Details

Autor(en) / Beteiligte

• Phan, Tin
• Brozak, Samantha
• Pell, Bruce
• Oghuan, Jeremiah
• Gitter, Anna
• Hu, Tao
• Ribeiro, Ruy M.
• Ke, Ruian
• Mena, Kristina D.
• Perelson, Alan S.
• Kuang, Yang
• Wu, Fuqing

Titel

Making waves: Integrating wastewater surveillance with dynamic modeling to track and predict viral outbreaks

Ist Teil von

• Water research (Oxford), 2023-09, Vol.243, p.120372-120372, Article 120372

Ort / Verlag

England: Elsevier Ltd

Erscheinungsjahr

2023

Quelle

Access via ScienceDirect (Elsevier)

Beschreibungen/Notizen

• •Dynamic models provide a mechanistic understanding of WBS data.•WBS data provides unique information that enhances dynamic models.•Between-host models can maximize the epidemiological potential of WBS data.•Within-host models can provide insight on viral shedding dynamics.•Integrating WBS and dynamic models will yield a robust predictive monitoring system. Wastewater surveillance has proved to be a valuable tool to track the COVID-19 pandemic. However, most studies using wastewater surveillance data revolve around establishing correlations and lead time relative to reported case data. In this perspective, we advocate for the integration of wastewater surveillance data with dynamic within-host and between-host models to better understand, monitor, and predict viral disease outbreaks. Dynamic models overcome emblematic difficulties of using wastewater surveillance data such as establishing the temporal viral shedding profile. Complementarily, wastewater surveillance data bypasses the issues of time lag and underreporting in clinical case report data, thus enhancing the utility and applicability of dynamic models. The integration of wastewater surveillance data with dynamic models can enhance real-time tracking and prevalence estimation, forecast viral transmission and intervention effectiveness, and most importantly, provide a mechanistic understanding of infectious disease dynamics and the driving factors. Dynamic modeling of wastewater surveillance data will advance the development of a predictive and responsive monitoring system to improve pandemic preparedness and population health. [Display omitted]