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用于新发传染病暴发监测的病毒脱落动力学:SARS-CoV-2 Alpha 和 Omicron 感染的建模方法。

Kinetics of Viral Shedding for Outbreak Surveillance of Emerging Infectious Diseases: Modeling Approach to SARS-CoV-2 Alpha and Omicron Infection.

机构信息

Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei, Taiwan.

School of Oral Hygiene, College of Oral Medicine, Taipei Medical University, Taipei, Taiwan.

出版信息

JMIR Public Health Surveill. 2024 Sep 19;10:e54861. doi: 10.2196/54861.

DOI:10.2196/54861
PMID:39298261
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11450350/
Abstract

BACKGROUND

Previous studies have highlighted the importance of viral shedding using cycle threshold (Ct) values obtained via reverse transcription polymerase chain reaction to understand the epidemic trajectories of SARS-CoV-2 infections. However, it is rare to elucidate the transition kinetics of Ct values from the asymptomatic or presymptomatic phase to the symptomatic phase before recovery using individual repeated Ct values.

OBJECTIVE

This study proposes a novel Ct-enshrined compartment model to provide a series of quantitative measures for delineating the full trajectories of the dynamics of viral load from infection until recovery.

METHODS

This Ct-enshrined compartment model was constructed by leveraging Ct-classified states within and between presymptomatic and symptomatic compartments before recovery or death among people with infections. A series of recovery indices were developed to assess the net kinetic movement of Ct-up toward and Ct-down off recovery. The model was applied to (1) a small-scale community-acquired Alpha variant outbreak under the "zero-COVID-19" policy without vaccines in May 2021 and (2) a large-scale community-acquired Omicron variant outbreak with high booster vaccination rates following the lifting of the "zero-COVID-19" policy in April 2022 in Taiwan. The model used Bayesian Markov chain Monte Carlo methods with the Metropolis-Hastings algorithm for parameter estimation. Sensitivity analyses were conducted by varying Ct cutoff values to assess the robustness of the model.

RESULTS

The kinetic indicators revealed a marked difference in viral shedding dynamics between the Alpha and Omicron variants. The Alpha variant exhibited slower viral shedding and lower recovery rates, but the Omicron variant demonstrated swifter viral shedding and higher recovery rates. Specifically, the Alpha variant showed gradual Ct-up transitions and moderate recovery rates, yielding a presymptomatic recovery index slightly higher than 1 (1.10), whereas the Omicron variant had remarkable Ct-up transitions and significantly higher asymptomatic recovery rates, resulting in a presymptomatic recovery index much higher than 1 (152.5). Sensitivity analysis confirmed the robustness of the chosen Ct values of 18 and 25 across different recovery phases. Regarding the impact of vaccination, individuals without booster vaccination had a 19% higher presymptomatic incidence rate compared to those with booster vaccination. Breakthrough infections in boosted individuals initially showed similar Ct-up transition rates but higher rates in later stages compared to nonboosted individuals. Overall, booster vaccination improved recovery rates, particularly during the symptomatic phase, although recovery rates for persistent asymptomatic infection were similar regardless of vaccination status once the Ct level exceeded 25.

CONCLUSIONS

The study provides new insights into dynamic Ct transitions, with the notable finding that Ct-up transitions toward recovery outpaced Ct-down and symptom-surfacing transitions during the presymptomatic phase. The Ct-up against Ct-down transition varies with variants and vaccination status. The proposed Ct-enshrined compartment model is useful for the surveillance of emerging infectious diseases in the future to prevent community-acquired outbreaks.

摘要

背景

先前的研究强调了使用逆转录聚合酶链反应(RT-PCR)获得的循环阈值(Ct)值来了解 SARS-CoV-2 感染的流行轨迹的重要性。然而,使用个体重复 Ct 值来阐明无症状或症状前阶段到恢复期的 Ct 值的转变动力学却很少见。

目的

本研究提出了一种新的 Ct 包裹隔室模型,为描绘从感染到恢复期间病毒载量动力学的全过程提供了一系列定量指标。

方法

该 Ct 包裹隔室模型通过利用感染人群在无症状或症状前阶段恢复或死亡之前的隔室内部和之间的 Ct 分类状态来构建。开发了一系列恢复指标来评估 Ct 上升向恢复和 Ct 下降离开恢复的净动力学运动。该模型应用于(1)2021 年 5 月在没有疫苗的“零 COVID-19”政策下发生的小规模社区获得性 Alpha 变体爆发,以及(2)2022 年 4 月在取消“零 COVID-19”政策后,具有高加强针接种率的大规模社区获得性 Omicron 变体爆发。该模型使用贝叶斯马尔可夫链蒙特卡罗方法和 Metropolis-Hastings 算法进行参数估计。通过改变 Ct 截止值进行敏感性分析,以评估模型的稳健性。

结果

动力学指标揭示了 Alpha 和 Omicron 变体之间病毒脱落动力学的显著差异。Alpha 变体表现出较慢的病毒脱落和较低的恢复率,但 Omicron 变体表现出更快的病毒脱落和更高的恢复率。具体来说,Alpha 变体表现出逐渐的 Ct 上升转变和适度的恢复率,产生的无症状恢复指数略高于 1(1.10),而 Omicron 变体则表现出显著的 Ct 上升转变和更高的无症状恢复率,导致无症状恢复指数远高于 1(152.5)。敏感性分析证实了在不同的恢复阶段选择 18 和 25 的 Ct 值的稳健性。关于疫苗接种的影响,未接种加强针的个体无症状发病率比接种加强针的个体高 19%。加强针接种者的突破性感染在最初阶段表现出相似的 Ct 上升转变率,但在后期阶段的转变率更高,而未接种加强针的个体则更高。总体而言,加强针接种提高了恢复率,尤其是在症状阶段,尽管一旦 Ct 值超过 25,无症状持续感染的恢复率无论接种状态如何都相似。

结论

本研究提供了对动态 Ct 转变的新见解,显著发现无症状前阶段的 Ct 上升向恢复的转变速度超过了 Ct 下降和症状出现的转变速度。Ct 上升与 Ct 下降的转变随变体和接种状态而异。提出的 Ct 包裹隔室模型可用于未来对新发传染病的监测,以预防社区获得性暴发。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c245/11450350/a0fd6926ec02/publichealth_v10i1e54861_fig7.jpg
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