审计美国COVID-19预测中心病例预测模型的公平性
Saad Mohammad Abrar1, Naman Awasthi1, Daniel Smolyak1
1Department of Computer Science, University of Maryland, College Park, Maryland, United States of America.
PloS one
|April 22, 2025
概括
美国预测中心的COVID-19预测显示了有偏见的预测. 少数群体和较少城市地区的预测错误较高,突出了公共卫生建模中公平度量的需要.
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:
- 美国COVID-19预测中心为疾病控制和预防中心 (CDC) 汇总了众多研究小组的预测.
- 虽然它对准确性有价值,但它在各种社会决定因素上的表现仍然未被评估.
- 众所周知,种族和城市化等社会因素会影响流行病的结果.
研究的目的:
- 从美国预测中心对COVID-19模型预测进行全面的公平性分析.
- 评估不同健康社会决定因素之间的模型性能变化.
- 鼓励公平和准确性在流行病学建模的准确性报告的指标.
主要方法:
- 利用了来自美国COVID-19预测中心的数据,包括来自50多个研究小组的预测.
- 进行了公平性分析,评估了人口 (种族/种族) 和地理 (城市化) 因素的预测性能.
- 采用统计方法来确定预测准确度的显著差异.
主要成果:
- 在跨社会决定因素的模型预测性表现中确定了统计学上显著的差异.
- 观察到较高的预测错误对于少数民族的种族和民族群体.
- 发现与城市化程度较低的地区相关的预测错误增加.
结论:
- COVID-19预测模型表现出与社会决定因素相关的绩效偏差.
- 公平性指标对于理解和减轻对弱势群体的潜在伤害至关重要.
- 疾病预防控制中心和建模人员应将公平性评估纳入其预测和报告实践中.
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