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Scalable epidemiological workflows to support COVID-19 planning and response
35th IEEE International Parallel and Distributed Processing Symposium, IPDPS 2021 ; : 639-650, 2021.
Article in English | Scopus | ID: covidwho-1393745
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ABSTRACT
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 counterfactual 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. © 2021 IEEE.

Full text: Available Collection: Databases of international organizations Database: Scopus Type of study: Observational study Language: English Journal: 35th IEEE International Parallel and Distributed Processing Symposium, IPDPS 2021 Year: 2021 Document Type: Article

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Full text: Available Collection: Databases of international organizations Database: Scopus Type of study: Observational study Language: English Journal: 35th IEEE International Parallel and Distributed Processing Symposium, IPDPS 2021 Year: 2021 Document Type: Article