Department of Statistics, University of California, Irvine, CA 92697, United States.
Departments of Population Health & Disease Prevention and Epidemiology & Biostatistics, University of California, Irvine, CA 92697, United States.
Biometrics. 2024 Jul 1;80(3). doi: 10.1093/biomtc/ujae074.
Concentrations of pathogen genomes measured in wastewater have recently become available as a new data source to use when modeling the spread of infectious diseases. One promising use for this data source is inference of the effective reproduction number, the average number of individuals a newly infected person will infect. We propose a model where new infections arrive according to a time-varying immigration rate which can be interpreted as an average number of secondary infections produced by one infectious individual per unit time. This model allows us to estimate the effective reproduction number from concentrations of pathogen genomes, while avoiding difficulty to verify assumptions about the dynamics of the susceptible population. As a byproduct of our primary goal, we also produce a new model for estimating the effective reproduction number from case data using the same framework. We test this modeling framework in an agent-based simulation study with a realistic data generating mechanism which accounts for the time-varying dynamics of pathogen shedding. Finally, we apply our new model to estimating the effective reproduction number of SARS-CoV-2, the causative agent of COVID-19, in Los Angeles, CA, using pathogen RNA concentrations collected from a large wastewater treatment facility.
最近，废水中病原体基因组的浓度作为一种新的数据来源，可以用于传染病传播的建模。该数据源的一个很有前途的用途是推断有效繁殖数，即每个新感染者将感染的平均人数。我们提出了一个模型，其中新的感染根据时变的移民率到达，这可以解释为一个具有传染性的个体在单位时间内产生的二次感染的平均数量。该模型允许我们从病原体基因组的浓度来估计有效繁殖数，同时避免了对易感染人群动态的假设进行验证的困难。作为我们主要目标的副产品，我们还使用相同的框架从病例数据中估计有效繁殖数的新模型。我们在一个具有现实数据生成机制的基于代理的模拟研究中测试了这个建模框架，该机制考虑了病原体脱落的时变动态。最后，我们使用从加利福尼亚州洛杉矶的一个大型废水处理设施收集的病原体 RNA 浓度，应用我们的新模型来估计导致 COVID-19 的 SARS-CoV-2 的有效繁殖数。
