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

Daniele Proverbio, Françoise Kemp, Stefano Magni, Leslie Ogorzaly, Henry-Michel Cauchie, Jorge Gonçalves, Alexander Skupin, Atte Aalto*

*Corresponding author for this work

Research output: Contribution to journalArticleResearchpeer-review

10 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)154235
JournalScience of the Total Environment
Publication statusPublished - 25 Jun 2022


  • COVID-19/epidemiology
  • Humans
  • RNA, Viral
  • SARS-CoV-2
  • Waste Water
  • Wastewater-Based Epidemiological Monitoring


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