通过利用后勤增长模型和模糊时间序列技术预测冠状病毒活跃病例
Chandrakanta Mahanty1, S Gopal Krishna Patro2, Sandeep Rathor3
1Department of Computer Science & Engineering, GITAM School of Technology, GITAM Deemed to Be University, Visakhapatnam, 530045, India.
Scientific reports
|August 4, 2024
概括
预测冠状病毒 (COVID-19) 活跃病例对于流行病管理至关重要. 混合模糊时间序列和非线性增长模型准确预测未来的感染,帮助公共卫生干预.
科学领域:
- 流行病学 流行病学
- 计算生物学 计算生物学
- 公共卫生 公共卫生
背景情况:
- 冠状病毒 (COVID-19) 在全球范围内造成了广泛的死亡率和经济破坏.
- 越来越多的确诊病例和无症状传播需要准确的预测,以有效应对流行病.
- 时间序列预测是分析流行病感染率和告知决策支持系统的标准方法.
研究的目的:
- 开发和评估一种混合模型,用于预测多个国家的活跃COVID-19病例.
- 通过结合无症状传播因素来提高流行病趋势预测的准确性.
- 为了解流行病模式和支持政府干预提供一个工具.
主要方法:
- 提出了一个混合模型,将模糊时间序列 (FTS) 与非线性增长模型相结合.
- 应用该模型来预测意大利,巴西,印度,德国,巴基斯坦和缅甸的活跃COVID-19病例.
- 在两个阶段评估模型性能:第一阶段 (至2020年6月5日) 和第二阶段 (至2022年1月15日).
主要成果:
- 与个人物流增长和FTS技术相比,混合模型表现出优越的合适效果.
- 在第一阶段达到0.9992和第二阶段达到0.9784,这表明预测准确度很强.
- 成功预测了26天 (第一阶段) 和14天 (第二阶段) 的活跃病例.
结论:
- 拟议的混合模型为预测流行病趋势提供了一个强大的框架.
- 准确预测活跃的COVID-19病例可以显著帮助政府制定有效的公共卫生干预措施.
- 该模型捕捉流行病模式的能力支持主动的流行病管理策略.
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