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

项目总结 在这份提案中，我们计划为解决上述基础性和操作性问题做出贡献 推动流感模型科学的发展和贡献新方法和 将提高季节性和大流行性流感的准确性和可用性的数据来源 模特们。为了应对这些挑战，我们计划在空间上建立独特的机械 我们联盟开发的结构化建模方法，其中包括随机 元种群模型和完全开发的基于代理的模型嵌套在我们的全球 流行病和流动性模型(GLEAM)方法。 该项目的目标是从以下方面产生新的和可操作的科学见解 有效整合密钥的流感传播动态传播模型 重点人口的社会人口指标，以及广泛的 药物干预和非药物干预。我们在具体目标1中提出的工作 (A1)将利用我们可以使用的全球建模(从全球到本地)框架 探讨流感疫苗接种、抗病毒预防/治疗以及 流感季节和大流行期间的社区缓解。我们的具体目标2(A2)将专注于 基于高质量数据的异质变速器建模及新型接触模式研究 分层，这将使我们能够指导缓解战略和干预优先事项。在……里面 我们的目标3(A3)我们将使用人工智能方法来识别 特别是协同作用，非常适合于特定的流行病情景，对于季节性和 大流行性流感。我们的首要目标是提供灵活和 创新的数学和计算方法。我们打算解决几个问题 经常被问及季节性和大流行性流感，并将这些与分析相匹配 方法和暴发预测。该项目中开发的模型和数据可以帮助 促进并证明透明的公共卫生决定的合理性，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 还有利于更广泛的建模团队网络，并可用于改善结果共享 以及统一建模方法。

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.