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 a compound parameter equal to the product of the proportion of susceptibles in the population and the transmission rate. This model allows us to estimate the effective reproduction number from concentrations of pathogen genomes while avoiding difficult 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 in Los Angeles, California, using pathogen RNA concentrations collected from a large wastewater treatment facility.

翻译：废水中的病原体基因组浓度近年来可作为建模传染病传播的新型数据源。该数据源的一个有前景的应用是推断有效再生数，即每个新感染者平均感染的人数。我们提出一种模型，其中新感染病例根据时变迁入率发生，该迁入率可解释为一个复合参数，等于易感人群比例与传播率的乘积。该模型使我们能够通过病原体基因组浓度估算有效再生数，同时避免对易感人群动态做出难以验证的假设。作为主要目标的副产品，我们还提出一种新模型，利用相同框架从病例数据中估算有效再生数。我们在基于智能体的仿真研究中测试该建模框架，采用考虑病原体排毒时变动态的现实数据生成机制。最后，我们将新模型应用于加利福尼亚州洛杉矶市SARS-CoV-2的有效再生数估算，所用数据来自大型废水处理设施收集的病原体RNA浓度。