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Background <p>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).</p> Methods <p>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. </p> <p>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 <Emphasis FontCategory="NonProportional">JAGS</Emphasis>, made the analyses relatively straightforward.</p> Results <p>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.</p> Conclusions <p>Using a Bayesian approach with the <Emphasis FontCategory="NonProportional">JAGS</Emphasis> 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.</p>

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The latency time of SARS-CoV- 2 Delta variant in infection- and vaccine-naive individuals from Vietnam

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年4月12日

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  • Vera H. Arntzen,
  • Manh Nguyen Duc,
  • Marta Fiocco,
  • Lan Truong Thi Thanh,
  • Tam Nguyen Hoai Thao,
  • Buu Mai Thanh,
  • Tu-Anh Nguyen,
  • Nhat Le Thanh Hoang,
  • Marc Choisy,
  • Lam Phung Khanh,
  • Nga Le Hong,
  • Ronald B. Geskus

摘要

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.