Model outputs are archived to Petrel, which is a storage and data service run by Argonne and accessible via Globus, and then transferred to the Argonne LCRC cluster for post processing. The group employs machine learning methods to guide large numbers of Cit圜OVID runs, transforming Theta into an in silico laboratory, where each experiment produces 500GB to 1TB of simulation outputs. In order to perform this sort of granular analysis the researchers are using Argonne’s supercomputer, Theta. Then, questions arose around re-opening, and now policy makers are shifting their attention to questions around vaccines and their strategic, efficient, and equitable distribution.
Initially the questions the researchers were asked centered around the impact of shutting down areas to slow the spread. However, the approach allows for the modeling of specific and realistic non-pharmaceutical interventions-such as imposing restrictions on specific types of businesses, or detailed school attendance models-closely resembling those being considered by public health officials. The detailed modeling approach is more computationally demanding than other types of modeling methods being applied to COVID-19, such as statistical models, and compartmental models. Rather, they are representations of individuals based on census data, and represent Chicago’s socio-economic makeup (age, home/school/work location, etc.). The group at Argonne has developed Cit圜OVID, a detailed agent-based model that represents the 2.7 million residents of Chicago in terms of people (behaviors and social interactions), places (including 1.2 million unique geo-locations such as households, schools, workplaces, and hospitals), and hourly activity schedules. Scientists at Argonne National Lab have deployed some of the Nation’s most powerful compute resources, and developed large-scale epidemiological models to provide policy makers with model forecasts and scenario analyses around the impact of various mitigation measures in the effort to battle and contain COVID-19. Image: Courtesy of Argonne National Laboratory
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