弹性净回归的应用用于模拟COVID-19社会人口统计风险因素
Tristan A Moxley1,2, Jennifer Johnson-Leung2,3, Erich Seamon3
1Bioinformatics and Computational Biology Program, University of Idaho, Moscow, ID, United States of America.
PloS one
|January 26, 2024
概括
社会脆弱性,包括少数群体地位和残疾,与COVID-19病例率相关. 较高的民主党投票与较少的病例有关,突出了差异,并为未来的流行病提供了资源分配的信息.
科学领域:
- 流行病学 流行病学
- 公共卫生 公共卫生
- 社会学 社会学 社会学
背景情况:
- COVID-19在2019年12月出现,需要了解疾病传播的社会驱动因素.
- 县级社会人口统计数据对于减轻流行病影响至关重要.
研究的目的:
- 调查社会脆弱性指数 (SVI) 与COVID-19病例率之间的相关性.
- 确定影响县级COVID-19发病率的特定社会人口因素.
主要方法:
- 使用弹性净回归来解决可变对线性和模型过拟合的问题.
- 分析采用了10个卫生和人类服务 (HHS) 区域,用于两个不同的时间段的子模型 (德尔塔变种前和德尔塔变种激增).
主要成果:
- 弹性净回归在多重回归上显示出更好的预测准确性,由较低的根平均平方误差 (RMSE) 和令人满意的R2系数证明.
- 变量重要性图片 (VIPs) 显示了COVID-19攻击率与少数民族,残疾人,群体季度中的个人和民主党选民的百分比之间存在显著的相关性.
结论:
- 少数群体的百分比与早期的COVID-19病例呈现出正相关性,后来转向负相关性,与先前的研究保持一致.
- 较高的残疾人百分比与早期病例有负相关性,而群体季度人口的早期发病率较高,疫苗接种后减少.
- 民主党更高的投票率和病例之间的持续负相关性表明存在党派分歧,在未来的公共卫生危机中为脆弱县分配资源.
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