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测量最近成为建模传染病传播的新数据源。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。我们提出一个模型，其中新感染按照时变迁入率到达，该迁入率可解释为复合参数——等于人群中易感者比例与传播率的乘积。该模型允许我们通过避开关于易感人群动态难以验证的假设，从病原体基因组浓度估计有效再生数。作为主要目标的副产品，我们还开发了一个使用相同框架从病例数据估计有效再生数的新模型。我们在基于智能体的模拟研究中测试该建模框架，该研究采用考虑病原体脱落时变动态的现实数据生成机制。最后，我们将新模型应用于估算加利福尼亚州洛杉矶市SARS-CoV-2的有效再生数，数据来自大型废水处理设施收集的病原体RNA浓度。