传输动态告知神经网络,适用于COVID-19感染
Mengqi He1, Biao Tang2, Yanni Xiao2
1School of Mathematics and Statistics, Shaanxi Normal University, Xi'an, China.
Computers in biology and medicine
|September 11, 2023
概括
一个新的传输动态信息神经网络 (TDINN) 通过整合机械和数据驱动的方法,有效地模拟了COVID-19的多个波. 这种方法揭示了波动的传播率和延迟的干预反应,导致流行病的激增.
科学领域:
- 流行病学和公共卫生.
- 计算生物学和生物信息学
- 传染病建模传染病建模
背景情况:
- 自2019年以来,COVID-19在全球范围内经历了多次浪潮,这给传统的流行病模型带来了挑战.
- 基于机制的模型难以估计多波期间的时间变化的传播能力.
- 数据驱动的深度神经网络缺乏解释性,无法识别反复爆发的驱动因素.
研究的目的:
- 开发一种基于数据的方法,用于在流行病模型中预测时间依赖的参数.
- 整合机械模型的优势与数据驱动的方法,以改善COVID-19分析.
- 提出和验证传输动态信息神经网络 (TDINN),以了解和预测流行病浪潮.
主要方法:
- 通过将SEIRD区模型编码到深度神经网络中,开发了一个传输动态信息神经网络 (TDINN).
- 将TDINN应用于来自美国,意大利,南非,肯尼亚和中国的Omicron变种爆发的COVID-19流行病数据.
- 利用数值模拟来评估TDINN的预测能力和分析传输动态.
主要成果:
- TDINN有效地与多个波的COVID-19流行数据相匹配,表现出强的性能.
- 假定的传播速率经常波动,流行病转变和传播能力变化之间的反循环驱动多个波.
- 在四个国家观察到长时间的反应延迟控制干预,与干预后中国疫情的快速下降形成鲜明对比.
结论:
- TDINN提供了一个强大的框架来分析和预测流行病模型中的时间依赖参数.
- 对干预措施的延迟反应显著增加了病例数量,突显了快速实施政策的必要性.
- 为了应对病例数的增加,行为变化可能会导致传播率下降,如中国所见.
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