The latency time of SARS-CoV- 2 Delta variant in infection- and vaccine-naive individuals from Vietnam
The latency time (from infection to infectiousness) guides the choice of measures required to control an infectious disease. Estimates of the SARS-CoV- 2 latency time are sparse due to lack of appropriate and representative data. Infection time is rarely known exactly and exposure information may be...
Saved in:
| Published in | BMC infectious diseases Vol. 25; no. 1; pp. 515 - 18 |
|---|---|
| Main Authors | , , , , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
England
BioMed Central Ltd
12.04.2025
BioMed Central BMC |
| Subjects | |
| Online Access | Get full text |
Cover
Loading…
| Abstract | The latency time (from infection to infectiousness) guides the choice of measures required to control an infectious disease. Estimates of the SARS-CoV- 2 latency time are sparse due to lack of appropriate and representative data. Infection time is rarely known exactly and exposure information may be subject to several biases. Information on the endpoint requires repeated testing. Moreover, estimation is challenging because both the starting point and endpoint are typically interval censored and data may be subject to length-biased sampling (truncation).
We collected detailed information on exposure from public health reports produced during an outbreak with the SARS-CoV- 2 Delta variant in Ho Chi Minh City, Vietnam, in May-July 2021. Using a custom digital form and application facilitated reliable choices on exposure window. This comprehensive data set on exposure and test results from 1951 individuals, collected in the absence of large-scale vaccination or earlier infection, is the first of its kind outside of China. We accounted for the doubly interval censored nature of the observations and went beyond the standard assumption of a constant infection risk over calendar time (exponential growth) and allowed for flexibility regarding the latency time (generalized gamma distribution). We addressed right truncation due to a cutoff in data collection and a finite quarantine length. Employing a Bayesian approach, using the program JAGS, made the analyses relatively straightforward.
Assuming exponential growth, our estimate of SARS-CoV- 2 Delta variant's mean latency time was 3.22 (95% Credible Interval 2.89 - 3.55) days; the median was 1.81 (95% CrI 1.44- 2.16) days; the 95 th percentile was 10.98 (95% CrI 9.91 - 12.41) days. These values were much larger if a uniform infection risk was assumed.
Using a Bayesian approach with the JAGS program, we were able to estimate the SARS-CoV- 2 latency time distribution of the Delta variant in infection-naive and vaccine-naive individuals. Estimates were sensitive to the assumptions made regarding the risk of infection within the exposure window. Compared to earlier studies, the median latency time was shorter, while the 95 th percentile was larger. Our results stress the importance of thoughtful data collection and analysis for evidence-based control of an infectious disease. |
|---|---|
| AbstractList | The latency time (from infection to infectiousness) guides the choice of measures required to control an infectious disease. Estimates of the SARS-CoV- 2 latency time are sparse due to lack of appropriate and representative data. Infection time is rarely known exactly and exposure information may be subject to several biases. Information on the endpoint requires repeated testing. Moreover, estimation is challenging because both the starting point and endpoint are typically interval censored and data may be subject to length-biased sampling (truncation).BACKGROUNDThe latency time (from infection to infectiousness) guides the choice of measures required to control an infectious disease. Estimates of the SARS-CoV- 2 latency time are sparse due to lack of appropriate and representative data. Infection time is rarely known exactly and exposure information may be subject to several biases. Information on the endpoint requires repeated testing. Moreover, estimation is challenging because both the starting point and endpoint are typically interval censored and data may be subject to length-biased sampling (truncation).We collected detailed information on exposure from public health reports produced during an outbreak with the SARS-CoV- 2 Delta variant in Ho Chi Minh City, Vietnam, in May-July 2021. Using a custom digital form and application facilitated reliable choices on exposure window. This comprehensive data set on exposure and test results from 1951 individuals, collected in the absence of large-scale vaccination or earlier infection, is the first of its kind outside of China. We accounted for the doubly interval censored nature of the observations and went beyond the standard assumption of a constant infection risk over calendar time (exponential growth) and allowed for flexibility regarding the latency time (generalized gamma distribution). We addressed right truncation due to a cutoff in data collection and a finite quarantine length. Employing a Bayesian approach, using the program JAGS, made the analyses relatively straightforward.METHODSWe collected detailed information on exposure from public health reports produced during an outbreak with the SARS-CoV- 2 Delta variant in Ho Chi Minh City, Vietnam, in May-July 2021. Using a custom digital form and application facilitated reliable choices on exposure window. This comprehensive data set on exposure and test results from 1951 individuals, collected in the absence of large-scale vaccination or earlier infection, is the first of its kind outside of China. We accounted for the doubly interval censored nature of the observations and went beyond the standard assumption of a constant infection risk over calendar time (exponential growth) and allowed for flexibility regarding the latency time (generalized gamma distribution). We addressed right truncation due to a cutoff in data collection and a finite quarantine length. Employing a Bayesian approach, using the program JAGS, made the analyses relatively straightforward.Assuming exponential growth, our estimate of SARS-CoV- 2 Delta variant's mean latency time was 3.22 (95% Credible Interval 2.89 - 3.55) days; the median was 1.81 (95% CrI 1.44- 2.16) days; the 95 th percentile was 10.98 (95% CrI 9.91 - 12.41) days. These values were much larger if a uniform infection risk was assumed.RESULTSAssuming exponential growth, our estimate of SARS-CoV- 2 Delta variant's mean latency time was 3.22 (95% Credible Interval 2.89 - 3.55) days; the median was 1.81 (95% CrI 1.44- 2.16) days; the 95 th percentile was 10.98 (95% CrI 9.91 - 12.41) days. These values were much larger if a uniform infection risk was assumed.Using a Bayesian approach with the JAGS program, we were able to estimate the SARS-CoV- 2 latency time distribution of the Delta variant in infection-naive and vaccine-naive individuals. Estimates were sensitive to the assumptions made regarding the risk of infection within the exposure window. Compared to earlier studies, the median latency time was shorter, while the 95 th percentile was larger. Our results stress the importance of thoughtful data collection and analysis for evidence-based control of an infectious disease.CONCLUSIONSUsing a Bayesian approach with the JAGS program, we were able to estimate the SARS-CoV- 2 latency time distribution of the Delta variant in infection-naive and vaccine-naive individuals. Estimates were sensitive to the assumptions made regarding the risk of infection within the exposure window. Compared to earlier studies, the median latency time was shorter, while the 95 th percentile was larger. Our results stress the importance of thoughtful data collection and analysis for evidence-based control of an infectious disease. BackgroundThe latency time (from infection to infectiousness) guides the choice of measures required to control an infectious disease. Estimates of the SARS-CoV- 2 latency time are sparse due to lack of appropriate and representative data. Infection time is rarely known exactly and exposure information may be subject to several biases. Information on the endpoint requires repeated testing. Moreover, estimation is challenging because both the starting point and endpoint are typically interval censored and data may be subject to length-biased sampling (truncation).MethodsWe collected detailed information on exposure from public health reports produced during an outbreak with the SARS-CoV- 2 Delta variant in Ho Chi Minh City, Vietnam, in May-July 2021. Using a custom digital form and application facilitated reliable choices on exposure window. This comprehensive data set on exposure and test results from 1951 individuals, collected in the absence of large-scale vaccination or earlier infection, is the first of its kind outside of China.We accounted for the doubly interval censored nature of the observations and went beyond the standard assumption of a constant infection risk over calendar time (exponential growth) and allowed for flexibility regarding the latency time (generalized gamma distribution). We addressed right truncation due to a cutoff in data collection and a finite quarantine length. Employing a Bayesian approach, using the program JAGS, made the analyses relatively straightforward.ResultsAssuming exponential growth, our estimate of SARS-CoV- 2 Delta variant’s mean latency time was 3.22 (95% Credible Interval 2.89 - 3.55) days; the median was 1.81 (95% CrI 1.44- 2.16) days; the 95 th percentile was 10.98 (95% CrI 9.91 - 12.41) days. These values were much larger if a uniform infection risk was assumed.ConclusionsUsing a Bayesian approach with the JAGS program, we were able to estimate the SARS-CoV- 2 latency time distribution of the Delta variant in infection-naive and vaccine-naive individuals. Estimates were sensitive to the assumptions made regarding the risk of infection within the exposure window. Compared to earlier studies, the median latency time was shorter, while the 95 th percentile was larger. Our results stress the importance of thoughtful data collection and analysis for evidence-based control of an infectious disease. The latency time (from infection to infectiousness) guides the choice of measures required to control an infectious disease. Estimates of the SARS-CoV- 2 latency time are sparse due to lack of appropriate and representative data. Infection time is rarely known exactly and exposure information may be subject to several biases. Information on the endpoint requires repeated testing. Moreover, estimation is challenging because both the starting point and endpoint are typically interval censored and data may be subject to length-biased sampling (truncation). We collected detailed information on exposure from public health reports produced during an outbreak with the SARS-CoV- 2 Delta variant in Ho Chi Minh City, Vietnam, in May-July 2021. Using a custom digital form and application facilitated reliable choices on exposure window. This comprehensive data set on exposure and test results from 1951 individuals, collected in the absence of large-scale vaccination or earlier infection, is the first of its kind outside of China. Assuming exponential growth, our estimate of SARS-CoV- 2 Delta variant's mean latency time was 3.22 (95% Credible Interval 2.89 - 3.55) days; the median was 1.81 (95% CrI 1.44- 2.16) days; the 95 th percentile was 10.98 (95% CrI 9.91 - 12.41) days. These values were much larger if a uniform infection risk was assumed. Using a Bayesian approach with the JAGS program, we were able to estimate the SARS-CoV- 2 latency time distribution of the Delta variant in infection-naive and vaccine-naive individuals. Estimates were sensitive to the assumptions made regarding the risk of infection within the exposure window. Compared to earlier studies, the median latency time was shorter, while the 95 th percentile was larger. Our results stress the importance of thoughtful data collection and analysis for evidence-based control of an infectious disease. The latency time (from infection to infectiousness) guides the choice of measures required to control an infectious disease. Estimates of the SARS-CoV- 2 latency time are sparse due to lack of appropriate and representative data. Infection time is rarely known exactly and exposure information may be subject to several biases. Information on the endpoint requires repeated testing. Moreover, estimation is challenging because both the starting point and endpoint are typically interval censored and data may be subject to length-biased sampling (truncation). We collected detailed information on exposure from public health reports produced during an outbreak with the SARS-CoV- 2 Delta variant in Ho Chi Minh City, Vietnam, in May-July 2021. Using a custom digital form and application facilitated reliable choices on exposure window. This comprehensive data set on exposure and test results from 1951 individuals, collected in the absence of large-scale vaccination or earlier infection, is the first of its kind outside of China. We accounted for the doubly interval censored nature of the observations and went beyond the standard assumption of a constant infection risk over calendar time (exponential growth) and allowed for flexibility regarding the latency time (generalized gamma distribution). We addressed right truncation due to a cutoff in data collection and a finite quarantine length. Employing a Bayesian approach, using the program JAGS, made the analyses relatively straightforward. Assuming exponential growth, our estimate of SARS-CoV- 2 Delta variant's mean latency time was 3.22 (95% Credible Interval 2.89 - 3.55) days; the median was 1.81 (95% CrI 1.44- 2.16) days; the 95 th percentile was 10.98 (95% CrI 9.91 - 12.41) days. These values were much larger if a uniform infection risk was assumed. Using a Bayesian approach with the JAGS program, we were able to estimate the SARS-CoV- 2 latency time distribution of the Delta variant in infection-naive and vaccine-naive individuals. Estimates were sensitive to the assumptions made regarding the risk of infection within the exposure window. Compared to earlier studies, the median latency time was shorter, while the 95 th percentile was larger. Our results stress the importance of thoughtful data collection and analysis for evidence-based control of an infectious disease. Background The latency time (from infection to infectiousness) guides the choice of measures required to control an infectious disease. Estimates of the SARS-CoV- 2 latency time are sparse due to lack of appropriate and representative data. Infection time is rarely known exactly and exposure information may be subject to several biases. Information on the endpoint requires repeated testing. Moreover, estimation is challenging because both the starting point and endpoint are typically interval censored and data may be subject to length-biased sampling (truncation). Methods We collected detailed information on exposure from public health reports produced during an outbreak with the SARS-CoV- 2 Delta variant in Ho Chi Minh City, Vietnam, in May-July 2021. Using a custom digital form and application facilitated reliable choices on exposure window. This comprehensive data set on exposure and test results from 1951 individuals, collected in the absence of large-scale vaccination or earlier infection, is the first of its kind outside of China. We accounted for the doubly interval censored nature of the observations and went beyond the standard assumption of a constant infection risk over calendar time (exponential growth) and allowed for flexibility regarding the latency time (generalized gamma distribution). We addressed right truncation due to a cutoff in data collection and a finite quarantine length. Employing a Bayesian approach, using the program JAGS, made the analyses relatively straightforward. Results Assuming exponential growth, our estimate of SARS-CoV- 2 Delta variant's mean latency time was 3.22 (95% Credible Interval 2.89 - 3.55) days; the median was 1.81 (95% CrI 1.44- 2.16) days; the 95 th percentile was 10.98 (95% CrI 9.91 - 12.41) days. These values were much larger if a uniform infection risk was assumed. Conclusions Using a Bayesian approach with the JAGS program, we were able to estimate the SARS-CoV- 2 latency time distribution of the Delta variant in infection-naive and vaccine-naive individuals. Estimates were sensitive to the assumptions made regarding the risk of infection within the exposure window. Compared to earlier studies, the median latency time was shorter, while the 95 th percentile was larger. Our results stress the importance of thoughtful data collection and analysis for evidence-based control of an infectious disease. Keywords: SARS-CoV- 2, Latency time, Quarantine length, Doubly interval censored data, Truncation, Generalized gamma distribution Abstract Background The latency time (from infection to infectiousness) guides the choice of measures required to control an infectious disease. Estimates of the SARS-CoV- 2 latency time are sparse due to lack of appropriate and representative data. Infection time is rarely known exactly and exposure information may be subject to several biases. Information on the endpoint requires repeated testing. Moreover, estimation is challenging because both the starting point and endpoint are typically interval censored and data may be subject to length-biased sampling (truncation). Methods We collected detailed information on exposure from public health reports produced during an outbreak with the SARS-CoV- 2 Delta variant in Ho Chi Minh City, Vietnam, in May-July 2021. Using a custom digital form and application facilitated reliable choices on exposure window. This comprehensive data set on exposure and test results from 1951 individuals, collected in the absence of large-scale vaccination or earlier infection, is the first of its kind outside of China. We accounted for the doubly interval censored nature of the observations and went beyond the standard assumption of a constant infection risk over calendar time (exponential growth) and allowed for flexibility regarding the latency time (generalized gamma distribution). We addressed right truncation due to a cutoff in data collection and a finite quarantine length. Employing a Bayesian approach, using the program JAGS, made the analyses relatively straightforward. Results Assuming exponential growth, our estimate of SARS-CoV- 2 Delta variant’s mean latency time was 3.22 (95% Credible Interval 2.89 - 3.55) days; the median was 1.81 (95% CrI 1.44- 2.16) days; the 95 th percentile was 10.98 (95% CrI 9.91 - 12.41) days. These values were much larger if a uniform infection risk was assumed. Conclusions Using a Bayesian approach with the JAGS program, we were able to estimate the SARS-CoV- 2 latency time distribution of the Delta variant in infection-naive and vaccine-naive individuals. Estimates were sensitive to the assumptions made regarding the risk of infection within the exposure window. Compared to earlier studies, the median latency time was shorter, while the 95 th percentile was larger. Our results stress the importance of thoughtful data collection and analysis for evidence-based control of an infectious disease. |
| ArticleNumber | 515 |
| Audience | Academic |
| Author | Nguyen Duc, Manh Arntzen, Vera H. Truong Thi Thanh, Lan Le Thanh Hoang, Nhat Nguyen, Tu-Anh Nguyen Hoai Thao, Tam Mai Thanh, Buu Fiocco, Marta Phung Khanh, Lam Geskus, Ronald B. Choisy, Marc Le Hong, Nga |
| Author_xml | – sequence: 1 givenname: Vera H. orcidid: 0000-0002-2642-9898 surname: Arntzen fullname: Arntzen, Vera H. – sequence: 2 givenname: Manh surname: Nguyen Duc fullname: Nguyen Duc, Manh – sequence: 3 givenname: Marta orcidid: 0000-0001-5588-0277 surname: Fiocco fullname: Fiocco, Marta – sequence: 4 givenname: Lan surname: Truong Thi Thanh fullname: Truong Thi Thanh, Lan – sequence: 5 givenname: Tam surname: Nguyen Hoai Thao fullname: Nguyen Hoai Thao, Tam – sequence: 6 givenname: Buu surname: Mai Thanh fullname: Mai Thanh, Buu – sequence: 7 givenname: Tu-Anh surname: Nguyen fullname: Nguyen, Tu-Anh – sequence: 8 givenname: Nhat orcidid: 0000-0002-3756-7217 surname: Le Thanh Hoang fullname: Le Thanh Hoang, Nhat – sequence: 9 givenname: Marc surname: Choisy fullname: Choisy, Marc – sequence: 10 givenname: Lam surname: Phung Khanh fullname: Phung Khanh, Lam – sequence: 11 givenname: Nga surname: Le Hong fullname: Le Hong, Nga – sequence: 12 givenname: Ronald B. orcidid: 0000-0002-2740-3155 surname: Geskus fullname: Geskus, Ronald B. |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/40221669$$D View this record in MEDLINE/PubMed |
| BookMark | eNqNkl1v0zAUhiM0xD7gD3CBInEDFxn-bOwrVJWvSpMmraO31olz3LlK7JG4Ffv3uOsYK-ICObKjcx6_iV-_p8VRiAGL4jUl55SqyYeRMlXrijBZUaK0qviz4oSKmlaMc3H05P24OB3HNSG0Vky_KI4FYYxOJvqkcNc3WHaQMNi7Mvkey-jKxfRqUc3isipZ-Qm7BOUWBg8hlT7kx6FNPoaqhNDmjrU-YBXAbzE3W7_17Qa6sXRD7MulxxSgf1k8d7mGrx7Ws-L7l8_Xs2_VxeXX-Wx6UVmpZMpzA8JSwakWNeOsRssVCIGghUVogDXQ0Foj6kZNamwpak4cCAUoG9vys2K-120jrM3t4HsY7kwEb-4LcVgZGJK3HZq6FURJQQCkyz7VTSsltM7Wjrome5a1Pu61bjdNj63FkAboDkQPO8HfmFXcGkq15lqprPDuQWGIPzY4JtP70WLXQcC4GQ2nSouJ5FJn9O1f6DpuhpC9MpwRKhmpJflDrSCfIN9EzB-2O1EzVVxymr3Z_fj5P6g8Wuy9zRlyPtcPNrw_2JCZhD_TCjbjaOaLq_9nL5eH7JunDj5a9zt-GWB7wA5xHAd0jwglZpdxs8-4yRk39xk3nP8CfI3qxA |
| Cites_doi | 10.3201/eid2905.221787 10.1001/jamanetworkopen.2021.37257 10.1093/cid/ciaa1130 10.2807/1560-7917.ES.2022.27.10.2100815 10.1101/2024.01.12.24301247 10.2307/1266594 10.1093/cid/ciab746 10.1093/cid/ciaa1195 10.1093/cid/ciab501 10.3389/fams.2022.1035393 10.1002/sim.3659 10.1016/j.mbs.2025.109432 10.1186/s12879-024-09433-7 10.1002/iid3.664 10.1002/sim.9726 10.1186/s12879-024-09158-7 10.1186/s40249-021-00847-y 10.18637/jss.v070.i08 10.1038/s41467-022-35496-8 10.1016/S2666-5247(23)00005-8 10.1038/s41579-022-00822-w 10.1038/s41590-021-00882-9 10.12688/f1000research.18002.1 10.7554/eLife.57149 10.3760/cma.j.cn112150-20220926-00925 |
| ContentType | Journal Article |
| Copyright | 2025. The Author(s). COPYRIGHT 2025 BioMed Central Ltd. 2025. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. The Author(s) 2025 2025 |
| Copyright_xml | – notice: 2025. The Author(s). – notice: COPYRIGHT 2025 BioMed Central Ltd. – notice: 2025. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. – notice: The Author(s) 2025 2025 |
| DBID | AAYXX CITATION CGR CUY CVF ECM EIF NPM IOV ISR 3V. 7QL 7T2 7U9 7X7 7XB 88E 8C1 8FI 8FJ 8FK ABUWG AEUYN AFKRA AZQEC BENPR C1K CCPQU COVID DWQXO FYUFA GHDGH H94 K9. M0S M1P M7N PHGZM PHGZT PIMPY PJZUB PKEHL PPXIY PQEST PQQKQ PQUKI PRINS 7X8 5PM DOA |
| DOI | 10.1186/s12879-025-10898-3 |
| DatabaseName | CrossRef Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed Opposing Viewpoints in Context (Gale) Gale In Context: Science ProQuest Central (Corporate) Bacteriology Abstracts (Microbiology B) Health and Safety Science Abstracts (Full archive) Virology and AIDS Abstracts ProQuest Health & Medical Collection ProQuest Central (purchase pre-March 2016) Medical Database (Alumni Edition) Public Health Database (subscription) Hospital Premium Collection Hospital Premium Collection (Alumni Edition) ProQuest Central (Alumni) (purchase pre-March 2016) ProQuest Central (Alumni) ProQuest One Sustainability (subscription) ProQuest Central UK/Ireland ProQuest Central Essentials ProQuest Central Environmental Sciences and Pollution Management ProQuest One Community College Coronavirus Research Database ProQuest Central Korea Health Research Premium Collection Health Research Premium Collection (Alumni) AIDS and Cancer Research Abstracts ProQuest Health & Medical Complete (Alumni) Health & Medical Collection (Alumni Edition) Medical Database Algology Mycology and Protozoology Abstracts (Microbiology C) ProQuest Central Premium ProQuest One Academic Publicly Available Content Database (subscription) ProQuest Health & Medical Research Collection ProQuest One Academic Middle East (New) ProQuest One Health & Nursing ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Academic ProQuest One Academic UKI Edition ProQuest Central China MEDLINE - Academic PubMed Central (Full Participant titles) DOAJ Directory of Open Access Journals - NZ |
| DatabaseTitle | CrossRef MEDLINE Medline Complete MEDLINE with Full Text PubMed MEDLINE (Ovid) Publicly Available Content Database ProQuest One Academic Middle East (New) ProQuest Central Essentials ProQuest Health & Medical Complete (Alumni) ProQuest Central (Alumni Edition) ProQuest One Community College ProQuest One Health & Nursing ProQuest Central China Environmental Sciences and Pollution Management ProQuest Central ProQuest One Sustainability ProQuest Health & Medical Research Collection Health Research Premium Collection Health and Medicine Complete (Alumni Edition) ProQuest Central Korea Bacteriology Abstracts (Microbiology B) Algology Mycology and Protozoology Abstracts (Microbiology C) Health & Medical Research Collection AIDS and Cancer Research Abstracts Health & Safety Science Abstracts ProQuest Central (New) ProQuest Medical Library (Alumni) ProQuest Public Health Virology and AIDS Abstracts ProQuest One Academic Eastern Edition Coronavirus Research Database ProQuest Hospital Collection Health Research Premium Collection (Alumni) ProQuest Hospital Collection (Alumni) ProQuest Health & Medical Complete ProQuest Medical Library ProQuest One Academic UKI Edition ProQuest One Academic ProQuest One Academic (New) ProQuest Central (Alumni) MEDLINE - Academic |
| DatabaseTitleList | MEDLINE - Academic Publicly Available Content Database MEDLINE |
| Database_xml | – sequence: 1 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 2 dbid: NPM name: PubMed url: https://proxy.k.utb.cz/login?url=http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 3 dbid: EIF name: MEDLINE url: https://proxy.k.utb.cz/login?url=https://www.webofscience.com/wos/medline/basic-search sourceTypes: Index Database – sequence: 4 dbid: BENPR name: ProQuest Central url: https://www.proquest.com/central sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Medicine Public Health |
| EISSN | 1471-2334 |
| EndPage | 18 |
| ExternalDocumentID | oai_doaj_org_article_7d408540aa5f4717bd55adfc7f1fb334 PMC11993988 A835311794 40221669 10_1186_s12879_025_10898_3 |
| Genre | Journal Article |
| GeographicLocations | Vietnam Ho Chi Minh City Vietnam |
| GeographicLocations_xml | – name: Vietnam – name: Ho Chi Minh City Vietnam |
| GrantInformation_xml | – fundername: Wellcome Trust – fundername: Wellcome Trust grantid: 225167/Z/22/Z – fundername: Wellcome Trust grantid: 225167/A/22/Z |
| GroupedDBID | --- 0R~ 23N 2WC 53G 5VS 6J9 6PF 7X7 88E 8C1 8FI 8FJ AAFWJ AAJSJ AASML AAWTL AAYXX ABDBF ABUWG ACGFO ACGFS ACIHN ACPRK ACUHS ADBBV ADRAZ ADUKV AEAQA AENEX AEUYN AFKRA AFPKN AFRAH AHBYD AHMBA AHYZX ALIPV ALMA_UNASSIGNED_HOLDINGS AMKLP AMTXH AOIJS BAPOH BAWUL BCNDV BENPR BFQNJ BMC BPHCQ BVXVI C6C CCPQU CITATION CS3 DIK DU5 E3Z EAD EAP EAS EBD EBLON EBS EMB EMK EMOBN ESX F5P FYUFA GROUPED_DOAJ GX1 HMCUK HYE IAO IHR INH INR IOV ISR ITC KQ8 M1P M~E O5R O5S OK1 OVT P2P PGMZT PHGZM PHGZT PIMPY PQQKQ PROAC PSQYO RBZ RNS ROL RPM RSV SMD SOJ SV3 TR2 TUS UKHRP W2D WOQ WOW XSB CGR CUY CVF ECM EIF NPM PJZUB PPXIY PMFND 3V. 7QL 7T2 7U9 7XB 8FK AZQEC C1K COVID DWQXO H94 K9. M48 M7N PKEHL PQEST PQUKI PRINS 7X8 5PM PUEGO |
| ID | FETCH-LOGICAL-c585t-c5ba4c14319472327ec38a44ea94ceaba2bab179ee9b867ed1e930fa48ae5bcd3 |
| IEDL.DBID | DOA |
| ISSN | 1471-2334 |
| IngestDate | Wed Aug 27 01:26:29 EDT 2025 Thu Aug 21 18:33:42 EDT 2025 Fri Jul 11 19:02:51 EDT 2025 Fri Jul 25 10:53:10 EDT 2025 Tue Jun 17 22:00:31 EDT 2025 Tue Jun 10 20:58:45 EDT 2025 Fri Jun 27 05:14:20 EDT 2025 Fri Jun 27 05:15:18 EDT 2025 Mon Jul 21 05:57:53 EDT 2025 Tue Jul 01 04:58:53 EDT 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 1 |
| Keywords | SARS-CoV- 2 Latency time Quarantine length Doubly interval censored data Generalized gamma distribution Truncation |
| Language | English |
| License | 2025. The Author(s). Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c585t-c5ba4c14319472327ec38a44ea94ceaba2bab179ee9b867ed1e930fa48ae5bcd3 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 |
| ORCID | 0000-0002-2740-3155 0000-0002-3756-7217 0000-0002-2642-9898 0000-0001-5588-0277 |
| OpenAccessLink | https://doaj.org/article/7d408540aa5f4717bd55adfc7f1fb334 |
| PMID | 40221669 |
| PQID | 3201520750 |
| PQPubID | 42582 |
| PageCount | 18 |
| ParticipantIDs | doaj_primary_oai_doaj_org_article_7d408540aa5f4717bd55adfc7f1fb334 pubmedcentral_primary_oai_pubmedcentral_nih_gov_11993988 proquest_miscellaneous_3189465359 proquest_journals_3201520750 gale_infotracmisc_A835311794 gale_infotracacademiconefile_A835311794 gale_incontextgauss_ISR_A835311794 gale_incontextgauss_IOV_A835311794 pubmed_primary_40221669 crossref_primary_10_1186_s12879_025_10898_3 |
| ProviderPackageCode | CITATION AAYXX |
| PublicationCentury | 2000 |
| PublicationDate | 2025-04-12 |
| PublicationDateYYYYMMDD | 2025-04-12 |
| PublicationDate_xml | – month: 04 year: 2025 text: 2025-04-12 day: 12 |
| PublicationDecade | 2020 |
| PublicationPlace | England |
| PublicationPlace_xml | – name: England – name: London |
| PublicationTitle | BMC infectious diseases |
| PublicationTitleAlternate | BMC Infect Dis |
| PublicationYear | 2025 |
| Publisher | BioMed Central Ltd BioMed Central BMC |
| Publisher_xml | – name: BioMed Central Ltd – name: BioMed Central – name: BMC |
| References | VH Arntzen (10898_CR9) 2023; 42 10898_CR30 10898_CR11 10898_CR33 10898_CR34 10898_CR31 10898_CR32 10898_CR2 10898_CR15 10898_CR3 10898_CR16 10898_CR4 10898_CR13 10898_CR5 10898_CR14 NG Reich (10898_CR17) 2009; 28 10898_CR19 10898_CR1 10898_CR18 H Xin (10898_CR21) 2021; 73 10898_CR22 10898_CR23 10898_CR20 10898_CR26 10898_CR27 10898_CR24 10898_CR25 10898_CR28 PQ Thai (10898_CR10) 2020; 72 10898_CR29 10898_CR7 10898_CR8 LV Tan (10898_CR12) 2021; 22 H Xin (10898_CR6) 2021; 74 |
| References_xml | – ident: 10898_CR14 doi: 10.3201/eid2905.221787 – ident: 10898_CR26 – ident: 10898_CR29 doi: 10.1001/jamanetworkopen.2021.37257 – volume: 72 start-page: e334 issue: 9 year: 2020 ident: 10898_CR10 publication-title: Clin Infect Dis. doi: 10.1093/cid/ciaa1130 – ident: 10898_CR24 – ident: 10898_CR2 doi: 10.2807/1560-7917.ES.2022.27.10.2100815 – ident: 10898_CR20 doi: 10.1101/2024.01.12.24301247 – ident: 10898_CR32 – ident: 10898_CR34 – ident: 10898_CR19 doi: 10.2307/1266594 – volume: 74 start-page: 1678 issue: 9 year: 2021 ident: 10898_CR6 publication-title: Clin Infect Dis. doi: 10.1093/cid/ciab746 – ident: 10898_CR11 doi: 10.1093/cid/ciaa1195 – volume: 73 start-page: 2344 issue: 12 year: 2021 ident: 10898_CR21 publication-title: Clin Infect Dis. doi: 10.1093/cid/ciab501 – ident: 10898_CR16 doi: 10.3389/fams.2022.1035393 – volume: 28 start-page: 2769 issue: 22 year: 2009 ident: 10898_CR17 publication-title: Stat Med. doi: 10.1002/sim.3659 – ident: 10898_CR1 doi: 10.1016/j.mbs.2025.109432 – ident: 10898_CR28 doi: 10.1186/s12879-024-09433-7 – ident: 10898_CR3 doi: 10.1002/iid3.664 – volume: 42 start-page: 2341 issue: 14 year: 2023 ident: 10898_CR9 publication-title: Stat Med. doi: 10.1002/sim.9726 – ident: 10898_CR4 doi: 10.1186/s12879-024-09158-7 – ident: 10898_CR27 doi: 10.1186/s40249-021-00847-y – ident: 10898_CR33 doi: 10.18637/jss.v070.i08 – ident: 10898_CR22 doi: 10.1038/s41467-022-35496-8 – ident: 10898_CR25 – ident: 10898_CR23 – ident: 10898_CR8 – ident: 10898_CR30 doi: 10.1016/S2666-5247(23)00005-8 – ident: 10898_CR7 doi: 10.1038/s41579-022-00822-w – volume: 22 start-page: 261 issue: 3 year: 2021 ident: 10898_CR12 publication-title: Nat Immunol. doi: 10.1038/s41590-021-00882-9 – ident: 10898_CR18 doi: 10.12688/f1000research.18002.1 – ident: 10898_CR13 – ident: 10898_CR15 – ident: 10898_CR31 doi: 10.7554/eLife.57149 – ident: 10898_CR5 doi: 10.3760/cma.j.cn112150-20220926-00925 |
| SSID | ssj0017829 |
| Score | 2.4188612 |
| Snippet | The latency time (from infection to infectiousness) guides the choice of measures required to control an infectious disease. Estimates of the SARS-CoV- 2... Background The latency time (from infection to infectiousness) guides the choice of measures required to control an infectious disease. Estimates of the... BackgroundThe latency time (from infection to infectiousness) guides the choice of measures required to control an infectious disease. Estimates of the... Abstract Background The latency time (from infection to infectiousness) guides the choice of measures required to control an infectious disease. Estimates of... |
| SourceID | doaj pubmedcentral proquest gale pubmed crossref |
| SourceType | Open Website Open Access Repository Aggregation Database Index Database |
| StartPage | 515 |
| SubjectTerms | Adolescent Adult Aged Antigens Bayesian analysis Child Child, Preschool Contact tracing COVID-19 - epidemiology COVID-19 - transmission COVID-19 - virology Data collection Disease control Doubly interval censored data Estimates Exposure Female Generalized gamma distribution Health risks Humans Infections Infectious diseases Latency Latency time Male Median (statistics) Medical research Medicine, Experimental Middle Aged Probability distribution functions Public health Quarantine Quarantine length Risk SARS-CoV- 2 SARS-CoV-2 - physiology Severe acute respiratory syndrome Time Factors Truncation Vaccines Vietnam - epidemiology Viral diseases Virus Latency Young Adult |
| SummonAdditionalLinks | – databaseName: ProQuest Health & Medical Collection dbid: 7X7 link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwfV3di9QwEA96gggien6tnhJF8EHCtU3aJk-ynh6ncAq33rJvYZKm58KZntddwf_emba7XhF92YdmWraZ73TmN4y9ClCgW6mdMFUthSohFcZLL1xItNfokSFQv_Px5-LoVH1a5IvhwK0dyio3NrEz1FXj6Yx8X-Ij84wc3NuLH4KmRtHX1WGExnV2g6DLqKSrXGwTrhS9n9k0yuhiv0VbXBpBA1zTRBst5MgZdZj9f1vmK65pXDZ5xQ8d3mV3hgCST3uO32PXQtxlN4-HT-S77HZ_EMf7_qL7rEZB4OdAofEvTpPkeVPz2fRkJg6aueAZfx_OV8B_Ys6Mm8yXkW_qs6LgECtc8fRkEQENI19uG7haTq0pfL4MqwjfH7DTww9fD47EMFxBeMwQVvjrQHmMllKjSgyryuClBqUCGOUDOMgcONTWEIzTRRmqNBiZ1KA0hNz5Sj5kO7GJ4THjWep1ApnOswow-nIgKyiDcqn3KkcLMWFvNrtsL3oMDdvlHrqwPU8s8sR2PLFywt4RI7aUhH_dXWguz-ygTrasCJlNJQB5je61dFWeQ1X7ska5k1JN2EtioyWEi0glNGewblv78cvcTjHmlB0w3r-IZicjotcDUd0g1z0MbQv46oScNaLcG1Ginvrx8kak7GAnWvtHqifsxXaZ7qTatxiaNdKk2hAKXm4m7FEvgdvNwew_S4sCV_RINke7N16Jy28dinhKpZtG6yf__19P2a2MNKaDt9xjO6vLdXiGcdjKPe-U7TcsizBU priority: 102 providerName: ProQuest |
| Title | The latency time of SARS-CoV- 2 Delta variant in infection- and vaccine-naive individuals from Vietnam |
| URI | https://www.ncbi.nlm.nih.gov/pubmed/40221669 https://www.proquest.com/docview/3201520750 https://www.proquest.com/docview/3189465359 https://pubmed.ncbi.nlm.nih.gov/PMC11993988 https://doaj.org/article/7d408540aa5f4717bd55adfc7f1fb334 |
| Volume | 25 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV3dixMxEA96gvgifl_Ps0QRfJBw3U12kzy29Y5TuFNarxRfwiSb1cKZPWwr-N872Y_SRcQXX_LQmba7M5kvMvMLIa895BhWSst0UXImJCRMO-6Y9SPlFEZk8HHe-eIyP78SH5bZcu-qr9gT1sADN4I7kUXE4BIjgKxERyptkWVQlE6W-A-c10igGPO6Yqo9P8C4p7sRGZWfrNELS83i1a3JSGnFeC8M1Wj9f_rkvaDUb5jci0BnD8j9NnWk4-aRH5JbPjwidy_aw_HHpESV02uISfAvGu-Mp1VJ5-PZnE2rBaMpfeevN0B_YnWM4qSrQLtOrMAohAIpLv4SC4AukK52o1prGodQ6GLlNwG-PyFXZ6efp-esvUaBOawFNrhaEA7zokQLiQmU9I4rEMKDFs6DhdSCRbv0XluVS18kXvNRCUKBz6wr-FNyEKrgDwlNE6dGkKosLQDzLAu8AOmFTZwTGfqCAXnbSdXcNGgZpq4yVG4aHRjUgal1YPiATKLgd5wR6br-APVvWv2bf-l_QF5FtZmIZRFis8xX2K7X5v3HhRljdslrCLy_Mc1nPaY3LVNZoZYdtAMK-OoRI6vHedzjRIt0fXK3hUzrEdaGo0lkaUzQBuTljhy_Gbvcgq-2yJMoHfHuMj0gz5odtxMO1vlpkudIUb292JNenxJW32q88CQ2aWqljv6HvJ-Te2m0oxru8pgcbH5s_QvMyzZ2SG7LpcRVTZMhuTM5vfw0G9Zmiets8uU3OA44yw |
| linkProvider | Directory of Open Access Journals |
| linkToHtml | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtR3bbtMw1BqdBEgIwbgVBhgE4mGy1sRO4jwg1N3UsrWgbqv2Zo4dZ1QayVha0H6Kb-Q4l7IIwdte8pBzYiU-9_hcCHljIUSzkmoWJylnIgKPxYYbpm1PGokWGayrdx6Nw8Gx-HgSnKyQX00tjEurbHRiqaiT3Lh_5Jsclwx8Z-A-nH9nbmqUO11tRmhUbLFvL39iyFa8H-4gfd_6_t7u0faA1VMFmEHXeI5XDcKgm4Dhe4T-RGQNlyCEhVgYCxp8DRrZ1NpYyzCyiWdj3ktBSLCBNgnHdW-QVcExlOmQ1a3d8efJ8twC7W3clObIcLNA7R_FzI2M9Xoyloy3zF85JeBvW3DFGLYTNa9Yvr175G7tstJ-xWP3yYrN1sjNUX0ov0buVL_-aFXR9ICkyHr0DJwzfknd7Hqap_SwPzlk2_mUUZ_u2LM50B8YpSNZ6SyjTUZYxihkCUKMW5llgKqYzpYlYwV1xTB0OrPzDL49JMfXsvGPSCfLM_uEUN8zsge-DPwE0N_TwBOIrNCeMSJAndQlG80uq_Oqa4cqox0ZqoomCmmiSpoo3iVbjhBLTNdxu7yRX5yqWoBVlLhecKIHEKRo0COdBAEkqYlS5HTORZe8dmRUrqdG5pJ2TmFRFGr4aar66OXyshXfv5AOJy2kdzVSmiPVDdSFEvjprldXC3O9hYmawbTBDUupWjMV6o8cdcmrJdg96bLtMpsvEMeTseu7F8Rd8rjiwOXmCHT6vDBEiGzxZmv32pBs9rXsW-65ZNFYyqf_f6-X5NbgaHSgDobj_Wfktu-kp2yuuU4684uFfY5e4Fy_qEWPki_XLe2_AQh8cJM |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=The+latency+time+of+SARS-CoV-+2+Delta+variant+in+infection-+and+vaccine-naive+individuals+from+Vietnam&rft.jtitle=BMC+infectious+diseases&rft.au=Vera+H.+Arntzen&rft.au=Manh+Nguyen+Duc&rft.au=Marta+Fiocco&rft.au=Lan+Truong+Thi+Thanh&rft.date=2025-04-12&rft.pub=BMC&rft.eissn=1471-2334&rft.volume=25&rft.issue=1&rft.spage=1&rft.epage=18&rft_id=info:doi/10.1186%2Fs12879-025-10898-3&rft.externalDBID=DOA&rft.externalDocID=oai_doaj_org_article_7d408540aa5f4717bd55adfc7f1fb334 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1471-2334&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1471-2334&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1471-2334&client=summon |