Reference : Model-based assessment of COVID-19 epidemic dynamics by wastewater analysis
Scientific journals : Article
Life sciences : Multidisciplinary, general & others
Human health sciences : Immunology & infectious disease
Computational Sciences; Systems Biomedicine
http://hdl.handle.net/10993/48628
Model-based assessment of COVID-19 epidemic dynamics by wastewater analysis
English
Proverbio, Daniele mailto [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > Systems Control >]
Kemp, Francoise mailto [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > Integrative Cell Signalling >]
Magni, Stefano mailto [University of Luxembourg > > >]
Ogorzaly, Leslie mailto [Luxembourg Institute of Science & Technology - LIST > Environmental Research and Innovation Department]
Cauchie, Henry-Michel mailto [Luxembourg Institute of Science & Technology - LIST > Environmental Research and Innovation Department]
Goncalves, Jorge mailto [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > Systems Control >]
Skupin, Alexander mailto [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > Integrative Cell Signalling >]
Aalto, Atte mailto [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > Systems Control >]
2022
Science of the Total Environment
Elsevier
827
154235
Yes (verified by ORBilu)
0048-9697
1879-1026
Amsterdam
Netherlands
[en] Covid-19 ; Wastewater-based epidemiology ; Surveillance of wastewater for early epidemic prediction (SWEEP) ; Epidemiological modelling ; Kalman filter ; Early warning system
[en] 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.
Fonds National de la Recherche - FnR
Researchers ; Professionals
http://hdl.handle.net/10993/48628
10.1016/j.scitotenv.2022.154235
FnR ; FNR13684479 > Atte Aalto > DynCell > Dynamics Modelling From Single-cell Data > 01/12/2019 > 30/11/2021 > 2019

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