Jana S Huisman1,2,3, Jérémie Scire2,3, Lea Caduff4, Xavier Fernandez-Cassi5, Pravin Ganesanandamoorthy4, Anina Kull4, Andreas Scheidegger4, Elyse Stachler4, Alexandria B Boehm6, Bridgette Hughes7, Alisha Knudson7, Aaron Topol7, Krista R Wigginton8, Marlene K Wolfe6, Tamar Kohn5, Christoph Ort4, Tanja Stadler2,3, Timothy R Julian4,9,10. 1. Department of Environmental Systems Science, ETH Zurich, Swiss Federal Institute of Technology, Zurich, Switzerland. 2. Swiss Institute of Bioinformatics, Lausanne, Switzerland. 3. Department of Biosystems Science and Engineering, ETH Zurich, Swiss Federal Institute of Technology, Basel, Switzerland. 4. Eawag, Swiss Federal Institute of Aquatic Science and Technology, Dübendorf, Switzerland. 5. Laboratory of Environmental Chemistry, School of Architecture, Civil and Environmental Engineering, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland. 6. Department of Civil and Environmental Engineering, Stanford University, Stanford, California, USA. 7. Verily Life Sciences, South San Francisco, California, USA. 8. Department of Civil and Environmental Engineering, University of Michigan, Ann Arbor, Michigan, USA. 9. Swiss Tropical and Public Health Institute, Basel, Switzerland. 10. University of Basel, Basel, Switzerland.
Abstract
BACKGROUND: The effective reproductive number, Re, 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, Re 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 Re 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 Re from this incidence. RESULTS: The method to estimate Re from wastewater worked robustly on data from two different countries and two wastewater matrices. The resulting estimates were as similar to the Re estimates from case report data as Re 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 Re. DISCUSSION: To our knowledge, this is the first time Re 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.
BACKGROUND: The effective reproductive number, Re, 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, Re 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 Re 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 Re from this incidence. RESULTS: The method to estimate Re from wastewater worked robustly on data from two different countries and two wastewater matrices. The resulting estimates were as similar to the Re estimates from case report data as Re 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 Re. DISCUSSION: To our knowledge, this is the first time Re 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.
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