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 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.

AI-Driven Agent-Based Models to Study the Role of Vaccine Acceptance in Controlling COVID-19 Spread in the US

“…Additionally, the pipeline presented in this work also saves significant resources by bringing up/down database servers (serving the population data) dynamically. In contrast, in our earlier work [27] all the database servers were started in the beginning. Finally, in a very recent study [2] we improve on the present pipeline further.…”

“…In an previous study [27], we had presented a different pipeline from what is described in this study. Compared to [27], which scheduled tasks statically (apriori), a major innovation of this work is the use of dynamic scheduling of tasks/jobs which significantly improves task throughput.…”

“…In an previous study [27], we had presented a different pipeline from what is described in this study. Compared to [27], which scheduled tasks statically (apriori), a major innovation of this work is the use of dynamic scheduling of tasks/jobs which significantly improves task throughput. Additionally, the pipeline presented in this work also saves significant resources by bringing up/down database servers (serving the population data) dynamically.…”

“…The spatially explicit, time-varying social contact network captures the spatial and temporal heterogeneities. Our basic approach builds on our early work in [19], [15], [25], [3], [33], [27], [12]; this Fig. 1: Fig.…”

Deploying Vaccine Distribution Sites for Improved Accessibility and Equity to Support Pandemic Response

“…Data: We run our experiments on the mobility data from Charlottesville City and Albemarle County in Virginia. For these counties, we use synthetic data constructed from the 2019 U.S. population pipeline (see ; Machi et al [2021] for details). This dataset was constructed by tracking the week-long activity of county residents.…”