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Ángel Fragua¹, Antonio Jiménez-Martín², Alfonso Mateos¹

¹Decision Analysis and Statistics Group, Universidad Politécnica de Madrid, 28660, Boadilla del Monte, Spain.

Scientific reports

|October 24, 2023

概括

网络分析方法准确地预测了欧洲的COVID-19疫情,提供了20天的预警. 动态飞行数据增强了预测,优于早期检测的动态网络标记.

科学领域:

流行病学流行病学
网络科学网络科学
公共卫生公共卫生

背景情况:

航空运输促进了COVID-19在欧洲的早期传播.
有效的早期预警系统对于管理流行病爆发至关重要.

研究的目的:

评估动态网络标记 (DNM) 和网络分析方法,以预测欧洲的COVID-19疫情.
通过使用空中交通数据评估这些方法的准确性和预测能力.

主要方法:

对2020年2月15日至5月1日的COVID-19病例数据 (WHO) 和欧洲空中交通数据 (Flightradar24) 的分析.
动态网络标记 (DNM) 和基于网络分析的方法与四个相邻矩阵的比较.
基于每日确诊病例的准确性和预测预发时间的评估.

主要成果:

由于复杂的输出,大多数DNM被丢弃;单次系列DNM未能准确模拟COVID-19趋势.
几种网络分析方法与特定的相邻矩阵相结合,证明了高准确性,并提供了高达20天的预测.
网络密度和边缘计数方法在使用动态飞行频率数据时显示出略高的性能.

结论:

基于网络分析的方法,特别是将动态飞行数据纳入其中时,作为传染病爆发的早期预警系统显著有前途.
这些方法为预测COVID-19传播提供了一种可行的方法,其性能优于经过测试的动态网络标记.
这些发现突显了网络科学在公共卫生监测和流行病准备方面的实用性.

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