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密集和人口密的国家的COVID-19疫情预测使用机器学习
Aman Khakharia1, Vruddhi Shah1, Sankalp Jain1
1K. J. Somaiya College of Engineering, Vidyavihar, Mumbai, 400077 India.
Annals of data science
|April 16, 2024
概括
这项研究为10个人口密集的国家开发了一种使用机器学习的COVID-19爆发预测系统. 这些模型预测了5天的新病例,有助于在疫情期间的资源管理.
科学领域:
- 流行病学 流行病学
- 公共卫生 公共卫生
- 机器学习 机器学习
背景情况:
- COVID-19 疫情继续对全球健康产生重大影响,并压迫公共卫生资源.
- 管理不断升级的COVID-19病例数量对全球的管理机构构成重大挑战.
研究的目的:
- 开发和评估用于预测COVID-19爆发的机器学习模型.
- 预测人口密集的国家在5天内新增COVID-19病例的数量.
主要方法:
- 利用9种不同的机器学习算法来构建预测模型.
- 专注于全球人口最多和人口最密集的10个国家.
- 验证了模型性能,平均准确率为87.9%±3.9%.
主要成果:
- 开发了一个COVID-19预测系统,对所选的国家平均准确率为87.9%±3.9%.
- 使用自动回归移动平均 (ARMA) 模型在5天的预测中为埃塞俄比亚实现了99.93%的峰值准确性.
- 这些模型在预测新的COVID-19病例的增加方面表现出有效性.
结论:
- 开发的预测模型可以帮助利益相关者积极为COVID-19病例的突然激增做好准备.
- 有效的资源管理可以通过这些预测工具所促进的提前准备来确保.
- 该研究强调了机器学习在管理COVID-19流行病等公共卫生危机方面的实用性.
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