{"doi":"10.1109/ipdps49936.2021.00072","title":"Scalable Epidemiological Workflows to Support COVID-19 Planning and Response","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.","journal":null,"year":2021,"id":213594,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":18,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9469,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2021-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":556210,"name":"Parantapa Bhattacharya","orcid":"0000-0002-3626-9939","position":1,"is_corresponding":false},{"id":60846,"name":"Stefan Hoops","orcid":"0000-0001-8503-8371","position":2,"is_corresponding":false},{"id":465156,"name":"Jiangzhuo Chen","orcid":"0000-0002-2729-3881","position":3,"is_corresponding":false},{"id":465157,"name":"Henning Mortveit","orcid":"0000-0003-3363-2947","position":4,"is_corresponding":false},{"id":244680,"name":"Srinivasan Venkatramanan","orcid":"0000-0002-0874-8692","position":5,"is_corresponding":false},{"id":244678,"name":"Bryan Lewis","orcid":"0000-0003-0793-6082","position":6,"is_corresponding":false},{"id":551810,"name":"Mandy Wilson","orcid":"0000-0002-4778-5744","position":7,"is_corresponding":false},{"id":644180,"name":"Arindam Fadikar","orcid":"0000-0001-7396-0350","position":8,"is_corresponding":false},{"id":805894,"name":"Tom Maiden","orcid":null,"position":9,"is_corresponding":false},{"id":644181,"name":"Christopher L. Barrett","orcid":"0000-0003-2518-0039","position":10,"is_corresponding":false},{"id":244679,"name":"Madhav Marathe","orcid":"0000-0003-1653-0658","position":11,"is_corresponding":false},{"id":551808,"name":"Dustin Machi","orcid":"0000-0002-9081-6526","position":0,"is_corresponding":true}],"reference_count":42,"raw_metadata":null,"created_at":"2026-07-18T23:52:36.886828Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}