(Motivation)

Wastewater-based epidemiology (WBE) has emerged as a promising approach for monitoring the COVID-19 pandemic, since the measurement process is cost-effective and is exposed to fewer potential errors compared to other indicators like hospitalization data or the number of detected cases. Consequently, WBE was gradually becoming a key tool for epidemic surveillance and often the most reliable data source, as the intensity of clinical testing for COVID-19 drastically decreased by the third year of the pandemic. Recent results suggests that the model-based fusion of wastewater measurements with clinical data and other indicators is essential in future epidemic surveillance.

(Method)

In this work, we developed a wastewater-based compartmental epidemic model with a two-phase vaccination dynamics and immune evasion. We proposed a multi-step optimization-based data assimilation method for epidemic state reconstruction, parameter estimation, and prediction. The computations make use of the measured viral load in wastewater, the available clinical data (hospital occupancy, delivered vaccine doses, and deaths), the stringency index of the official social distancing rules, and other measures. The current state assessment and the estimation of the current transmission rate and immunity loss allow a plausible prediction of the future progression of the pandemic.

(Results)

Qualitative and quantitative evaluations revealed that the contribution of wastewater data in our computational epidemiological framework makes predictions more reliable. Predictions suggest that at least half of the Hungarian population has lost immunity during the epidemic outbreak caused by the BA.1 and BA.2 subvariants of Omicron in the first half of 2022. We obtained a similar result for the outbreaks caused by the subvariant BA.5 in the second half of 2022.

(Applicability)

The proposed approach has been used to support COVID management in Hungary and could be customized for other countries as well.

中文翻译：

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基于废水的建模、重建和预测，针对匈牙利由高度免疫逃避变异引起的 COVID-19 疫情

（动机）

基于废水的流行病学 (WBE) 已成为监测 COVID-19 大流行的一种有前途的方法，因为与住院数据或检测到的病例数等其他指标相比，测量过程具有成本效益，并且潜在错误较少。因此，随着 COVID-19 临床检测的强度在大流行的第三年急剧下降，WBE 逐渐成为流行病监测的关键工具，并且往往是最可靠的数据源。最近的结果表明，基于模型的废水测量与临床数据和其他指标的融合对于未来的流行病监测至关重要。

（方法）

在这项工作中，我们开发了一种基于废水的区室流行病模型，具有两阶段疫苗接种动态和免疫逃避。我们提出了一种基于多步优化的数据同化方法，用于流行病状态重建、参数估计和预测。这些计算利用了废水中测得的病毒载量、可用的临床数据（医院入住率、疫苗接种剂量和死亡人数）、官方社交距离规则的严格指数以及其他措施。当前状态评估以及对当前传播率和免疫力损失的估计可以对大流行的未来进展进行合理的预测。

（结果）

定性和定量评估表明，废水数据在我们的计算流行病学框架中的贡献使预测更加可靠。预测表明，在2022年上半年由Omicron的BA.1和BA.2亚变种引起的疫情爆发期间，至少有一半的匈牙利人口失去了免疫力。我们对由BA亚变种引起的疫情爆发也得到了类似的结果2022 年下半年 .5。

（适用性）

拟议的方法已用于支持匈牙利的新冠肺炎管理，也可以为其他国家进行定制。