University of California, San Francisco, California, United States of America
| | - Adam Catching
- Department of Microbiology and Immunology, UCSF, California, United States of America
| | - Michelle C. Davidson
- School of Medicine, University of California, San Francisco, California, United States of America
| | - Joshua R. Shak
- School of Medicine, University of California, San Francisco, California, United States of America
- San Francisco VA Medical Center, San Francisco, California, United States of America
| | - Mariela Romero
- Department of Epidemiology and Biostatistics, University of California, San Francisco, California, United States of America
- Institute for Global Health Sciences, University of California, San Francisco, California, United States of America
| | - Jesus Pineda-Ramirez
- Department of Epidemiology and Biostatistics, University of California, San Francisco, California, United States of America
- Institute for Global Health Sciences, University of California, San Francisco, California, United States of America
| | - Ruth Diaz-Sanchez
- Department of Epidemiology and Biostatistics, University of California, San Francisco, California, United States of America
- Institute for Global Health Sciences, University of California, San Francisco, California, United States of America
| | - Paulina Rugart
- Department of Epidemiology and Biostatistics, University of California, San Francisco, California, United States of America
- Institute for Global Health Sciences, University of California, San Francisco, California, United States of America
| | - Kevin Donohue
- School of Medicine, University of California, San Francisco, California, United States of America
| | - Jonathan Massachi
- School of Medicine, University of California, San Francisco, California, United States of America
| | - Hannah M. Sans
- School of Medicine, University of California, San Francisco, California, United States of America
| | - Manuella Djomaleu
- School of Medicine, University of California, San Francisco, California, United States of America
| | - Sujata Mathur
- Department of Epidemiology and Biostatistics, University of California, San Francisco, California, United States of America
- Institute for Global Health Sciences, University of California, San Francisco, California, United States of America
| | - Venice Servellita
- Division of Infectious Diseases, UCSF, California, United States of America
| | - David McIlwain
- Department of Microbiology and Immunology, Stanford, California, United States of America
| | - Brice Gaudiliere
- Department of Microbiology and Immunology, Stanford, California, United States of America
| | - Jessica Chen
- Department of Epidemiology and Biostatistics, University of California, San Francisco, California, United States of America
- Institute for Global Health Sciences, University of California, San Francisco, California, United States of America
| | - Enrique O. Martinez
- Department of Epidemiology and Biostatistics, University of California, San Francisco, California, United States of America
- Institute for Global Health Sciences, University of California, San Francisco, California, United States of America
| | - Jacqueline M. Tavs
- Department of Epidemiology and Biostatistics, University of California, San Francisco, California, United States of America
- Institute for Global Health Sciences, University of California, San Francisco, California, United States of America
| | - Grace Bronstone
- Department of Epidemiology and Biostatistics, University of California, San Francisco, California, United States of America
- Institute for Global Health Sciences, University of California, San Francisco, California, United States of America
| | - Jacob Weiss
- Department of Epidemiology and Biostatistics, University of California, San Francisco, California, United States of America
- Institute for Global Health Sciences, University of California, San Francisco, California, United States of America
| | - John T. Watson
- Respiratory Viruses Branch, Division of Viral Diseases, CDC, Atlanta, Georgia, United States of America
| | - Melissa Briggs-Hagen
- Respiratory Viruses Branch, Division of Viral Diseases, CDC, Atlanta, Georgia, United States of America
| | - Glen R. Abedi
- Respiratory Viruses Branch, Division of Viral Diseases, CDC, Atlanta, Georgia, United States of America
| | - George W. Rutherford
- Department of Epidemiology and Biostatistics, University of California, San Francisco, California, United States of America
- Institute for Global Health Sciences, University of California, San Francisco, California, United States of America
| | - Steven G. Deeks
- Division of HIV, Infectious Disease, and Global Medicine, UCSF, California, United States of America
| | - Charles Chiu
- Division of Infectious Diseases, UCSF, California, United States of America
| | - Sharon Saydah
- Respiratory Viruses Branch, Division of Viral Diseases, CDC, Atlanta, Georgia, United States of America
| | - Michael J. Peluso
- Division of HIV, Infectious Disease, and Global Medicine, UCSF, California, United States of America
| | - Claire M. Midgley
- Respiratory Viruses Branch, Division of Viral Diseases, CDC, Atlanta, Georgia, United States of America
| | - Jeffrey N. Martin
- Department of Epidemiology and Biostatistics, University of California, San Francisco, California, United States of America
| | - Raul Andino
- Department of Microbiology and Immunology, UCSF, California, United States of America
| | - J. Daniel Kelly
- Department of Epidemiology and Biostatistics, University of California, San Francisco, California, United States of America
- Institute for Global Health Sciences, University of California, San Francisco, California, United States of America
- San Francisco VA Medical Center, San Francisco, California, United States of America
- F.I. Proctor Foundation, University of California, San Francisco, California, United States of America
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36
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Risk Factors for Slow Viral Decline in COVID-19 Patients during the 2022 Omicron Wave. Viruses 2022; 14:v14081714. [PMID: 36016336 PMCID: PMC9412339 DOI: 10.3390/v14081714] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2022] [Revised: 07/31/2022] [Accepted: 08/02/2022] [Indexed: 02/01/2023] Open
Abstract
Formulating termination of isolation (de-isolation) policies requires up-to-date knowledge about viral shedding dynamics. However, current de-isolation policies are largely based on viral load data obtained before the emergence of Omicron variant. In this retrospective cohort study involving adult patients hospitalised for COVID-19 between January and February 2022, we sought to determine SARS-CoV-2 viral shedding kinetics and to investigate the risk factors associated with slow viral decline during the 2022 Omicron wave. A total of 104 patients were included. The viral load was highest (Ct value was lowest) on days 1 post-symptom-onset (PSO) and gradually declined. Older age, hypertension, hyperlipidaemia and chronic kidney disease were associated with slow viral decline in the univariate analysis on both day 7 and day 10 PSO, while incomplete or no vaccination was associated with slow viral decline on day 7 PSO only. However, older age was the only risk factor that remained statistically significant in the multivariate analysis. In conclusion, older age is an independent risk factor associated with slow viral decline in this study conducted during the Omicron-dominant 2022 COVID-19 wave. Transmission-based precaution guidelines should take age into consideration when determining the timing of de-isolation.
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37
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Liu Y, Zhao S, Ryu S, Ran J, Fan J, He D. Estimating the incubation period of SARS-CoV-2 Omicron BA.1 variant in comparison with that during the Delta variant dominance in South Korea. One Health 2022; 15:100425. [PMID: 35942477 PMCID: PMC9349028 DOI: 10.1016/j.onehlt.2022.100425] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Revised: 08/01/2022] [Accepted: 08/01/2022] [Indexed: 11/03/2022] Open
Abstract
Based on exposure history and symptom onset of 22 Omicron BA.1 cases in South Korea from November to December 2021, we estimated mean incubation period of 3.5 days (95% CI: 2.5, 3.8), and then compared to that of 6.5 days (95% CI: 5.3, 7.7) for 64 cases during Delta variants' dominance in June 2021. For Omicron BA.1 variants, we found that 95% of symptomatic cases developed clinical conditions within 6.0 days (95% CI: 4.3, 6.6) after exposure. Thus, a shorter quarantine period may be considered based on symptoms, or similarly laboratory testing, when Omicron BA.1 variants are circulating.
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38
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Ke R, Martinez PP, Smith RL, Gibson LL, Achenbach CJ, McFall S, Qi C, Jacob J, Dembele E, Bundy C, Simons LM, Ozer EA, Hultquist JF, Lorenzo-Redondo R, Opdycke AK, Hawkins C, Murphy RL, Mirza A, Conte M, Gallagher N, Luo CH, Jarrett J, Conte A, Zhou R, Farjo M, Rendon G, Fields CJ, Wang L, Fredrickson R, Baughman ME, Chiu KK, Choi H, Scardina KR, Owens AN, Broach J, Barton B, Lazar P, Robinson ML, Mostafa HH, Manabe YC, Pekosz A, McManus DD, Brooke CB. Longitudinal Analysis of SARS-CoV-2 Vaccine Breakthrough Infections Reveals Limited Infectious Virus Shedding and Restricted Tissue Distribution. Open Forum Infect Dis 2022; 9:ofac192. [PMID: 35791353 PMCID: PMC9047214 DOI: 10.1093/ofid/ofac192] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2022] [Accepted: 04/07/2022] [Indexed: 02/07/2023] Open
Abstract
Background The global effort to vaccinate people against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) during an ongoing pandemic has raised questions about how vaccine breakthrough infections compare with infections in immunologically naive individuals and the potential for vaccinated individuals to transmit the virus. Methods We examined viral dynamics and infectious virus shedding through daily longitudinal sampling in 23 adults infected with SARS-CoV-2 at varying stages of vaccination, including 6 fully vaccinated individuals. Results The durations of both infectious virus shedding and symptoms were significantly reduced in vaccinated individuals compared with unvaccinated individuals. We also observed that breakthrough infections are associated with strong tissue compartmentalization and are only detectable in saliva in some cases. Conclusions Vaccination shortens the duration of time of high transmission potential, minimizes symptom duration, and may restrict tissue dissemination.
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Affiliation(s)
- Ruian Ke
- T-6, Theoretical Biology and Biophysics, Los Alamos National Laboratory, Los Alamos, New Mexico, USA
| | - Pamela P Martinez
- Department of Microbiology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
- Department of Statistics, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
| | - Rebecca L Smith
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
- Department of Pathobiology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
- Carle Illinois College of Medicine, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
| | - Laura L Gibson
- Division of Infectious Diseases and Immunology, Departments of Medicine and Pediatrics, University of Massachusetts Medical School, Worcester, Massachusetts, USA
| | - Chad J Achenbach
- Division of Infectious Diseases and Immunology, Departments of Medicine and Pediatrics, University of Massachusetts Medical School, Worcester, Massachusetts, USA
- Institute for Global Health, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA
- Division of Infectious Diseases, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA
| | - Sally McFall
- Center for Innovation in Point-of-Care Technologies for HIV/AIDS at Northwestern University, Evanston, Illinois, USA
- Department of Biomedical Engineering, Northwestern University, Evanston, Illinois, USA
| | - Chao Qi
- Department of Pathology, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA
| | - Joshua Jacob
- Institute for Global Health, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA
| | - Etienne Dembele
- Institute for Global Health, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA
| | - Camille Bundy
- Institute for Sexual and Gender Minority Health and Wellbeing, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA
| | - Lacy M Simons
- Institute for Global Health, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA
- Division of Infectious Diseases, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA
| | - Egon A Ozer
- Institute for Global Health, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA
- Division of Infectious Diseases, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA
| | - Judd F Hultquist
- Institute for Global Health, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA
- Division of Infectious Diseases, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA
| | - Ramon Lorenzo-Redondo
- Institute for Global Health, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA
- Division of Infectious Diseases, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA
| | - Anita K Opdycke
- Department of Health Service, Northwestern University, Evanston, Illinois, USA
| | - Claudia Hawkins
- Institute for Global Health, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA
- Division of Infectious Diseases, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA
| | - Robert L Murphy
- Institute for Global Health, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA
- Division of Infectious Diseases, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA
| | - Agha Mirza
- Division of Infectious Diseases, Department of Medicine, Johns Hopkins School of Medicine, Baltimore, Maryland, USA
| | - Madison Conte
- Division of Infectious Diseases, Department of Medicine, Johns Hopkins School of Medicine, Baltimore, Maryland, USA
| | - Nicholas Gallagher
- Division of Medical Microbiology, Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Chun Huai Luo
- Division of Medical Microbiology, Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Junko Jarrett
- Division of Medical Microbiology, Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Abigail Conte
- W. Harry Feinstone Department of Molecular Microbiology and Immunology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA
| | - Ruifeng Zhou
- W. Harry Feinstone Department of Molecular Microbiology and Immunology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA
| | - Mireille Farjo
- Department of Microbiology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
| | - Gloria Rendon
- High-Performance Biological Computing at the Roy J. Carver Biotechnology Center, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
| | - Christopher J Fields
- High-Performance Biological Computing at the Roy J. Carver Biotechnology Center, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
| | - Leyi Wang
- Veterinary Diagnostic Laboratory, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
| | - Richard Fredrickson
- Veterinary Diagnostic Laboratory, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
| | - Melinda E Baughman
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
| | - Karen K Chiu
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
| | - Hannah Choi
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
| | - Kevin R Scardina
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
| | - Alyssa N Owens
- Center for Clinical and Translational Research, University of Massachusetts Medical School, Worcester, Massachusetts, USA
| | - John Broach
- Division of Infectious Diseases and Immunology, Departments of Medicine and Pediatrics, University of Massachusetts Medical School, Worcester, Massachusetts, USA
- UMass Memorial Medical Center, Worcester, Massachusetts, USA
- Department of Emergency Medicine, University of Massachusetts Medical School, Worcester, Massachusetts, USA
| | - Bruce Barton
- Division of Biostatistics and Health Services Research, University of Massachusetts Medical School, Worcester, Massachusetts, USA
- Department of Population and Quantitative Health Sciences, University of Massachusetts Medical School, Worcester, Massachusetts, USA
| | - Peter Lazar
- Division of Biostatistics and Health Services Research, University of Massachusetts Medical School, Worcester, Massachusetts, USA
| | - Matthew L Robinson
- Division of Infectious Diseases and Immunology, Departments of Medicine and Pediatrics, University of Massachusetts Medical School, Worcester, Massachusetts, USA
- Division of Infectious Diseases, Department of Medicine, Johns Hopkins School of Medicine, Baltimore, Maryland, USA
| | - Heba H Mostafa
- Division of Medical Microbiology, Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Yukari C Manabe
- Division of Infectious Diseases and Immunology, Departments of Medicine and Pediatrics, University of Massachusetts Medical School, Worcester, Massachusetts, USA
- Division of Infectious Diseases, Department of Medicine, Johns Hopkins School of Medicine, Baltimore, Maryland, USA
- W. Harry Feinstone Department of Molecular Microbiology and Immunology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA
| | - Andrew Pekosz
- W. Harry Feinstone Department of Molecular Microbiology and Immunology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA
| | - David D McManus
- Division of Infectious Diseases and Immunology, Departments of Medicine and Pediatrics, University of Massachusetts Medical School, Worcester, Massachusetts, USA
- Division of Cardiology, University of Massachusetts Medical School, Worcester, Massachusetts, USA
| | - Christopher B Brooke
- Department of Microbiology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
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39
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Shim E, Choi W, Song Y. Clinical Time Delay Distributions of COVID-19 in 2020-2022 in the Republic of Korea: Inferences from a Nationwide Database Analysis. J Clin Med 2022; 11:3269. [PMID: 35743340 PMCID: PMC9225637 DOI: 10.3390/jcm11123269] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2022] [Revised: 06/01/2022] [Accepted: 06/06/2022] [Indexed: 02/07/2023] Open
Abstract
Epidemiological distributions of the coronavirus disease 2019 (COVID-19), including the intervals from symptom onset to diagnosis, reporting, or death, are important for developing effective disease-control strategies. COVID-19 case data (from 19 January 2020 to 10 January 2022) from a national database maintained by the Korea Disease Control and Prevention Agency and the Central Disease Control Headquarters were analyzed. A joint Bayesian subnational model with partial pooling was used and yielded probability distribution models of key epidemiological distributions in Korea. Serial intervals from before and during the Delta variant's predominance were estimated. Although the mean symptom-onset-to-report interval was 3.2 days at the national level, it varied across different regions (2.9-4.0 days). Gamma distribution showed the best fit for the onset-to-death interval (with heterogeneity in age, sex, and comorbidities) and the reporting-to-death interval. Log-normal distribution was optimal for ascertaining the onset-to-diagnosis and onset-to-report intervals. Serial interval (days) was shorter before the Delta variant-induced outbreaks than during the Delta variant's predominance (4.4 vs. 5.2 days), indicating the higher transmission potential of the Delta variant. The identified heterogeneity in region-, age-, sex-, and period-based distributions of the transmission dynamics of COVID-19 will facilitate the development of effective interventions and disease-control strategies.
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Affiliation(s)
- Eunha Shim
- Correspondence: ; Tel.: +82-(2)-820-0416; Fax: +82-(2)-824-4383
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40
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Wang Y, Sun K, Feng Z, Yi L, Wu Y, Liu H, Wang Q, Ajelli M, Viboud C, Yu H. Assessing the feasibility of sustaining SARS-CoV-2 local containment in China in the era of highly transmissible variants. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2022:2022.05.07.22274792. [PMID: 35611330 PMCID: PMC9128785 DOI: 10.1101/2022.05.07.22274792] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
We developed a spatially structured, fully stochastic, individual-based SARS-CoV-2 transmission model to evaluate the feasibility of sustaining SARS-CoV-2 local containment in mainland China considering currently dominant Omicron variants, China's current immunization level, and non-pharmaceutical interventions (NPIs). We also built a statistical model to estimate the overall disease burden under various hypothetical mitigation scenarios. We found that due to high transmissibility, neither Omicron BA.1 or BA.2 could be contained by China's pre-Omicron NPI strategies which were successful prior to the emergence of the Omicron variants. However, increased intervention intensity, such as enhanced population mobility restrictions and multi-round mass testing, could lead to containment success. We estimated that an acute Omicron epidemic wave in mainland China would result in significant number of deaths if China were to reopen under current vaccine coverage with no antiviral uptake, while increasing vaccination coverage and antiviral uptake could substantially reduce the disease burden. As China's current vaccination has yet to reach high coverage in older populations, NPIs remain essential tools to maintain low levels of infection while building up protective population immunity, ensuring a smooth transition out of the pandemic phase while minimizing the overall disease burden.
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Affiliation(s)
- Yan Wang
- School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China
| | - Kaiyuan Sun
- Division of International Epidemiology and Population Studies, Fogarty International Center, National Institutes of Health, Bethesda, MD, USA
| | - Zhaomin Feng
- Beijing Center for Disease Prevention and Control (CDC), China
| | - Lan Yi
- School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China
| | - Yanpeng Wu
- School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China
| | - Hengcong Liu
- School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China
| | - Quanyi Wang
- Beijing Center for Disease Prevention and Control (CDC), China
| | - Marco Ajelli
- Laboratory of Computational Epidemiology and Public Health, Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomington, IN, USA
| | - Cécile Viboud
- Division of International Epidemiology and Population Studies, Fogarty International Center, National Institutes of Health, Bethesda, MD, USA
| | - Hongjie Yu
- School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China
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41
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Using high-resolution contact networks to evaluate SARS-CoV-2 transmission and control in large-scale multi-day events. Nat Commun 2022; 13:1956. [PMID: 35414056 PMCID: PMC9005731 DOI: 10.1038/s41467-022-29522-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2021] [Accepted: 03/11/2022] [Indexed: 11/12/2022] Open
Abstract
The emergence of highly transmissible SARS-CoV-2 variants has created a need to reassess the risk posed by increasing social contacts as countries resume pre-pandemic activities, particularly in the context of resuming large-scale events over multiple days. To examine how social contacts formed in different activity settings influences interventions required to control Delta variant outbreaks, we collected high-resolution data on contacts among passengers and crew on cruise ships and combined the data with network transmission models. We found passengers had a median of 20 (IQR 10-36) unique close contacts per day, and over 60% of their contact episodes were made in dining or sports areas where mask wearing is typically limited. In simulated outbreaks, we found that vaccination coverage and rapid antigen tests had a larger effect than mask mandates alone, indicating the importance of combined interventions against Delta to reduce event risk in the vaccine era.
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42
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Zachreson C, Shearer FM, Price DJ, Lydeamore MJ, McVernon J, McCaw J, Geard N. COVID-19 in low-tolerance border quarantine systems: Impact of the Delta variant of SARS-CoV-2. SCIENCE ADVANCES 2022; 8:eabm3624. [PMID: 35394833 PMCID: PMC8993115 DOI: 10.1126/sciadv.abm3624] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/27/2021] [Accepted: 02/16/2022] [Indexed: 05/25/2023]
Abstract
In controlling transmission of coronavirus disease 2019 (COVID-19), the effectiveness of border quarantine strategies is a key concern for jurisdictions in which the local prevalence of disease and immunity is low. In settings like this such as China, Australia, and New Zealand, rare outbreak events can lead to escalating epidemics and trigger the imposition of large-scale lockdown policies. Here, we develop and apply an individual-based model of COVID-19 to simulate case importation from managed quarantine under various vaccination scenarios. We then use the output of the individual-based model as input to a branching process model to assess community transmission risk. For parameters corresponding to the Delta variant, our results demonstrate that vaccination effectively counteracts the pathogen's increased infectiousness. To prevent outbreaks, heightened vaccination in border quarantine systems must be combined with mass vaccination. The ultimate success of these programs will depend sensitively on the efficacy of vaccines against viral transmission.
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Affiliation(s)
- Cameron Zachreson
- School of Computing and Information Systems, The University of Melbourne, Parkville, Victoria, Australia
| | - Freya M. Shearer
- Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, Australia
| | - David J. Price
- Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, Australia
- Department of Infectious Diseases, The University of Melbourne at the Peter Doherty Institute for Infection and Immunity, Melbourne, Victoria, Australia
| | - Michael J. Lydeamore
- Department of Econometrics and Business Statistics, Monash University, Clayton, Victoria, Australia
| | - Jodie McVernon
- Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, Australia
- Department of Infectious Diseases, The University of Melbourne at the Peter Doherty Institute for Infection and Immunity, Melbourne, Victoria, Australia
- Victorian Infectious Diseases Laboratory Epidemiology Unit, Royal Melbourne Hospital at The Peter Doherty Institute for Infection and Immunity, Melbourne, Victoria, Australia
| | - James McCaw
- Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, Australia
- Department of Infectious Diseases, The University of Melbourne at the Peter Doherty Institute for Infection and Immunity, Melbourne, Victoria, Australia
- School of Mathematics and Statistics, The University of Melbourne, Parkville, Victoria, Australia
| | - Nicholas Geard
- School of Computing and Information Systems, The University of Melbourne, Parkville, Victoria, Australia
- Department of Infectious Diseases, The University of Melbourne at the Peter Doherty Institute for Infection and Immunity, Melbourne, Victoria, Australia
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43
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Kim JM, Rhee JE, Yoo M, Kim HM, Lee NJ, Woo SH, Jo HJ, Kwon D, Lee S, Yoo CK, Kim EJ. Increase in Viral Load in Patients With SARS-CoV-2 Delta Variant Infection in the Republic of Korea. Front Microbiol 2022; 13:819745. [PMID: 35308391 PMCID: PMC8928404 DOI: 10.3389/fmicb.2022.819745] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2021] [Accepted: 01/20/2022] [Indexed: 11/13/2022] Open
Abstract
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spread rapidly, causing in COVID-19 being declared a global pandemic by the World Health Organization. The key variants include alpha, beta, gamma, and delta; these exhibit high viral transmission, pathogenicity, and immune evasion mechanisms. The delta variant, first confirmed in India, was detected in the majority of COVID-19 patients at the recent wave in the Republic of Korea. Here, the features of the delta variant were compared to the earlier waves, with focus on increased transmissibility. The viral load, from the initial days of infection to 14 days later, was compared based on epidemiological data collected at the time of confirmed diagnosis. The increased viral load observed in the delta variant-led infections influences the scale of the wave, owing to the increased rate of transmission. Infections caused by the delta variant increases the risk of hospitalization within 14 days after symptom onset, and the high viral load correlates with COVID-19 associated morbidity and mortality. Therefore, the future studies should compare the trend of disease severity caused by the high viral load of delta variant with previous waves and analyze the vaccine effects in light of the delta variant of fourth wave.
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Affiliation(s)
- Jeong-Min Kim
- Division of Emerging Infectious Diseases, Bureau of Infectious Disease Diagnosis Control, Korea Disease Control and Prevention Agency, Cheongju-si, South Korea
| | - Jee Eun Rhee
- Division of Emerging Infectious Diseases, Bureau of Infectious Disease Diagnosis Control, Korea Disease Control and Prevention Agency, Cheongju-si, South Korea
| | - Myeongsu Yoo
- Division of Public Health Emergency Response Research, Korea Disease Control and Prevention Agency, Cheongju-si, South Korea
| | - Heui Man Kim
- Division of Emerging Infectious Diseases, Bureau of Infectious Disease Diagnosis Control, Korea Disease Control and Prevention Agency, Cheongju-si, South Korea
| | - Nam-Joo Lee
- Division of Emerging Infectious Diseases, Bureau of Infectious Disease Diagnosis Control, Korea Disease Control and Prevention Agency, Cheongju-si, South Korea
| | - Sang Hee Woo
- Division of Emerging Infectious Diseases, Bureau of Infectious Disease Diagnosis Control, Korea Disease Control and Prevention Agency, Cheongju-si, South Korea
| | - Hye-Jun Jo
- Division of Emerging Infectious Diseases, Bureau of Infectious Disease Diagnosis Control, Korea Disease Control and Prevention Agency, Cheongju-si, South Korea
| | - Donghyok Kwon
- Division of Public Health Emergency Response Research, Korea Disease Control and Prevention Agency, Cheongju-si, South Korea
| | - Sangwon Lee
- Korea Disease Control and Prevention Agency, Cheongju-si, South Korea
| | - Cheon Kwon Yoo
- Bureau of Infectious Disease Diagnosis Control, Korea Disease Control and Prevention Agency, Cheongju-si, South Korea
| | - Eun-Jin Kim
- Division of Emerging Infectious Diseases, Bureau of Infectious Disease Diagnosis Control, Korea Disease Control and Prevention Agency, Cheongju-si, South Korea
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