Kadoya Syun-Suke, Li Yubing, Wang Yilei, Katayama Hiroyuki, Sano Daisuke
Department of Urban Engineering, The University of Tokyo, 7-3-1, Hongo, Bunkyo-ku, Tokyo 113-8656, Japan.
Department of Frontier Science for Advanced Environment, Graduate School of Environmental Studies, Tohoku University, Aoba 6-6-06, Aramaki, Aoba-ku, Sendai, Miyagi 980-8579, Japan.
J R Soc Interface. 2025 Jan;22(222):20240456. doi: 10.1098/rsif.2024.0456. Epub 2025 Jan 8.
The current situation of COVID-19 measures makes it difficult to accurately assess the prevalence of SARS-CoV-2 due to a decrease in reporting rates, leading to missed initial transmission events and subsequent outbreaks. There is growing recognition that wastewater virus data assist in estimating potential infections, including asymptomatic and unreported infections. Understanding the COVID-19 situation hidden behind the reported cases is critical for decision-making when choosing appropriate social intervention measures. However, current models implicitly assume homogeneity in human behaviour, such as virus shedding patterns within the population, making it challenging to predict the emergence of new variants due to variant-specific transmission or shedding parameters. This can result in predictions with considerable uncertainty. In this study, we established a state-space model based on wastewater viral load to predict both reported cases and potential infection numbers. Our model using wastewater virus data showed high goodness-of-fit to COVID-19 case numbers despite the dataset including waves of two distinct variants. Furthermore, the model successfully provided estimates of potential infection, reflecting the superspreading nature of SARS-CoV-2 transmission. This study supports the notion that wastewater surveillance and state-space modelling have the potential to effectively predict both reported cases and potential infections.
由于报告率下降，新冠疫情防控措施的现状使得准确评估新冠病毒的流行情况变得困难，导致最初的传播事件和随后的疫情爆发被遗漏。人们越来越认识到，污水病毒数据有助于估计潜在感染情况，包括无症状和未报告的感染。了解报告病例背后隐藏的新冠疫情情况对于选择适当的社会干预措施时的决策至关重要。然而，当前的模型隐含地假设人类行为具有同质性，例如人群中的病毒脱落模式，这使得由于变异株特异性传播或脱落参数而预测新变异株的出现具有挑战性。这可能导致预测存在相当大的不确定性。在本研究中，我们建立了一个基于污水病毒载量的状态空间模型，以预测报告病例数和潜在感染数。我们使用污水病毒数据的模型对新冠病例数显示出很高的拟合优度，尽管数据集包括两个不同变异株的波次。此外，该模型成功地提供了潜在感染的估计值，反映了新冠病毒传播的超级传播特性。本研究支持这样一种观点，即污水监测和状态空间建模有潜力有效预测报告病例和潜在感染情况。
