TY - JOUR
T1 - Model-based assessment of COVID-19 epidemic dynamics by wastewater analysis
AU - Proverbio, Daniele
AU - Kemp, Françoise
AU - Magni, Stefano
AU - Ogorzaly, Leslie
AU - Cauchie, Henry-Michel
AU - Gonçalves, Jorge
AU - Skupin, Alexander
AU - Aalto, Atte
N1 - D.P. and S.M. are supported by the Luxembourg National Research Fund (FNR) through PRIDE15/10907093/CriTiCS and F.K. by the FNR project PRIDE17/12244779/PARK-QC. A.A. is supported by the FNR through CORE19/13684479/DynCell. L.O. and H.M.C. are supported by the FNR through the COVID-19-FT2/14806023/Coronastep+. J.G. is partly supported by the 111 Project on Computational Intelligence and Intelligent Control, ref. B18024. The authors want to thank the Research Luxembourg COVID-19 Task Force for general support and collaborative spirit.
Copyright © 2022 The Authors. Published by Elsevier B.V. All rights reserved.
PY - 2022/6/25
Y1 - 2022/6/25
N2 - Continuous surveillance of COVID-19 diffusion remains crucial to control its diffusion and to anticipate infection waves. Detecting viral RNA load in wastewater samples has been suggested as an effective approach for epidemic monitoring and the development of an effective warning system. However, its quantitative link to the epidemic status and the stages of outbreak is still elusive. Modelling is thus crucial to address these challenges. In this study, we present a novel mechanistic model-based approach to reconstruct the complete epidemic dynamics from SARS-CoV-2 viral load in wastewater. Our approach integrates noisy wastewater data and daily case numbers into a dynamical epidemiological model. As demonstrated for various regions and sampling protocols, it quantifies the case numbers, provides epidemic indicators and accurately infers future epidemic trends. Following its quantitative analysis, we also provide recommendations for wastewater data standards and for their use as warning indicators against new infection waves. In situations of reduced testing capacity, our modelling approach can enhance the surveillance of wastewater for early epidemic prediction and robust and cost-effective real-time monitoring of local COVID-19 dynamics.
AB - Continuous surveillance of COVID-19 diffusion remains crucial to control its diffusion and to anticipate infection waves. Detecting viral RNA load in wastewater samples has been suggested as an effective approach for epidemic monitoring and the development of an effective warning system. However, its quantitative link to the epidemic status and the stages of outbreak is still elusive. Modelling is thus crucial to address these challenges. In this study, we present a novel mechanistic model-based approach to reconstruct the complete epidemic dynamics from SARS-CoV-2 viral load in wastewater. Our approach integrates noisy wastewater data and daily case numbers into a dynamical epidemiological model. As demonstrated for various regions and sampling protocols, it quantifies the case numbers, provides epidemic indicators and accurately infers future epidemic trends. Following its quantitative analysis, we also provide recommendations for wastewater data standards and for their use as warning indicators against new infection waves. In situations of reduced testing capacity, our modelling approach can enhance the surveillance of wastewater for early epidemic prediction and robust and cost-effective real-time monitoring of local COVID-19 dynamics.
KW - COVID-19/epidemiology
KW - Humans
KW - RNA, Viral
KW - SARS-CoV-2
KW - Waste Water
KW - Wastewater-Based Epidemiological Monitoring
UR - https://pubmed.ncbi.nlm.nih.gov/35245552
U2 - 10.1016/j.scitotenv.2022.154235
DO - 10.1016/j.scitotenv.2022.154235
M3 - Article
C2 - 35245552
SN - 0048-9697
VL - 827
SP - 154235
JO - Science of the Total Environment
JF - Science of the Total Environment
ER -