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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: 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: 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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Park E, Choi SY, Lee S, Kim M, Lee K, Lee S, Yoon S, Kim N, Oh WS, Kim E, Kim BI, Song JS. Widespread Household Transmission of SARS-CoV-2 B.1.1.529 (Omicron) Variant from Children, South Korea, 2022. Yonsei Med J 2023; 64:344-348. [PMID: 37114638 PMCID: PMC10151225 DOI: 10.3349/ymj.2022.0608] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Revised: 02/26/2023] [Accepted: 03/03/2023] [Indexed: 04/29/2023] Open
Abstract
The role that children play in the transmission of the omicron variant is unclear. Here we report an outbreak that started in young children attending various pediatric facilities, leading to extensive household transmission that affected 75 families with 88 confirmed case-patients in 3 weeks. Tailored social and public health measures directed towards children and pediatric facilities are warranted with the emergence of highly transmissible omicron variant to mitigate the impact of coronavirus diseases 2019 (COVID-19).
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Affiliation(s)
- Eunkyung Park
- Korea Disease Control and Prevention Agency, Cheongju, Korea
| | - So Young Choi
- Korea Disease Control and Prevention Agency, Cheongju, Korea
| | - Shinyoung Lee
- Korea Disease Control and Prevention Agency, Cheongju, Korea
| | - Miyoung Kim
- Korea Disease Control and Prevention Agency, Cheongju, Korea
| | - Kyusug Lee
- Wonju Public Health Center, Wonju, Korea
| | - Seonju Lee
- Wonju Public Health Center, Wonju, Korea
| | | | | | - Won Sup Oh
- Gangwon Center for Infectious Diseases, Chuncheon, Korea
- Department of Internal Medicine, Kangwon National University School of Medicine, Chuncheon, Korea
| | - Eunmi Kim
- Gangwon Center for Infectious Diseases, Chuncheon, Korea
| | - Bryan Inho Kim
- Korea Disease Control and Prevention Agency, Cheongju, Korea
| | - Jin Su Song
- Korea Disease Control and Prevention Agency, Cheongju, Korea
- Graduate School of Global Development and Entrepreneurship, Handong Global University, Pohang, Korea.
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Inferring time-varying generation time, serial interval, and incubation period distributions for COVID-19. Nat Commun 2022; 13:7727. [PMID: 36513688 PMCID: PMC9747081 DOI: 10.1038/s41467-022-35496-8] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2022] [Accepted: 12/07/2022] [Indexed: 12/15/2022] Open
Abstract
The generation time distribution, reflecting the time between successive infections in transmission chains, is a key epidemiological parameter for describing COVID-19 transmission dynamics. However, because exact infection times are rarely known, it is often approximated by the serial interval distribution. This approximation holds under the assumption that infectors and infectees share the same incubation period distribution, which may not always be true. We estimated incubation period and serial interval distributions using 629 transmission pairs reconstructed by investigating 2989 confirmed cases in China in January-February 2020, and developed an inferential framework to estimate the generation time distribution that accounts for variation over time due to changes in epidemiology, sampling biases and public health and social measures. We identified substantial reductions over time in the serial interval and generation time distributions. Our proposed method provides more reliable estimation of the temporal variation in the generation time distribution, improving assessment of transmission dynamics.
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Epidemiology and Transmission Dynamics of Infectious Diseases and Control Measures. Viruses 2022; 14:v14112510. [PMID: 36423119 PMCID: PMC9695084 DOI: 10.3390/v14112510] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2022] [Revised: 11/10/2022] [Accepted: 11/10/2022] [Indexed: 11/16/2022] Open
Abstract
The epidemiology and transmission dynamics of infectious diseases must be understood at the individual and community levels to improve public health decision-making for real-time and integrated community-based control strategies. Herein, we explore the epidemiological characteristics for assessing the impact of public health interventions in the community setting and their applications. Computational statistical methods could advance research on infectious disease epidemiology and accumulate scientific evidence of the potential impacts of pharmaceutical/nonpharmaceutical measures to mitigate or control infectious diseases in the community. Novel public health threats from emerging zoonotic infectious diseases are urgent issues. Given these direct and indirect mitigating impacts at various levels to different infectious diseases and their burdens, we must consider an integrated assessment approach, 'One Health', to understand the dynamics and control of infectious diseases.
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Li W, Bulekova K, Gregor B, White LF, Kolaczyk ED. Estimation of local time-varying reproduction numbers in noisy surveillance data. PHILOSOPHICAL TRANSACTIONS. SERIES A, MATHEMATICAL, PHYSICAL, AND ENGINEERING SCIENCES 2022; 380:20210303. [PMID: 35965456 PMCID: PMC9376722 DOI: 10.1098/rsta.2021.0303] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Accepted: 04/11/2022] [Indexed: 05/04/2023]
Abstract
A valuable metric in understanding local infectious disease dynamics is the local time-varying reproduction number, i.e. the expected number of secondary local cases caused by each infected individual. Accurate estimation of this quantity requires distinguishing cases arising from local transmission from those imported from elsewhere. Realistically, we can expect identification of cases as local or imported to be imperfect. We study the propagation of such errors in estimation of the local time-varying reproduction number. In addition, we propose a Bayesian framework for estimation of the true local time-varying reproduction number when identification errors exist. And we illustrate the practical performance of our estimator through simulation studies and with outbreaks of COVID-19 in Hong Kong and Victoria, Australia. This article is part of the theme issue 'Technical challenges of modelling real-life epidemics and examples of overcoming these'.
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Affiliation(s)
- Wenrui Li
- Department of Mathematics and Statistics, Boston University, Boston, MA 02215, USA
| | - Katia Bulekova
- Research Computing Services, Information Services and Technology Boston University, Boston, MA 02215, USA
| | - Brian Gregor
- Research Computing Services, Information Services and Technology Boston University, Boston, MA 02215, USA
| | - Laura F. White
- Department of Biostatistics, Boston University, Boston, MA 02215, USA
| | - Eric D. Kolaczyk
- Department of Mathematics and Statistics, Boston University, Boston, MA 02215, USA
- Hariri Institute for Computing, Boston University, Boston,MA 02215, USA
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Chen SLS, Jen GHH, Hsu CY, Yen AMF, Lai CC, Yeh YP, Chen THH. A new approach to modeling pre-symptomatic incidence and transmission time of imported COVID-19 cases evolving with SARS-CoV-2 variants. STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT : RESEARCH JOURNAL 2022; 37:441-452. [PMID: 36120386 PMCID: PMC9464357 DOI: 10.1007/s00477-022-02305-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Accepted: 08/24/2022] [Indexed: 06/15/2023]
Abstract
There is paucity of the statistical model that is specified for data on imported COVID-19 cases with the unique global information on infectious properties of SARS-CoV-2 variant different from local outbreak data used for estimating transmission and infectiousness parameters via the established epidemic models. To this end, a new approach with a four-state stochastic model was proposed to formulate these well-established infectious parameters with three new parameters, including the pre-symptomatic incidence rate, the median of pre-symptomatic transmission time (MPTT) to symptomatic state, and the incidence (proportion) of asymptomatic cases using imported COVID-19 data. We fitted the proposed stochastic model to empirical data on imported COVID-19 cases from D614G to Omicron with the corresponding calendar periods according to the classification GISAID information on the evolution of SARS-CoV-2 variant between March 2020 and Jan 2022 in Taiwan. The pre-symptomatic incidence rate was the highest for Omicron followed by Alpha, Delta, and D614G. The MPTT (in days) increased from 3.45 (first period) ~ 4.02 (second period) of D614G until 3.94-4.65 of VOC Alpha but dropped to 3.93-3.49 of Delta and 2 days (only first period) of Omicron. The proportion of asymptomatic cases increased from 29% of D-614G period to 59.2% of Omicron. Modeling data on imported cases across strains of SARS-CoV-2 not only bridges the link between the underlying natural infectious properties elucidated in the previous epidemic models and different disease phenotypes of COVID-19 but also provides precision quarantine and isolation policy for border control in the face of various emerging SRAS-CoV-2 variants globally.
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Affiliation(s)
- Sam Li-Sheng Chen
- School of Oral Hygiene, College of Oral Medicine, Taipei Medical University, Taipei, Taiwan
| | - Grace Hsiao-Hsuan Jen
- School of Oral Hygiene, College of Oral Medicine, Taipei Medical University, Taipei, Taiwan
| | - Chen-Yang Hsu
- Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Room 533, No. 17, Hsuchow Road, Taipei, 100 Taiwan
| | - Amy Ming-Fang Yen
- School of Oral Hygiene, College of Oral Medicine, Taipei Medical University, Taipei, Taiwan
| | - Chao-Chih Lai
- Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Room 533, No. 17, Hsuchow Road, Taipei, 100 Taiwan
- Emergency Department of Taipei City Hospital, Ren-Ai Branch, Taipei, Taiwan
| | - Yen-Po Yeh
- Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Room 533, No. 17, Hsuchow Road, Taipei, 100 Taiwan
- Changhua County Public Health Bureau, Changhua, Taiwan
| | - Tony Hsiu-Hsi Chen
- Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Room 533, No. 17, Hsuchow Road, Taipei, 100 Taiwan
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Li W, Bulekova K, Gregor B, White LF, Kolaczyk ED. Estimation of local time-varying reproduction numbers in noisy surveillance data. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2022:2021.04.23.21255958. [PMID: 33948612 PMCID: PMC8095231 DOI: 10.1101/2021.04.23.21255958] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
A valuable metric in understanding local infectious disease dynamics is the local time-varying reproduction number, i.e. the expected number of secondary local cases caused by each infected individual. Accurate estimation of this quantity requires distinguishing cases arising from local transmission from those imported from elsewhere. Realistically, we can expect identification of cases as local or imported to be imperfect. We study the propagation of such errors in estimation of the local time-varying reproduction number. In addition, we propose a Bayesian framework for estimation of the true local time-varying reproduction number when identification errors exist. And we illustrate the practical performance of our estimator through simulation studies and with outbreaks of COVID-19 in Hong Kong and Victoria, Australia.
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Affiliation(s)
- Wenrui Li
- Department of Mathematics and Statistics, Boston University, Boston MA, USA
| | - Katia Bulekova
- Research Computing Services, Information Services and Technology, Boston University, Boston MA, USA
| | - Brian Gregor
- Research Computing Services, Information Services and Technology, Boston University, Boston MA, USA
| | - Laura F. White
- Department of Biostatistics, Boston University, Boston MA, USA
| | - Eric D. Kolaczyk
- Department of Mathematics and Statistics, Boston University, Boston MA, USA
- Hariri Institute for Computing, Boston University, Boston MA, USA
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Increased transmission of SARS-CoV-2 in Denmark during UEFA European championships. Epidemiol Infect 2022; 150:e123. [PMID: 35317884 PMCID: PMC9254153 DOI: 10.1017/s095026882200019x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
Abstract
Denmark hosted four games during the 2020 UEFA European championships (EC2020). After declining positive SARS-CoV-2 test rates in Denmark, a rise occurred during and after the tournament, concomitant with the replacement of the dominant Alpha lineage (B.1.1.7) by the Delta lineage (B.1.617.2), increasing vaccination rates and cessation of several restrictions. A cohort study including 33 227 cases was conducted from 30 May to 25 July 2021, 14 days before and after the EC2020. Included was a nested cohort with event information from big-screen events and matches at the Danish national stadium, Parken (DNSP) in Copenhagen, held from 12 June to 28 June 2021. Information from whole-genome sequencing, contact tracing and Danish registries was collected. Case–case connections were used to establish transmission trees. Cases infected on match days were compared to cases not infected on match days as a reference. The crude incidence rate ratio (IRR) of transmissions was 1.55, corresponding to 584 (1.76%) cases attributable to EC2020 celebrations. The IRR adjusted for covariates was lower (IRR 1.41) but still significant, and also pointed to a reduced number of transmissions from fully vaccinated cases (IRR 0.59). These data support the hypothesis that the EC2020 celebrations contributed to the rise of cases in Denmark in the early summer of 2021.
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Deciphering Multifactorial Correlations of COVID-19 Incidence and Mortality in the Brazilian Amazon Basin. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph19031153. [PMID: 35162177 PMCID: PMC8834595 DOI: 10.3390/ijerph19031153] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/03/2021] [Revised: 12/31/2021] [Accepted: 01/18/2022] [Indexed: 12/10/2022]
Abstract
Amazonas suffered greatly during the COVID-19 pandemic. The mortality and fatality rates soared and scarcity of oxygen and healthcare supplies led the health system and funerary services to collapse. Thus, we analyzed the trends of incidence, mortality, and lethality indicators of COVID-19 and the dynamics of their main determinants in the state of Amazonas from March 2020 to June 2021. This is a time-series ecological study. We calculated the lethality, mortality, and incidence rates with official and public data from the Health Department. We used the Prais-Winsten regression and trends were classified as stationary, increasing, or decreasing. The effective reproduction number (Rt) was also estimated. Differences were considered significant when p < 0.05. We extracted 396,772 cases of and 13,420 deaths from COVID-19; 66% of deaths were in people aged over 60; 57% were men. Cardiovascular diseases were the most common comorbidity (28.84%), followed by diabetes (25.35%). Rural areas reported 53% of the total cases and 31% of the total deaths. The impact of COVID-19 in the Amazon is not limited to the direct effects of the pandemic itself; it may present characteristics of a syndemic due to the interaction of COVID-19 with pre-existing illnesses, endemic diseases, and social vulnerabilities.
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