Scalable Epidemiological Workflows to Support COVID-19 Planning and Response

• Published in: medRxiv : the preprint server for health sciences
• Main Authors: Machi, Dustin, Bhattacharya, Parantapa, Hoops, Stefan, Chen, Jiangzhuo, Mortveit, Henning, Venkatramanan, Srinivasan, Lewis, Bryan, Wilson, Mandy, Fadikar, Arindam, Maiden, Tom, Barrett, Christopher L, Marathe, Madhav V
• Format: Journal Article
• Language: English
• Published: United States 26.02.2021
• DOI: 10.1101/2021.02.23.21252325

The COVID-19 global outbreak represents the most significant epidemic event since the 1918 influenza pandemic. Simulations have played a crucial role in supporting COVID-19 planning and response efforts. Developing scalable workflows to provide policymakers quick responses to important questions pertaining to logistics, resource allocation, epidemic forecasts and intervention analysis remains a challenging computational problem. In this work, we present scalable high performance computing-enabled workflows for COVID-19 pandemic planning and response. The scalability of our methodology allows us to run fine-grained simulations daily, and to generate county-level forecasts and other counter-factual analysis for each of the 50 states (and DC), 3140 counties across the USA. Our workflows use a hybrid cloud/cluster system utilizing a combination of local and remote cluster computing facilities, and using over 20,000 CPU cores running for 6-9 hours every day to meet this objective. Our state (Virginia), state hospital network, our university, the DOD and the CDC use our models to guide their COVID-19 planning and response efforts. We began executing these pipelines March 25, 2020, and have delivered and briefed weekly updates to these stakeholders for over 30 weeks without interruption.