在延迟和不完整的病例报告的情况下估计疫情结束的概率
M J Plank1, W S Hart2, J Polonsky3
1School of Mathematics and Statistics, University of Canterbury, Christchurch, New Zealand.
Proceedings. Biological sciences
|January 29, 2025
概括
确定传染病爆发的结束涉及到平衡利益与复发风险. 这项研究引入了一种新的数学模型,用于对疫情结束概率进行可靠的估计,以帮助公共卫生政策决策.
科学领域:
- 流行病学 流行病学
- 数学建模的数学建模
- 公共卫生政策 公共卫生政策
背景情况:
- 宣布传染病爆发的结束对于政策决策至关重要,涉及缓解控制措施和复发风险之间的权衡.
- 量化疫情结束概率的现有数学方法需要改进,以纳入现实世界的复杂性.
研究的目的:
- 开发和介绍一种新的数学方法来估计传染病爆发结束的概率.
- 考虑到现实世界爆发的关键特征,包括不完整的病例确诊,报告延迟,传染异质性和进口与本地病例.
主要方法:
- 开发一种新的数学框架,以在疫情爆发结束时建模传染病动态.
- 纳入不完全确诊病例的参数,报告延迟,个体传染性变化和病例来源 (进口/本地).
- 模型的应用和验证使用新西兰COVID-19 (2020) 和刚果民主共和国 (2018) 埃博拉病毒病的案例研究.
主要成果:
- 拟议的建模框架为疫情结束概率提供了定量估计.
- 案例研究表明,达到95%的未来无感染概率的估计日期在各种建模假设中是一致的.
- 该模型的稳定性表明它有助于为公共卫生政策提供有关疫情声明的信息.
结论:
- 开发的数学建模框架提供了一种可靠的方法来估计传染病爆发的概率.
- 结果在不同假设的一致性突出了政策顾问的方法的可靠性.
- 这种定量工具可以支持基于证据的决策,以宣布疫情结束,改善公共卫生应对策略.
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