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使用ANNN对COVID-19锁定效应的空间时间分析,以在美国各县生存,使用ANNN

Reyhane Jalali¹, Hossein Etemadfard²

¹Civil Engineering Department, Ferdowsi University of Mashhad, Mashhad, Iran.

Scientific reports

|August 23, 2024

概括

这项研究使用神经网络量化了锁定对COVID-19管理的有效性. 该FFNN模型确定了显著的幸存者数量,突出了州特定的锁定影响.

科学领域:

流行病学流行病学
公共卫生公共卫生
数据科学数据科学数据科学

背景情况:

由于COVID-19的流行,需要进行非药物干预.
全球范围内广泛实施了封锁,以制病毒传播.
量化这些措施的确切影响仍然至关重要.

研究的目的:

评估锁定政策在减少COVID-19死亡率方面的有效性.
量化美国选定的州7天内因封锁而挽救的生命数量.
为此分析,比较各种神经网络模型的预测性能.

主要方法:

收集了加利福尼亚州,乔治亚州,新泽西州和南卡罗来纳州的每日COVID-19死亡人数数据.
实施并比较了五种神经网络模型:MLP,FFNN,CFNN,ENN和NARX.
使用最佳性能模型 (FFNN) 来估计挽救的生命.

主要成果:

在测试的模型中,Feedforward神经网络 (FFNN) 显示出卓越的预测准确性.
由于封锁政策造成的估计7天生存率为211 (CA),270 (GA),989 (NJ) 和60 (SC).
FFNN模型系数的变化表明受社会人口因素和公众坚持影响的不同影响.

结论:

在COVID-19大流行期间,封锁显然挽救了生命.
封锁的有效性在各州有很大差异,受当地条件和遵守的影响.
神经网络建模提供了一种可靠的方法来量化公共卫生干预措施的影响.

关键词:

一个年龄,一个年龄.地理信息系统 (GIS) 是一个医疗保健医疗保健医疗保健医疗保健锁定封锁锁定封闭时间空间分析.

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