Phan Tin, Brozak Samantha, Pell Bruce, Ciupe Stanca M, Ke Ruian, Ribeiro Ruy M, Gitter Anna, Mena Kristina D, Perelson Alan S, Kuang Yang, Wu Fuqing
Theoretical Biology and Biophysics, Los Alamos National Laboratory, Los Alamos, NM, USA.
School of Mathematical and Statistical Sciences, Arizona State University, Tempe, AZ, USA.
Commun Med (Lond). 2025 May 22;5(1):193. doi: 10.1038/s43856-025-00908-5.
The prolonged viral shedding from the gastrointestinal tract is well documented for numerous pathogens, including SARS-CoV-2. However, the impact of prolonged viral shedding on epidemiological inferences using wastewater data is not yet fully understood.
To gain a better understanding of this phenomenon at the population level, we extended a wastewater-based modeling framework that integrates viral shedding dynamics, viral load data in wastewater, case report data, and an epidemic model.
Our results indicate that as an outbreak progresses, the viral load from recovered individuals gradually becomes predominant, surpassing that from the infectious population. This phenomenon leads to a dynamic relationship between model-inferred and reported daily incidence over the course of an outbreak. Sensitivity analyses on the duration and rate of viral shedding for recovered individuals reveal that accounting for this phenomenon can considerably advance prediction of transmission peak timing. Furthermore, extensive viral shedding from the recovered population toward the conclusion of an epidemic wave may overshadow viral signals from newly infected cases carrying emerging variants, which can delay the rapid recognition of emerging variants based on viral load.
These findings highlight the necessity of integrating post-recovery viral shedding to enhance the accuracy and utility of wastewater-based epidemiological analysis.
包括严重急性呼吸综合征冠状病毒2(SARS-CoV-2)在内的众多病原体都有胃肠道病毒长期脱落的充分记录。然而,长期病毒脱落对利用废水数据进行的流行病学推断的影响尚未完全了解。
为了在人群层面更好地理解这一现象,我们扩展了一个基于废水的建模框架,该框架整合了病毒脱落动态、废水中的病毒载量数据、病例报告数据和一个流行病模型。
我们的结果表明,随着疫情的发展,康复个体的病毒载量逐渐占主导地位,超过了感染人群的病毒载量。这种现象导致了疫情期间模型推断的每日发病率与报告的每日发病率之间的动态关系。对康复个体病毒脱落持续时间和速率的敏感性分析表明,考虑这一现象可以显著提前对传播高峰时间的预测。此外,在疫情波接近尾声时,康复人群的大量病毒脱落可能会掩盖携带新出现变体的新感染病例的病毒信号,这可能会延迟基于病毒载量对新出现变体的快速识别。
这些发现强调了整合康复后病毒脱落以提高基于废水的流行病学分析的准确性和实用性的必要性。