Model-based assessment of COVID-19 epidemic dynamics by wastewater analysis.
View / Open Files
Authors
Proverbio, Daniele
Kemp, Françoise
Magni, Stefano
Ogorzaly, Leslie
Cauchie, Henry-Michel
Gonçalves, Jorge
Skupin, Alexander
Aalto, Atte
Publication Date
2022-03-01Journal Title
Sci Total Environ
ISSN
0048-9697
Publisher
Elsevier BV
Number
154235
Pages
154235-154235
Type
Article
This Version
AM
Metadata
Show full item recordCitation
Proverbio, D., Kemp, F., Magni, S., Ogorzaly, L., Cauchie, H., Gonçalves, J., Skupin, A., & et al. (2022). Model-based assessment of COVID-19 epidemic dynamics by wastewater analysis.. Sci Total Environ, (154235), 154235-154235. https://doi.org/10.1016/j.scitotenv.2022.154235
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.
Embargo Lift Date
2023-03-31
Identifiers
External DOI: https://doi.org/10.1016/j.scitotenv.2022.154235
This record's URL: https://www.repository.cam.ac.uk/handle/1810/334651
Rights
Attribution-NonCommercial-NoDerivatives 4.0 International
Licence URL: https://creativecommons.org/licenses/by-nc-nd/4.0/
Statistics
Total file downloads (since January 2020). For more information on metrics see the
IRUS guide.
Recommended or similar items
The current recommendation prototype on the Apollo Repository will be turned off on 03 February 2023. Although the pilot has been fruitful for both parties, the service provider IKVA is focusing on horizon scanning products and so the recommender service can no longer be supported. We recognise the importance of recommender services in supporting research discovery and are evaluating offerings from other service providers. If you would like to offer feedback on this decision please contact us on: support@repository.cam.ac.uk