美国COVID-19病例和死亡的两部分预测建模
Teresa-Thuong Le1, Xiyue Liao2
1California State University, Long Beach, CA, United States of America.
PloS one
|June 6, 2024
概括
这项研究开发了COVID-19病例和死亡的预测模型. 最好的模型,一个两部分的XGBoost,确定了人口和初级保健医生率作为影响COVID-19发生和死亡率的关键因素.
科学领域:
- 流行病学 流行病学
- 生物统计学 生物统计学
- 数据科学数据科学数据科学
背景情况:
- 准确的COVID-19预测对于疾病控制和预防至关重要.
- 现有的模型可能会因为COVID-19病例和死亡数据的偏差分布而扎.
研究的目的:
- 开发和评估COVID-19病例和死亡的预测模型.
- 确定与COVID-19发生和死亡相关的关键因素.
- 为了应对COVID-19数据分布高度偏差所带来的挑战.
主要方法:
- 一个由两个部分组成的建模框架,将后勤分类与机器学习或统计平滑相结合.
- 使用了来自5个Kaggle来源的28,955个观测和18个变量的横截面数据集.
- 在模型开发和评估中使用XGBoost,根平均平方误差 (RMSE) 和平均绝对误差 (MAE).
主要成果:
- 两部分的XGBoost模型在预测COVID-19病例和死亡的整个分布方面表现出卓越的表现.
- 人口密度和初级保健医生的比例被确定为影响COVID-19病例和死亡的最重要因素.
- 拟议的两部分框架有效地处理了数据分布的偏差.
结论:
- 两部分的XGBoost模型为COVID-19预测提供了一个强大的方法,特别是在歪曲的数据中.
- 了解人口和医疗保健获取的影响对于COVID-19缓解策略至关重要.
- 这种预测建模框架可以帮助公共卫生倡议管理传染病.
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