基于流动性的COVID-19病例预测模型的公平性评估
Abdolmajid Erfani1, Vanessa Frias-Martinez2,3
1Department of Civil, Environmental, and Geospatial Engineering, Michigan Technological University, Houghton, MI, United States of America.
PloS one
|October 18, 2023
概括
使用移动数据的COVID-19预测模型显示偏差,对老年,贫穷和农村人口的表现不太准确. 这凸显了需要更具代表性的数据收集,以确保所有人口群体之间公平的模型性能.
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 公共卫生
背景情况:
- 人类流动性分析对于了解COVID-19传播和干预效果至关重要.
- 公共可用的移动数据有助于研究时空趋势和疾病传播.
- 基于流动性的预测模型在人口统计学中的公平表现仍然是一个开放的问题.
研究的目的:
- 为了调查基于移动性的COVID-19预测模型是否在不同的人口群体中表现差异.
- 测试这种假设,即移动数据中的偏差有助于不公平的模型准确性.
- 确定与模型性能变化相关的特定社会人口统计特征.
主要方法:
- 在美国县级应用了两种基于移动性的COVID-19感染预测模型.
- 利用SafeGraph移动数据进行模型培训和评估.
- 与县级社会人口统计数据相关联的模型性能指标.
主要成果:
- 在人口统计学特征中观察到模型性能存在显著的系统偏差.
- 模型对大,受过教育,富裕,年轻和城市县的准确性更高.
- 对于老年,贫穷,受教育程度较低和农村人口的预测,准确性较低.
结论:
- 当前预测模型中使用的移动数据可能不足以代表某些人群,导致偏见的COVID-19预测.
- 需要改进数据收集和抽样策略,以确保全面的流动模式代表性.
- 解决数据偏差对于开发公平,准确的公共卫生监测工具至关重要.
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