基于时间序列的机器学习分析的COVID-19疫苗的影响和有效性:基于人口的研究
Rafael Garcia-Carretero1, Maria Ordoñez-Garcia2, Oscar Vazquez-Gomez1
1Internal Medicine Department, Mostoles University Hospital, Rey Juan Carlos University, 29835 Mostoles, Spain.
Journal of clinical medicine
|October 16, 2024
概括
在西班牙,COVID-19疫苗接种显著减少了住院和死亡. 数学模型估计,疫苗避免了超过17万例住院治疗和24000例死亡,在各年龄组中显示出广泛的临床益处.
科学领域:
- 流行病学 流行病学
- 公共卫生 公共卫生
- 传染病建模 传染病建模
背景情况:
- 尽管SARS-CoV-2病例下降,但西班牙对疫苗对COVID-19住院和死亡的影响的研究是有限的.
- 了解疫苗接种对疾病严重程度和死亡率的全国影响对于公共卫生战略至关重要.
研究的目的:
- 量化西班牙国家一级疫苗接种导致的COVID-19严重程度和死亡率的减少.
- 为了估计COVID-19疫苗推出所避免的住院和死亡人数.
主要方法:
- 这是一项基于人口的回顾性研究,分析了感染波,住院特征和疫苗接种.
- 开发两种机器学习模型 (ElasticNet,RandomForest) 来模拟非疫苗接种场景.
- 疫苗接种和不接种方案的比较,以估计避免住院和死亡.
主要成果:
- 总共有498,789名患者被纳入,全球死亡率为14.3%.
- 在所有年龄组中,疫苗接种开始与住院和死亡人数减少之间观察到强烈的相关性.
- 据估计,疫苗接种在2021年3月至12月期间在西班牙预防了170,959例住院治疗和24,546例死亡,总死亡人数减少了9.19%.
结论:
- COVID-19患者的人口和临床概况在大流行期间发生了变化.
- 疫苗接种带来了显著的临床益处,特别是在80岁以上的人群中.
- 机器学习模型有效估计了避免的疾病负担,证实了COVID-19疫苗接种在减少严重程度和死亡率方面的全人口有效性.
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