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Updated: Jul 7, 2025

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机器学习识别了丹麦人口中COVID-19后长期病假相关的风险因素

Kim Daniel Jakobsen1, Elisabeth O'Regan2, Ingrid Bech Svalgaard2

  • 1Department of Epidemiology Research, Statens Serum Institut, Copenhagen, Denmark. kija@ssi.dk.

Communications medicine
|December 20, 2023
PubMed
概括

后COVID-19状态 (PCC) 显著增加长期病假,风险因个人因素而异. 识别这些风险群体,如高BMI和抑郁症患者,对于有针对性的干预至关重要.

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In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
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Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:
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