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Global Pervasive Computational Epidemiology (GPCE) is a multi-institution partnership aimed at advancing the computational foundations, engineering principles, and technologies of computational epidemiology at an unprecedented scale. This Expeditions in Computing program was funded by the NSF.

By fostering collaborations between researchers of complementary expertise among our member institutions and beyond, we have taken on challenges we couldn't have done alone and made significant scientific and computational advances. Our members have submitted over 150 papers for publication and preprint, including in Science, PNAS, and a number of top-tier conferences and journals in computer science, mathematics, and other areas.

Having started this NSF Expeditions program in April 2020, the coincidental occurrence of the pandemic led us to focus our initial efforts on the most vexing questions raised by the evolving situation. We highlight some of the resulting scientific advances and the social and policy impact of our work below:

Breakthrough 1: Models to understand the effects of lockdown strategies on the spread of SARS-CoV-2

Solution: For every hour, our mobility-based epidemiological model predicts who got infected where. We fit the model to past reported COVID-19 cases and deaths to get accurate predictions, and then applied our model to analyze reopening, disparities, superspreaders, and more. We show higher infection risks to lower-income and minority census block groups.

Computational advances: Real-time data-driven epidemic modeling

➢ Mobility network models of COVID-19 explain inequities and inform reopening (Chang 2020)

Social and Policy impacts: This work was referenced in two Supreme Court decisions and multiple national-level public health policies.

Breakthrough 2: Models to optimize resource allocation and distribution systems that promote accessibility and equity

Solution: We capture accessibility and equity in a combinatorial problem, allowing us to develop efficient algorithms with theoretical guarantees on both of these aspects. This work has led to the optimization of public health resource utilization, including emergency field hospitals; vaccine distribution and allocation; contact tracing; and quarantine and testing.

Computational advances: Real-time stochastic optimization in an uncertain environment

➢ Data-driven Real-time Strategic Placement of Mobile Distribution Sites (Mehrab 2022)

Social and Policy impacts: The state of Virginia has relied on our models to inform public health policy decisions, from March 2020 to present. A Spring 2022 report from the VDH Central Office to the VA General Assembly articulated the impact of our work.

➢ Medical Resource Demand Dashboard

Breakthrough 3: Tools to support long-term state-level US COVID-19 forecasts to public health officials, decision makers, and the general public

Solution: Provided forecasts of COVID-19 trajectories for various scenarios under different assumptions, such as: scale of wave, impact of variants with up-to-date data and epidemiological understanding, vaccination of children, and others.

Computational advances: Data-driven, national-scale, high-performance computing agent models

➢ ACM Gorden Bell Finalist - Specialist Prize for HPC-based COVID-19 Research: Scalable Epidemiological Workflows to Support COVID-19 Planning and Response (Machi 2021)

➢ DeepCOVID: Deep Learning-driven Framework for Explainable Real-time COVID-19 Forecasting (Rodriguez 2021)

Social and Policy impacts: This work was referenced in two Supreme Court decisions and multiple national-level public health policies, and the results were presented to the COVID-19 task force, the head of the CDC, and the White House coronavirus response coordinator, Dr. Jha, and his team.

➢ CDC Morbidity and Mortality Weekly Report May 14, 2021

➢ UVA Provost’s Office Award for Collaborative Excellence in Public Service

Research

View Research ➢

Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States

➢ PRAKASH LAB PAGE

Mobility network models of COVID-19 explain inequities and inform reopening

➢ LESKOVEC FACULTY PAGE

➢ PROJECT INFO PAGE

Immune life history, vaccination, and the dynamics of SARS-CoV-2 over the next 5 years

➢ LEVIN LAB PAGE

Epidemiology and transmission dynamics of COVID-19 in two Indian states

➢ LAXMINARAYAN PROFILE PAGE

Partner Organizations

Outreach	View Outreach ➢
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Community	View Community ➢



Check out our YouTube Channel for our ➢ Seminar Series ➢ Researcher Spotlights ➢ Student Presentations & Spotlights		Find our two fascinating podcasts on Spotify! Science Before the Storm: When we speak of the next pandemic, it's no longer a question of "if" but "when". Join us as we talk to the scientists in the NSF research community readying us for the next pandemic. COVID Chasers: A special podcast series from researchers at the Biocomplexity Institute (NSSAC Division), University of Virginia on supporting COVID-19 response in the US over the past year.