资源环境科技发展态势分析平台

Background:

The effective reproductive number, $R_e$, is a critical indicator to monitor disease dynamics, inform regional and national policies, and estimate the effectiveness of interventions. It describes the average number of new infections caused by a single infectious person through time. To date, $R_e$ estimates are based on clinical data such as observed cases, hospitalizations, and/or deaths. These estimates are temporarily biased when clinical testing or reporting strategies change.

Objectives:

We show that the dynamics of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA in wastewater can be used to estimate $R_e$ in near real time, independent of clinical data and without the associated biases.

Methods:

We collected longitudinal measurements of SARS-CoV-2 RNA in wastewater in Zurich, Switzerland, and San Jose, California, USA. We combined this data with information on the temporal dynamics of shedding (the shedding load distribution) to estimate a time series proportional to the daily COVID-19 infection incidence. We estimated a wastewater-based $R_e$ from this incidence.

Results:

The method to estimate $R_e$ from wastewater worked robustly on data from two different countries and two wastewater matrices. The resulting estimates were as similar to the $R_e$ estimates from case report data as $R_e$ estimates based on observed cases, hospitalizations, and deaths are among each other. We further provide details on the effect of sampling frequency and the shedding load distribution on the ability to infer $R_e$.

Discussion:

To our knowledge, this is the first time $R_e$ has been estimated from wastewater. This method provides a low-cost, rapid, and independent way to inform SARS-CoV-2 monitoring during the ongoing pandemic and is applicable to future wastewater-based epidemiology targeting other pathogens. https://doi.org/10.1289/EHP10050