结合动态模型和深度神经网络,识别COVID-19大流行期间干预的强度
Mengqi He1, Sanyi Tang1, Yanni Xiao2
1School of Mathematics and Statistics, Shaanxi Normal University, Xi'an, China.
PLoS computational biology
|October 18, 2023
概括
一个新的深度学习算法,传输动态信息的神经网络 (TDINN),准确量化COVID-19控制措施. 它揭示了干预策略和疫情复发之间的反循环,突出了控制强度的区域差异.
科学领域:
- 流行病学 流行病学
- 计算生物学 计算生物学
- 人工智能的人工智能
背景情况:
- 有效的COVID-19控制依赖于诸如接触者追踪,隔离和隔离等干预措施.
- 量化这些措施的动态影响,特别是接触和隔离率,仍然是一个重大挑战.
研究的目的:
- 开发一种精确的方法来量化流行病期间的干预强度.
- 引入扩展传输动力学信息的神经网络 (TDINN) 算法,用于分析流行病数据.
主要方法:
- 物理信息神经网络 (PINN) 被扩展到创建TDINN算法.
- TDINN将分散的观测数据与深度学习和流行病模型相结合.
- 算法学习了疫情系统规则,没有预定义的速率函数.
主要成果:
- TDINN准确地匹配了来自中国各个城市的多个来源的COVID-19数据.
- 算法重建了流行病趋势,即使有不完整的数据.
- 推断接触和隔离率显示出显著的波动和流行病学上有意义的模式.
- 发现了干预策略和疫情复发之间的反循环.
结论:
- TDINN算法提供了一种强大的方法来量化流行病干预动态.
- 估计的比率显示了控制战略强度的区域差异.
- 研究结果强调了公共卫生干预措施与疾病复发之间的动态关系.
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