Reference : CoWWAn: Model-based assessment of COVID-19 epidemic dynamics by wastewater analysis
E-prints/Working papers : Already available on another site
Life sciences : Multidisciplinary, general & others
Human health sciences : Immunology & infectious disease
Computational Sciences; Systems Biomedicine
http://hdl.handle.net/10993/48628
CoWWAn: 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 >]
2021
No
[en] Covid-19 ; Wastewater ; Epidemics ; SEIR ; Modelling
[en] We present COVID-19 Wastewater Analyser (CoWWAn) to reconstruct the epidemic dynamics from SARS-CoV-2 viral load in wastewater. As demonstrated for various regions and sampling protocols, this mechanistic model-based approach quantifies the case numbers, provides epidemic indicators and accurately infers future epidemic trends. In situations of reduced testing capacity, analysing wastewater data with CoWWAn is a robust and cost-effective alternative for real-time surveillance of local COVID-19 dynamics.
Researchers ; Professionals
http://hdl.handle.net/10993/48628
https://www.medrxiv.org/content/10.1101/2021.10.15.21265059v1
FnR ; FNR13684479 > Atte Aalto > DynCell > Dynamics Modelling From Single-cell Data > 01/12/2019 > 30/11/2021 > 2019

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