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传染病时间序列的整体标签,以评估早期预警系统

Andreas Hicketier¹, Moritz Bach¹, Philip Oedi¹

¹Robert Koch Institute, Infectious Disease Epidemiology, Seestraße 10, 13353, Berlin, Germany.

Infectious Disease Modelling

|February 16, 2026

概括

我们开发了一种适应性标签方法,以评估疾病爆发的早期预警系统 (EWS). 这种方法准确地标记了COVID-19疫情,与传统方法相比,提高了机器学习模型的性能.

科学领域:

流行病学流行病学
数据科学数据科学数据科学
公共卫生信息学公共卫生信息学

背景情况:

早期预警系统 (EWS) 在疾病爆发期间对公共卫生决策至关重要.
现有的COVID-19 EWS利用了移动性和社交媒体数据等共变量,但由于缺乏基本真相,面临评估挑战.
当前的后期标签方法与异质和非静止的COVID-19时间序列数据作斗争.

研究的目的:

通过提出一种新的适应性标签方法来解决EWS的评估差距.
在复杂的COVID-19时间序列中生成可靠的时间索引标签,用于类似疫情的时期.
提高机器学习模型的训练和性能,以检测疫情爆发.

主要方法:

开发了一套定制的自适应标签方法.
将该方法应用于异质的,非静止的COVID-19时间序列数据.
利用自制的标签来训练和评估与传统方法相比的机器学习模型.

主要成果:

适应性标签方法在不同空间分辨率的不同爆发模式 (波浪,峰值) 中始终产生有用的标签.
用拟议的标签进行训练的机器学习模型与传统的无监督爆发检测算法相比,表现出更高的性能.
该方法有效地处理了COVID-19时间序列固有的异质性和非静止性.

结论:

拟议的自适应标签方法为对复杂的疫情数据进行EWS评估提供了强大的解决方案.
这种方法提高了疫情检测模型的准确性和可靠性,特别是对于COVID-19等新出现的传染病.
这些发现表明了评估疾病监测系统有效性的新标准.

关键词:

在 COVID-19 疫情中,早期预警系统早期预警系统疫情爆发的检测和检测.监督监督监督监督监督监督

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