Model-based assessment of COVID-19 epidemic dynamics by wastewater analysis.


Type
Article
Change log
Authors
Proverbio, Daniele 
Kemp, Françoise 
Magni, Stefano 
Ogorzaly, Leslie 
Cauchie, Henry-Michel 
Abstract

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.

Description
Keywords
Journal Title
Sci Total Environ
Conference Name
Journal ISSN
0048-9697
1879-1026
Volume Title
Publisher
Elsevier BV