FluMod - Center for the Multiscale Modeling of Pandemic and seasonal Flu Prevention and Control

项目摘要 在本提案中，我们计划为解决上述基础和操作问题做出贡献。 通过推进流感建模科学和贡献新方法， 将提高季节性流感和大流行性流感的准确性和可用性的数据来源 模型为了应对这些挑战，我们计划建立在独特的机械空间 结构化建模方法，由我们的财团，其中包括随机 集合种群模型和完全开发的基于代理的模型嵌套在一起，在我们的全球 流行病和流动性模型（GLEAM）。 该项目的目标是从以下方面产生新颖且可操作的科学见解 流感传播的动态传播模型，有效地整合了关键 重点人口的社会人口指标，以及 药物和非药物干预。我们在具体目标1中的拟议工作 (A1)我将利用我们的全球建模（从全球到局部规模）框架， 探讨流感疫苗接种、抗病毒预防/治疗的多年影响，以及 在流感季节和大流行期间采取社区缓解措施。我们的具体目标2（A2）将侧重于 使用高质量的数据来模拟异构传输驱动器和新型接触模式 这将使我们能够指导缓解战略和干预措施的优先次序。在 我们的目标3（A3），我们将使用人工智能方法来确定干预措施， 特别是协同作用，非常适合特定的流行病情况， 大流行性流感我们的首要目标是提供一个建模组合， 创新的数学和计算方法。我们旨在解决几个问题 常见的季节性流感和大流行性流感问题，并将这些问题与分析 方法和疫情预测。在这个项目中开发的建模和数据可以帮助 促进和证明透明的公共卫生决策，同时有助于定义 模型选择和验证的标准方法。最后，我们的流感建模平台可以 也有利于更广泛的建模团队网络，并可用于改善结果共享 统一建模方法。

PROJECT SUMMARY In this proposal we plan to contribute addressing the above foundational and operational challenges by advancing the science of influenza modeling and contributing novel methods and data sources that will increase the accuracy and availability of seasonal and pandemic influenza models. To address these challenges, we plan to build on the unique mechanistic spatially structured modeling approaches developed by our consortium, that includes stochastic metapopulation models and fully developed agent-based models nested together in our global epidemic and mobility modeling (GLEAM) approach. The objective of this project is to generate novel and actionable scientific insights from dynamic transmission models of influenza transmission that effectively integrate key socio-demographic indicators of the focus population, as well as a wide spectrum of pharmaceutical and non-pharmaceutical interventions. Our proposed work in specific aim 1 (A1) will leverage our global modeling (from the global to local scale) framework that can be used to explore the multi-year impact of influenza vaccination, antiviral prophylaxis/treatment, and community mitigation during influenza seasons and pandemics. Our specific aim 2 (A2) will focus on using high quality data to model heterogeneous transmission drivers and novel contact pattern stratifications that will allow us to guide mitigation strategies and prioritization for interventions. In our Aim 3 (A3) we will use artificial intelligence approaches to identify interventions that are particularly synergistic and well-suited to particular epidemic scenarios, for seasonal and pandemic influenza. Our overarching goal is to provide a modeling portfolio with flexible and innovative mathematical and computational approaches. We aim to address several questions commonly asked about seasonal and pandemic influenza and match these with analytical methods and outbreak projections. The modeling and data developed in this project can help facilitate and justify transparent public health decisions, while contributing to the definition of standard methods for model selection and validation. Finally, our influenza modeling platform can also benefit the broader network of modeling teams and can be used to improve result sharing and harmonization of modeling approaches.