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Huisman JS, Scire J, Caduff L, Fernandez-Cassi X, Ganesanandamoorthy P, Kull A, Scheidegger A, Stachler E, Boehm AB, Hughes B, Knudson A, Topol A, Wigginton KR, Wolfe MK, Kohn T, Ort C, Stadler T, Julian TR. Wastewater-Based Estimation of the Effective Reproductive Number of SARS-CoV-2. Environ Health Perspect 2022; 130:57011. [PMID: 35617001 PMCID: PMC9135136 DOI: 10.1289/ehp10050] [Citation(s) in RCA: 46] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/29/2021] [Revised: 04/09/2022] [Accepted: 04/26/2022] [Indexed: 05/05/2023]
Abstract
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.
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Affiliation(s)
- Jana S. Huisman
- Department of Environmental Systems Science, ETH Zurich, Swiss Federal Institute of Technology, Zurich, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
- Department of Biosystems Science and Engineering, ETH Zurich, Swiss Federal Institute of Technology, Basel, Switzerland
| | - Jérémie Scire
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
- Department of Biosystems Science and Engineering, ETH Zurich, Swiss Federal Institute of Technology, Basel, Switzerland
| | - Lea Caduff
- Eawag, Swiss Federal Institute of Aquatic Science and Technology, Dübendorf, Switzerland
| | - Xavier Fernandez-Cassi
- Laboratory of Environmental Chemistry, School of Architecture, Civil and Environmental Engineering, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | | | - Anina Kull
- Eawag, Swiss Federal Institute of Aquatic Science and Technology, Dübendorf, Switzerland
| | - Andreas Scheidegger
- Eawag, Swiss Federal Institute of Aquatic Science and Technology, Dübendorf, Switzerland
| | - Elyse Stachler
- Eawag, Swiss Federal Institute of Aquatic Science and Technology, Dübendorf, Switzerland
| | - Alexandria B. Boehm
- Department of Civil and Environmental Engineering, Stanford University, Stanford, California, USA
| | | | - Alisha Knudson
- Verily Life Sciences, South San Francisco, California, USA
| | - Aaron Topol
- Verily Life Sciences, South San Francisco, California, USA
| | - Krista R. Wigginton
- Department of Civil and Environmental Engineering, University of Michigan, Ann Arbor, Michigan, USA
| | - Marlene K. Wolfe
- Department of Civil and Environmental Engineering, Stanford University, Stanford, California, USA
| | - Tamar Kohn
- Laboratory of Environmental Chemistry, School of Architecture, Civil and Environmental Engineering, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Christoph Ort
- Eawag, Swiss Federal Institute of Aquatic Science and Technology, Dübendorf, Switzerland
| | - Tanja Stadler
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
- Department of Biosystems Science and Engineering, ETH Zurich, Swiss Federal Institute of Technology, Basel, Switzerland
| | - Timothy R. Julian
- Eawag, Swiss Federal Institute of Aquatic Science and Technology, Dübendorf, Switzerland
- Swiss Tropical and Public Health Institute, Basel, Switzerland
- University of Basel, Basel, Switzerland
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Huisman JS, Scire J, Caduff L, Fernandez-Cassi X, Ganesanandamoorthy P, Kull A, Scheidegger A, Stachler E, Boehm AB, Hughes B, Knudson A, Topol A, Wigginton KR, Wolfe MK, Kohn T, Ort C, Stadler T, Julian TR. Wastewater-Based Estimation of the Effective Reproductive Number of SARS-CoV-2. Environ Health Perspect 2022; 130:57011. [PMID: 35617001 DOI: 10.1101/2021.04.29.21255961] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/20/2023]
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.
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Affiliation(s)
- Jana S Huisman
- Department of Environmental Systems Science, ETH Zurich, Swiss Federal Institute of Technology, Zurich, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
- Department of Biosystems Science and Engineering, ETH Zurich, Swiss Federal Institute of Technology, Basel, Switzerland
| | - Jérémie Scire
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
- Department of Biosystems Science and Engineering, ETH Zurich, Swiss Federal Institute of Technology, Basel, Switzerland
| | - Lea Caduff
- Eawag, Swiss Federal Institute of Aquatic Science and Technology, Dübendorf, Switzerland
| | - Xavier Fernandez-Cassi
- Laboratory of Environmental Chemistry, School of Architecture, Civil and Environmental Engineering, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | | | - Anina Kull
- Eawag, Swiss Federal Institute of Aquatic Science and Technology, Dübendorf, Switzerland
| | - Andreas Scheidegger
- Eawag, Swiss Federal Institute of Aquatic Science and Technology, Dübendorf, Switzerland
| | - Elyse Stachler
- Eawag, Swiss Federal Institute of Aquatic Science and Technology, Dübendorf, Switzerland
| | - Alexandria B Boehm
- Department of Civil and Environmental Engineering, Stanford University, Stanford, California, USA
| | | | - Alisha Knudson
- Verily Life Sciences, South San Francisco, California, USA
| | - Aaron Topol
- Verily Life Sciences, South San Francisco, California, USA
| | - Krista R Wigginton
- Department of Civil and Environmental Engineering, University of Michigan, Ann Arbor, Michigan, USA
| | - Marlene K Wolfe
- Department of Civil and Environmental Engineering, Stanford University, Stanford, California, USA
| | - Tamar Kohn
- Laboratory of Environmental Chemistry, School of Architecture, Civil and Environmental Engineering, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Christoph Ort
- Eawag, Swiss Federal Institute of Aquatic Science and Technology, Dübendorf, Switzerland
| | - Tanja Stadler
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
- Department of Biosystems Science and Engineering, ETH Zurich, Swiss Federal Institute of Technology, Basel, Switzerland
| | - Timothy R Julian
- Eawag, Swiss Federal Institute of Aquatic Science and Technology, Dübendorf, Switzerland
- Swiss Tropical and Public Health Institute, Basel, Switzerland
- University of Basel, Basel, Switzerland
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