We will use innovative computing to advance epidemiological science.
Key objectives:
- Develop exa-scale enabled analytic methods and simulations of spreading process on multi-scale multi-layer (MSML) networks with a billion or more nodes.
- Use simulation-based methods for analyzing complex interventions to contain real-time epidemics.
- Combine simulations with HPC-enabled machine learning methods to support modeling and decision support tasks.
Expected outcomes and computational advances:
- Scalable computational techniques for a new class of simulation problems for exa-scale systems and parallel/distributed algorithms for machine learning and large-scale data analytics:
- HPC simulations on emerging exa-scale architectures for global-scale epidemics (approximately 10^10 nodes and 10^12 edges) that run in less than 5 seconds per replicate.
- Scalable data analytic techniques that will facilitate pervasive apps to work in near-real-time.
- An integrated self-sustainable global cyber-environment (CERTES) for infectious disease epidemiology with a focus on robustness, scalability, and pervasive access. CERTES will support real-time epidemic science.
