Early detection of local SARS-CoV-2 outbreaks by wastewater surveillance: A feasibility study
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Early detection of local SARS-CoV-2 outbreaks by wastewater surveillance : A feasibility study. / Nauta, Maarten; McManus, Oliver; Træholt Franck, Kristina; Lindberg Marving, Ellinor; Dam Rasmussen, Lasse; Raith Richter, Stine; Ethelberg, Steen.
I: Epidemiology and Infection, Bind 151, e28, 2023.Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt
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TY - JOUR
T1 - Early detection of local SARS-CoV-2 outbreaks by wastewater surveillance
T2 - A feasibility study
AU - Nauta, Maarten
AU - McManus, Oliver
AU - Træholt Franck, Kristina
AU - Lindberg Marving, Ellinor
AU - Dam Rasmussen, Lasse
AU - Raith Richter, Stine
AU - Ethelberg, Steen
N1 - Publisher Copyright: © 2023 The Author(s). Published by Cambridge University Press.
PY - 2023
Y1 - 2023
N2 - Wastewater surveillance and quantitative analysis of SARS-CoV-2 RNA are increasingly used to monitor the spread of COVID-19 in the community. We studied the feasibility of applying the surveillance data for early detection of local outbreaks. A Monte Carlo simulation model was constructed, applying data on reported variation in RNA gene copy concentration in faeces and faecal masses shed. It showed that, even with a constant number of SARS-CoV-2 RNA shedders, the variation in concentrations found in wastewater samples will be large, and that it will be challenging to translate viral concentrations into incidence estimates, especially when the number of shedders is low. Potential signals for early detection of hypothetical outbreaks were analysed for their performance in terms of sensitivity and specificity of the signals. The results suggest that a sudden increase in incidence is not easily identified on the basis of wastewater surveillance data, especially in small sampling areas and in low-incidence situations. However, with a high number of shedders and when combining data from multiple consecutive tests, the performance of wastewater sampling is expected to improve considerably. The developed modelling approach can increase our understanding of the results from wastewater surveillance of SARS-CoV-2.
AB - Wastewater surveillance and quantitative analysis of SARS-CoV-2 RNA are increasingly used to monitor the spread of COVID-19 in the community. We studied the feasibility of applying the surveillance data for early detection of local outbreaks. A Monte Carlo simulation model was constructed, applying data on reported variation in RNA gene copy concentration in faeces and faecal masses shed. It showed that, even with a constant number of SARS-CoV-2 RNA shedders, the variation in concentrations found in wastewater samples will be large, and that it will be challenging to translate viral concentrations into incidence estimates, especially when the number of shedders is low. Potential signals for early detection of hypothetical outbreaks were analysed for their performance in terms of sensitivity and specificity of the signals. The results suggest that a sudden increase in incidence is not easily identified on the basis of wastewater surveillance data, especially in small sampling areas and in low-incidence situations. However, with a high number of shedders and when combining data from multiple consecutive tests, the performance of wastewater sampling is expected to improve considerably. The developed modelling approach can increase our understanding of the results from wastewater surveillance of SARS-CoV-2.
KW - COVID-19
KW - early warning
KW - modelling
KW - wastewater-based surveillance
U2 - 10.1017/S0950268823000146
DO - 10.1017/S0950268823000146
M3 - Journal article
C2 - 36722251
AN - SCOPUS:85147700801
VL - 151
JO - Epidemiology and Infection
JF - Epidemiology and Infection
SN - 0950-2688
M1 - e28
ER -
ID: 340319979