tetano
Editor, Senior Moderator
Environ Health Perspect
. 2022 May;130(5):57011.
doi: 10.1289/EHP10050. Epub 2022 May 26.
Wastewater-Based Estimation of the Effective Reproductive Number of SARS-CoV-2
Jana S Huisman[SUP] 1 2 3 [/SUP], Jérémie Scire[SUP] 2 3 [/SUP], Lea Caduff[SUP] 4 [/SUP], Xavier Fernandez-Cassi[SUP] 5 [/SUP], Pravin Ganesanandamoorthy[SUP] 4 [/SUP], Anina Kull[SUP] 4 [/SUP], Andreas Scheidegger[SUP] 4 [/SUP], Elyse Stachler[SUP] 4 [/SUP], Alexandria B Boehm[SUP] 6 [/SUP], Bridgette Hughes[SUP] 7 [/SUP], Alisha Knudson[SUP] 7 [/SUP], Aaron Topol[SUP] 7 [/SUP], Krista R Wigginton[SUP] 8 [/SUP], Marlene K Wolfe[SUP] 6 [/SUP], Tamar Kohn[SUP] 5 [/SUP], Christoph Ort[SUP] 4 [/SUP], Tanja Stadler[SUP] 2 3 [/SUP], Timothy R Julian[SUP] 4 9 10 [/SUP]
Affiliations
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.
. 2022 May;130(5):57011.
doi: 10.1289/EHP10050. Epub 2022 May 26.
Wastewater-Based Estimation of the Effective Reproductive Number of SARS-CoV-2
Jana S Huisman[SUP] 1 2 3 [/SUP], Jérémie Scire[SUP] 2 3 [/SUP], Lea Caduff[SUP] 4 [/SUP], Xavier Fernandez-Cassi[SUP] 5 [/SUP], Pravin Ganesanandamoorthy[SUP] 4 [/SUP], Anina Kull[SUP] 4 [/SUP], Andreas Scheidegger[SUP] 4 [/SUP], Elyse Stachler[SUP] 4 [/SUP], Alexandria B Boehm[SUP] 6 [/SUP], Bridgette Hughes[SUP] 7 [/SUP], Alisha Knudson[SUP] 7 [/SUP], Aaron Topol[SUP] 7 [/SUP], Krista R Wigginton[SUP] 8 [/SUP], Marlene K Wolfe[SUP] 6 [/SUP], Tamar Kohn[SUP] 5 [/SUP], Christoph Ort[SUP] 4 [/SUP], Tanja Stadler[SUP] 2 3 [/SUP], Timothy R Julian[SUP] 4 9 10 [/SUP]
Affiliations
- PMID: 35617001
- DOI: 10.1289/EHP10050
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.