University of Luxembourg, Luxembourg Centre for Systems Biomedicine, 6 av. du Swing, Belvaux 4376, Luxembourg.
Luxembourg Institute of Science and Technology, Environmental Research and Innovation Department, Belvaux 4422, Luxembourg.
Sci Total Environ. 2022 Jun 25;827:154235. doi: 10.1016/j.scitotenv.2022.154235. Epub 2022 Mar 1.
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.
持续监测 COVID-19 的传播仍然是控制其传播和预测感染浪潮的关键。在废水中检测病毒 RNA 载量已被提议作为一种有效的流行监测和开发有效预警系统的方法。然而，其与流行状况和爆发阶段的定量关系仍难以捉摸。因此，建模对于解决这些挑战至关重要。在这项研究中，我们提出了一种新颖的基于机制模型的方法，从废水中的 SARS-CoV-2 病毒载量来重建完整的流行动态。我们的方法将嘈杂的废水数据和每日病例数整合到一个动态流行病学模型中。正如在不同地区和采样方案中所证明的那样，它可以量化病例数、提供流行指标，并准确推断未来的流行趋势。在进行定量分析后，我们还为废水数据标准提供了建议，并将其作为防范新感染浪潮的预警指标加以利用。在检测能力下降的情况下，我们的建模方法可以增强对废水的监测，以便更早地预测流行趋势，并对当地 COVID-19 动态进行稳健且具有成本效益的实时监测。
