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预测COVID-19的传播趋势:基于日常,死亡和进口病例的可解释机器学习方法
1Department of Statistics, Kyungpook National University, Daegu 41566, Republic of Korea.
Mathematical biosciences and engineering : MBE
|June 14, 2024
概括
机器学习模型准确预测COVID-19传播趋势,识别潜在的疫情. 这有助于及时实施控制政策,以管理病毒动态.
科学领域:
- 流行病学 流行病学
- 传染病建模 传染病建模
- 计算生物学 计算生物学
背景情况:
- 导致COVID-19的SARS-CoV-2病毒自2019年以来导致了变种和对复苏的担忧.
- 准确预测传染病爆发对于有效的公共卫生干预至关重要.
研究的目的:
- 使用机器学习预测COVID-19传播模式.
- 将传输趋势分类为下降 (L0),维持 (L1) 和增加 (L2).
主要方法:
- 使用的机器学习算法:支持矢量机器,随机森林和XGBoost.
- 雇员确认病例,死亡病例和进口病例数据用于预测.
- 开发了一个风险指数函数来量化ML分类的传输趋势变化.
主要成果:
- 在确诊病例 (91.5-95.5%),死亡病例 (85.6-91.8%) 和进口病例 (77.7-89.4%) 中实现了高预测准确度.
- 确诊病例数据比死亡和进口病例数据准确度更高.
- 在所有数据类型中,L2 (增加) 预测比L0 (减少) 和L1 (维持) 预测表现优越.
结论:
- 机器学习模型为COVID-19传播动态提供了可靠的预测.
- 准确预测不断增加的传播趋势 (L2) 对于防止新的疫情爆发至关重要.
- 该研究强调了由准确的传输预测提供信息的及时控制政策的重要性.
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