评估组合次流行病框架和其他时间序列模型对2022-2023年mopox流行病的预测性能
Amanda Bleichrodt1, Ruiyan Luo1, Alexander Kirpich1
1Department of Population Health Sciences, School of Public Health, Georgia State University, Atlanta, GA, USA.
Royal Society open science
|July 30, 2024
概括
与其他模型相比,n-亚流行病建模框架在预测mopox爆发方面表现优越. 这一发现支持使用亚流行病框架来预测传染病轨迹.
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:
- 在2022-2023年Mopox疫情突出需要准确的短期预测,以指导公共卫生干预措施.
- 评估现有的预测模型至关重要,因为mopox病例数量正在下降,以改善未来的疫情准备.
研究的目的:
- 为了比较各种统计和机器学习模型的性能,用于短期MPOX流行病预测.
- 确定最有效的建模框架来预测新出现的传染病的发展轨迹.
主要方法:
- 使用来自巴西,加拿大,法国,德国,西班牙,英国和美国的数据生成了回顾性每周预测.
- 模型包括自回归集成移动平均 (ARIMA),通用增值模型,线性回归,Prophet,亚流行浪和n-亚流行框架.
- 性能被评估使用指标,如平均平方误差,平均绝对误差,加权间隔得分,和预测间隔覆盖范围.
主要成果:
- 在多个地点和预测时间范围内,n-亚流行病建模框架的表现始终优于其他模型.
- 一个没有加权的合奏模型经常达到最佳表现.
- 亚流行病波和n-亚流行病框架显示出与ARIMA模型相比显著的性能改善.
结论:
- 在n-亚流行病建模框架是非常有效的短期预测mopox和类似的新兴传染病.
- 副流行病框架在流行病预测方面提供了宝贵的进步,优于ARIMA等传统方法.
- 这些发现支持将先进的亚流行病建模纳入传染病监测公共卫生战略.
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