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使用多菌株传染病模型与物理信息神经网络来研究SARS-CoV-2变种的时间依赖性

Wenxuan Li¹, Xu Chen², Suli Liu¹

¹School of Mathematics, Jilin University, Changchun, Jilin, China.

PLoS computational biology

|February 14, 2025

概括

一个新的VOCs-INN算法使用流行病模型和神经网络准确地模拟SARS-CoV-2变种的传播. 这种方法有效地适应数据,预测未来的趋势,并揭示区域控制策略的变化.

科学领域:

流行病学和公共卫生.
计算生物学和生物信息学
人工智能在医学中的应用

背景情况:

自2020年底以来,SARS-CoV-2的持续演变导致了多种关注变体 (VOC) 和感兴趣变体 (VOI) 的出现.
这些变种在毒性,抗原性和传染性方面表现出不同的特征,需要适应性防御和控制策略.
对变种传播的准确建模对于有效的公共卫生干预至关重要.

研究的目的:

开发和验证一种新的计算框架,用于模拟和预测SARS-CoV-2变种的传播.
将传染病模型与物理信息神经网络 (PINNs) 集成,创建一个VOCs-informed神经网络 (VOCs-INN).
评估算法在匹配报告数据,推断参数和预测疾病动态方面的性能.

主要方法:

开发一种新的传染病模型来代表SARS-CoV-2变种传播动态.
使用物理信息神经网络 (PINNs) 将流行病模型与报告的变种感染数据集成.
创建一个名为VOCs-informed神经网络 (VOCs-INN) 的专用算法,用于数据拟合和预测.

主要成果:

该VOCs-INN算法在匹配报告的SARS-CoV-2变种数据方面表现出高准确性,用于加拿大不列颠哥伦比亚省及其卫生机构.
该模型成功模拟了观察到的和未观察到的疾病动态,推断了依赖时间的流行病学参数,并提供了准确的短期预测.
分析显示,在不同地区实施的控制策略的强度存在显著差异,突出了对本地化方法的需求.

结论:

挥发性有机物-INN框架为模拟SARS-CoV-2变种传播提供了一个强大而准确的机制.
该算法的执行拟合,参数推理和预测的能力使其成为公共卫生决策的宝贵工具.
在分析长期和多波流行病数据方面,VOCs-INN表现出强的表现,支持适应性流行病应对策略.

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