什么时候我们需要多种传染病模型? 在多模型设置中,预测等级和大小之间的协议
La Keisha Wade-Malone1, Emily Howerton1, William J M Probert2
1Department of Biology and Center for Infectious Disease Dynamics, The Pennsylvania State University, University Park, PA, USA.
Epidemics
|May 7, 2024
概括
多模型中心可以改善传染病的预测,但资源需求很高. 这项研究表明,对于排名流行病情景,较少的模型可能足够,优化公共卫生规划中的资源使用.
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:
- 数学模型对于公共卫生规划和应对传染病威胁至关重要.
- 不同模型之间的差异可能会阻碍有效的决策.
- 多模型枢纽生成组合以提高预测准确性,但资源密集,模型的最佳数量不清楚.
研究的目的:
- 在不同的决策环境中比较多模型预测的好处:定量结果与排名流行病情景.
- 开发一个数学框架来评估多模型协议及其影响.
- 利用现实世界COVID-19数据探索模型组合的实用性.
主要方法:
- 开发了一个数学框架来模拟多模型预测设置.
- 量化了不同模型预测之间的一致频率.
- 分析了来自美国COVID-19场景建模中心14轮预测的经验数据.
主要成果:
- 多个模型的价值因决策环境而异.
- 为了对替代流行病情景进行排名,与定量结果相比,较少的模型可能会提供可靠的预测.
- 使用COVID-19数据的模型协议分析支持多模型合集的上下文依赖的实用性.
结论:
- 枢纽中的最佳模型数量可能因决策是否依赖于定量预测或情景排名而有所不同.
- 专注于场景排名可以用更少的模型更强大,在公共卫生危机中告知有效的资源配置.
- 需要进行进一步的研究,以确定各种决策环境中足够多的模型.
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