Data-driven scalable pipeline using national agent-based models for real-time pandemic response and decision support

Abstract

This paper describes an integrated, data-driven operational pipeline based on national agent-based models to support federal and state-level pandemic planning and response. The pipeline consists of ( i) an automatic semantic-aware scheduling method that coordinates jobs across two separate high performance computing systems; ( ii) a data pipeline to collect, integrate and organize national and county-level disaggregated data for initialization and post-simulation analysis; ( iii) a digital twin of national social contact networks made up of 288 Million individuals and 12.6 Billion time-varying interactions covering the US states and DC; ( iv) an extension of a parallel agent-based simulation model to study epidemic dynamics and associated interventions. This pipeline can run 400 replicates of national runs in less than 33 h, and reduces the need for human intervention, resulting in faster turnaround times and higher reliability and accuracy of the results. Scientifically, the work has led to significant advances in real-time epidemic sciences.

An agent-based framework to study forced migration: A case study of Ukraine

“…Spatially, the ABM can disaggregate conflict-induced displacement at any spatial scale such as Admin-2 level Raions within Ukraine. Finally, since we model individual agents by using a synthetic population developed from publicly available data ( 19 , 20 ), migration estimates can be aggregated based on demographic characteristics including age and gender. We demonstrate policy relevance of the model in two ways.…”

BharatSim: An agent-based modelling framework for India

“…On the positive side, however, more realistic investigations of disease dynamics in specific populations become possible [21]. The impacts of targeted interventions, such as lockdowns, school closures, vaccination drives, and restrictions on public transport, can be assessed more realistically than is possible with ODE-based approaches [22, 23]. An exponential expansion in computational power over the past several decades has also made simulating large-scale individual-level models progressively easier.…”

“…approaches [22,23]. An exponential expansion in computational power over the past several decades has also made simulating large-scale individual-level models progressively easier.…”