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.
The objective of this proposal is to advance the science of modeling and contribute novel methods and data analytics tools that will increase the understanding of seasonal and pandemic influenza in the context of the network of modeling teams coordinated by the CDC. To address these challenges, we plan to develop a novel global modeling framework, contribute new data and methods for improve the accuracy and validation of flu modeling approaches, and evolve successful methodologies to advance the analysis of layered intervention with artificial Intelligence.
