为加拿大大流行病准备的数学建模:从COVID-19中学习
Nicholas H Ogden1, Emily S Acheson1, Kevin Brown2,3
1Public Health Risk Sciences Division, National Microbiology Laboratory, Public Health Agency of Canada.
概括
数学建模对于疫情准备至关重要,有助于资源分配和预测. 建立强大的网络和应对数据挑战是有效公共卫生应对的关键.
科学领域:
- 公共卫生 公共卫生
- 流行病学 流行病学
- 数学建模的数学建模
背景情况:
- COVID-19 疫情凸显了疫情规划的重要性以及数学建模在告知公共卫生决策中的作用.
- 不同的建模方法,包括分区,基于代理的和进口模型,对于疫情准备是必不可少的.
- 最佳实践强调生物现实主义,多学科合作,适当的模型复杂性,不确定性评估和清晰的沟通.
研究的目的:
- 根据COVID-19的经验,审查数学建模在传染病控制中的应用.
- 概述数学建模如何最好地支持加拿大的流行病准备.
- 确定在公共卫生领域加强建模能力的关键挑战和建议.
主要方法:
- 根据加拿大公共卫生局传染病外部建模网络 (PHAC EMN-ID) 的集体经验,进行了叙述性审查.
- 该审查综合了来自国家实践社区的见解,重点是公共卫生的数学建模.
主要成果:
- 数学建模通过估计假设的流行病的资源需求,支持疫情准备,并允许快速适应预测新出现的威胁.
- 建模可以为干预和长期流行病预测的决策提供信息.
- 确定了两个主要应用:资源需求估计和可适应的预测系统.
结论:
- 加拿大公共卫生组织内加强建模专业知识至关重要.
- 为了有效应对紧急情况,促进将公共卫生和学术建模者联系起来的实践社区至关重要.
- 关键的挑战包括确保获得相关的公共卫生,医院和基因组数据.
相关概念视频
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