在不确定性下优化疫苗接种策略,以预防流行病
Lewis Ntaimo1, Mjumo Mzyece2, David R Katerere3
1Wm Michael Barnes Department of Industrial & Systems Engineering, Texas A&M University, College Station, TX, United States.
Frontiers in public health
|March 12, 2026
概括
本研究提出了一种综合的机会约束随机编程 (ICC-SP) 模型,用于最佳的疫苗接种策略,以防止未来的流行病. 数据驱动的方法优先接种高风险群体的疫苗,并将疫苗接种与干预水平相结合,以有效控制疾病.
科学领域:
- 流行病学 流行病学
- 公共卫生 公共卫生
- 数学建模的数学建模
背景情况:
- 未来的传染病流行病带来了重大的公共卫生挑战.
- 不同种群和不确定的疾病动态使最佳的疫苗接种策略变得复杂.
- 现有的模型可能无法完全捕捉现实世界疾病传播和干预有效性的复杂性.
研究的目的:
- 在多社区环境中开发一个数据驱动的模型,以实现最佳的疫苗接种策略.
- 将不确定性纳入社会混合,疾病传播和疫苗有效性的不确定性.
- 为了使公共卫生政策分析能够为流行病做好准备.
主要方法:
- 综合机会约束随机编程 (ICC-SP) 疾病传播模型的导出.
- 使用易于获得的数据:人口普查人口统计数据,与年龄相关的易感性/传染性,病毒变体和疫苗疗效.
- 根据比例,家庭类型,年龄组和干预水平来定义疫苗接种策略.
主要成果:
- 在不确定的情况下,ICC-SP模型有效地确定了最佳的疫苗接种策略.
- 疫苗接种策略必须与流行病控制的具体干预水平相结合.
- 疫苗有效性标准和干预水平影响需要接种疫苗的人口比例.
- 最佳策略优先打疫苗给高易感性和高传染性家庭和年龄组.
结论:
- 数据驱动的ICC-SP模型为疫情准备提供了一个强大的框架.
- 有效的疫情预防需要有针对性的疫苗接种和公共卫生干预措施的结合.
- 该模型的灵活性允许基于风险的政策分析和适应不同流行病情景.
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