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基于废水的建模,重建和预测,对匈牙利的COVID-19疫情,由高度免疫的逃避变种引起

Péter Polcz¹, Kálmán Tornai¹, János Juhász²

¹National Laboratory for Health Security, Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Práter utca 85, Budapest, H-1083, Hungary.

|June 9, 2023

概括

基于废水的流行病学 (WBE) 提供了可靠的COVID-19监测. 将WBE与临床数据合并提高了流行病预测的准确性,揭示了匈牙利在Omicron爆发期间显著的免疫损失.

科学领域:

流行病学流行病学
计算建模计算建模
公共卫生监督是对公共卫生的监督.

背景情况:

基于废水的流行病学 (WBE) 为监测COVID-19等传染病提供了一种具有成本效益和强大的方法.
在疫情监测方面,WBE变得至关重要,特别是随着临床测试的减少.
将WBE与临床数据相结合,可以提高未来的流行病监测能力.

研究的目的:

开发一种基于废水的分区流行病模型,包括疫苗接种动态和免疫逃避.
实施数据同化方法,用于准确的流行病状态重建,参数估计和预测.
评估废水数据对流行病预测可靠性的影响.

主要方法:

开发了一个分区流行病模型,采用两相疫苗接种和免疫逃避.
使用基于多步优化的数据同化方法.
综合废水病毒载荷数据与临床指标 (医院占用,疫苗接种,死亡) 和社会距离措施.

主要成果:

废水数据显著提高了计算流行病学框架内预测的可靠性.
预测表明,在匈牙利的BA.1/BA.2和BA.5Omicron变种爆发期间,人口免疫力大幅下降.
该模型准确地评估了当前的传播率和免疫损失,以确定未来可能的流行病进展.

结论:

拟议的WBE集成模型提高了流行病监测和预测准确度.
该方法为病毒变种爆发期间的人口免疫力学提供了宝贵的见解.
这个框架可以适应其他国家的COVID-19管理和监测.

关键词:

动态系统是一个动态系统.流行病模型的流行病模型.优化优化优化优化参数估计的参数估计.基于废水的流行病学.

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