门动态和流行病信息的机器学习来预测mpox:美国的案例研究
S Nivetha1, Parthasakha Das2, Mini Ghosh1
1Department of Mathematics, School of Advanced Sciences, Vellore Institute of Technology, Chennai Campus, Chennai 600127, Tamil Nadu, India.
Chaos (Woodbury, N.Y.)
|November 13, 2025
概括
这项研究模拟了mopox (麻疹) 传播动态,发现及时治疗,卫生和接种疫苗对于缓解传播至关重要. 我们的综合框架有助于预测疫情爆发,并为公共卫生战略提供信息.
科学领域:
- 流行病学 流行病学
- 数学建模的数学建模
- 公共卫生 公共卫生
背景情况:
- 麻疹 (Mombox) 是一种重新出现的病毒性疾病,在全球范围内持续传播给人类.
- 了解mopox传播动态对于有效的公共卫生干预至关重要.
研究的目的:
- 开发和验证mopox传播的区间流行病模型.
- 评估医疗干预对MPOX疾病进展和恢复的影响.
- 通过机器学习,提高mopox疫情的实时预测.
主要方法:
- 开发了一个包含治疗功能的分区流行病模型.
- 使用美国mopox案例数据和信任区域反射优化对模型进行校准.
- 进行了全球灵敏度分析,并使用基本复制号 (RM0) 检查了分叉结构.
- 整合了对自回归神经网络 (ARNN),ARIMA和LSTM预测模型的机制性洞察.
主要成果:
- 该模型准确地捕获mopox传输趋势,并确定影响的关键参数.
- 分叉分析揭示了复杂的特有动态和值行为.
- 根据疫情信息的机器学习模型显示,对实时预测的预测准确度有所提高.
- 敏感性分析强调了与治疗和传播相关的参数的重要性.
结论:
- 及时的医疗治疗,加强卫生实践和疫苗接种对于控制mpox.是必不可少的.
- 开发的综合建模和预测框架为公共卫生决策提供了有价值的工具.
- 这项研究为预测MPOX疫情和指导战略应对提供了强有力的方法.
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