A population data-driven workflow for COVID-19 modeling and learning

Ozik, Jonathan; Wozniak, Justin M.; Collier, Nicholson; Macal, Charles M.; Binois, Mickaël

doi:10.1177/10943420211035164

Title: A population data-driven workflow for COVID-19 modeling and learning

Journal Article · 10 September 2021 · International Journal of High Performance Computing Applications

DOI: https://doi.org/10.1177/10943420211035164 · OSTI ID:1819798

^[1]; Wozniak, Justin M. ^[1]; Collier, Nicholson ^[1]; Macal, Charles M. ^[1]; Binois, Mickaël ^[2]

Argonne National Laboratory, Lemont, IL, USA
Inria Sophia Antipolis - Méditerranée, France

CityCOVID is a detailed agent-based model that represents the behaviors and social interactions of 2.7 million residents of Chicago as they move between and colocate in 1.2 million distinct places, including households, schools, workplaces, and hospitals, as determined by individual hourly activity schedules and dynamic behaviors such as isolating because of symptom onset. Disease progression dynamics incorporated within each agent track transitions between possible COVID-19 disease states, based on heterogeneous agent attributes, exposure through colocation, and effects of protective behaviors of individuals on viral transmissibility. Throughout the COVID-19 epidemic, CityCOVID model outputs have been provided to city, county, and state stakeholders in response to evolving decision-making priorities, while incorporating emerging information on SARS-CoV-2 epidemiology. Here we demonstrate our efforts in integrating our high-performance epidemiological simulation model with large-scale machine learning to develop a generalizable, flexible, and performant analytical platform for planning and crisis response.

View Journal Article

Cite

Export

Save

Sponsoring Organization:: USDOE

OSTI ID:: 1819798

Journal Information:: International Journal of High Performance Computing Applications, Journal Name: International Journal of High Performance Computing Applications Journal Issue: 5 Vol. 35; ISSN 1094-3420

Publisher:: SAGE PublicationsCopyright Statement

Country of Publication:: United States

Language:: English

References (50)

Distributed computing in practice: the Condor experience Thain, Douglas; Tannenbaum, Todd; Livny, Miron Concurrency and Computation: Practice and Experience, Vol. 17, Issue 2-4, p. 323-356 https://doi.org/10.1002/cpe.938	journal	January 2005
Data Assimilation Evensen, Geir https://doi.org/10.1007/978-3-642-03711-5	book	January 2009
Fast Computation of the Multi-Points Expected Improvement with Applications in Batch Selection Chevalier, Clément; Ginsbourger, David Lecture Notes in Computer Science https://doi.org/10.1007/978-3-642-44973-4_7	book	January 2013
Adaptive approximate Bayesian computation for complex models Lenormand, Maxime; Jabot, Franck; Deffuant, Guillaume Computational Statistics, Vol. 28, Issue 6 https://doi.org/10.1007/s00180-013-0428-3	journal	July 2013
An adaptive sequential Monte Carlo method for approximate Bayesian computation Del Moral, Pierre; Doucet, Arnaud; Jasra, Ajay Statistics and Computing, Vol. 22, Issue 5 https://doi.org/10.1007/s11222-011-9271-y	journal	August 2011
Quantifying uncertainty on Pareto fronts with Gaussian process conditional simulations Binois, M.; Ginsbourger, D.; Roustant, O. European Journal of Operational Research, Vol. 243, Issue 2 https://doi.org/10.1016/j.ejor.2014.07.032	journal	June 2015
Care HPS: A high performance simulation tool for parallel and distributed agent-based modeling Borges, Francisco; Gutierrez-Milla, Albert; Luque, Emilio Future Generation Computer Systems, Vol. 68 https://doi.org/10.1016/j.future.2016.08.015	journal	March 2017
MPI jobs within MPI jobs: A practical way of enabling task-level fault-tolerance in HPC workflows Wozniak, Justin M.; Dorier, Matthieu; Ross, Robert Future Generation Computer Systems, Vol. 101 https://doi.org/10.1016/j.future.2019.05.020	journal	December 2019
Random Forests Breiman, Leo Machine Learning, Vol. 45, Issue 1, p. 5-32 https://doi.org/10.1023/A:1010933404324	journal	January 2001
Novel approach to nonlinear/non-Gaussian Bayesian state estimation Gordon, N. J.; Salmond, D. J.; Smith, A. F. M. IEE Proceedings F Radar and Signal Processing, Vol. 140, Issue 2 https://doi.org/10.1049/ip-f-2.1993.0015	journal	January 1993
Factorial Sampling Plans for Preliminary Computational Experiments Morris, Max D. Technometrics, Vol. 33, Issue 2 https://doi.org/10.1080/00401706.1991.10484804	journal	May 1991
Replication or Exploration? Sequential Design for Stochastic Simulation Experiments Binois, Mickaël; Huang, Jiangeng; Gramacy, Robert B. Technometrics, Vol. 61, Issue 1 https://doi.org/10.1080/00401706.2018.1469433	journal	September 2018
Practical Heteroscedastic Gaussian Process Modeling for Large Simulation Experiments Binois, Mickaël; Gramacy, Robert B.; Ludkovski, Mike Journal of Computational and Graphical Statistics, Vol. 27, Issue 4 https://doi.org/10.1080/10618600.2018.1458625	journal	July 2018
Adaptive approximate Bayesian computation Beaumont, M. A.; Cornuet, J. -M.; Marin, J. -M. Biometrika, Vol. 96, Issue 4 https://doi.org/10.1093/biomet/asp052	journal	October 2009
VECMAtk: a scalable verification, validation and uncertainty quantification toolkit for scientific simulations Groen, D.; Arabnejad, H.; Jancauskas, V. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, Vol. 379, Issue 2197 https://doi.org/10.1098/rsta.2020.0221	journal	March 2021
A fast and elitist multiobjective genetic algorithm: NSGA-II Deb, K.; Pratap, A.; Agarwal, S. IEEE Transactions on Evolutionary Computation, Vol. 6, Issue 2 https://doi.org/10.1109/4235.996017	journal	April 2002
A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking Arulampalam, M. S.; Maskell, S.; Gordon, N. IEEE Transactions on Signal Processing, Vol. 50, Issue 2 https://doi.org/10.1109/78.978374	journal	January 2002
Swift/T: Large-Scale Application Composition via Distributed-Memory Dataflow Processing Wozniak, J. M.; Armstrong, T. G.; Wilde, M. 2013 13th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid), 2013 13th IEEE/ACM International Symposium on Cluster, Cloud, and Grid Computing https://doi.org/10.1109/CCGrid.2013.99	conference	May 2013
Hypervolume-based expected improvement: Monotonicity properties and exact computation Emmerich, Michael T. M.; Deutz, Andre H.; Klinkenberg, Jan Willem 2011 IEEE Congress on Evolutionary Computation (CEC), 2011 IEEE Congress of Evolutionary Computation (CEC) https://doi.org/10.1109/CEC.2011.5949880	conference	June 2011
FLAME GPU: Complex System Simulation Framework Richmond, Paul; Chimeh, Mozhgan K. 2017 International Conference on High Performance Computing & Simulation (HPCS) https://doi.org/10.1109/HPCS.2017.12	conference	July 2017
Scioto: A Framework for Global-View Task Parallelism Dinan, James; Krishnamoorthy, Sriram; Larkins, D. Brian 2008 37th International Conference on Parallel Processing (ICPP) https://doi.org/10.1109/ICPP.2008.44	conference	September 2008
Taking the Human Out of the Loop: A Review of Bayesian Optimization Shahriari, Bobak; Swersky, Kevin; Wang, Ziyu Proceedings of the IEEE, Vol. 104, Issue 1 https://doi.org/10.1109/JPROC.2015.2494218	journal	January 2016
Experiences in Developing a Distributed Agent-based Modeling Toolkit with Python Collier, Nicholson T.; Ozik, Jonathan; Tatara, Eric R. 2020 IEEE/ACM 9th Workshop on Python for High-Performance and Scientific Computing (PyHPC) https://doi.org/10.1109/PyHPC51966.2020.00006	conference	November 2020
Swift: Fast, Reliable, Loosely Coupled Parallel Computation Zhao, Yong; Hategan, Mihael; Clifford, Ben 2007 IEEE Congress on Services (Services 2007) https://doi.org/10.1109/SERVICES.2007.63	conference	July 2007
Extreme-Scale Dynamic Exploration of a Distributed Agent-Based Model With the EMEWS Framework Ozik, Jonathan; Collier, Nicholson T.; Wozniak, Justin M. IEEE Transactions on Computational Social Systems, Vol. 5, Issue 3 https://doi.org/10.1109/TCSS.2018.2859189	journal	September 2018
From desktop to Large-Scale Model Exploration with Swift/T Ozik, Jonathan; Collier, Nicholson T.; Wozniak, Justin M. 2016 Winter Simulation Conference (WSC) https://doi.org/10.1109/WSC.2016.7822090	conference	December 2016
Chisim: an Agent-Based Simulation Model of Social Interactions in a Large Urban area Macal, Charles M.; Collier, Nicholson T.; Ozik, Jonathan 2018 Winter Simulation Conference (WSC) https://doi.org/10.1109/WSC.2018.8632409	conference	December 2018
EasyABC: performing efficient approximate Bayesian computation sampling schemes using R Jabot, Franck; Faure, Thierry; Dumoulin, Nicolas Methods in Ecology and Evolution, Vol. 4, Issue 7 https://doi.org/10.1111/2041-210X.12050	journal	April 2013
Statistical inference for stochastic simulation models - theory and application: Inference for stochastic simulation models Hartig, Florian; Calabrese, Justin M.; Reineking, Björn Ecology Letters, Vol. 14, Issue 8 https://doi.org/10.1111/j.1461-0248.2011.01640.x	journal	June 2011
A Fast and High Quality Multilevel Scheme for Partitioning Irregular Graphs Karypis, George; Kumar, Vipin SIAM Journal on Scientific Computing, Vol. 20, Issue 1 https://doi.org/10.1137/S1064827595287997	journal	January 1998
Dataflow coordination of data-parallel tasks via MPI 3.0 Wozniak, Justin M.; Peterka, Tom; Armstrong, Timothy G. Proceedings of the 20th European MPI Users' Group Meeting on - EuroMPI '13 https://doi.org/10.1145/2488551.2488561	conference	January 2013
ABCpy: A User-Friendly, Extensible, and Parallel Library for Approximate Bayesian Computation Dutta, Ritabrata; Schoengens, Marcel; Onnela, Jukka-Pekka PASC '17: Platform for Advanced Scientific Computing Conference, Proceedings of the Platform for Advanced Scientific Computing Conference https://doi.org/10.1145/3093172.3093233	conference	June 2017
A massively parallel infrastructure for adaptive multiscale simulations: modeling RAS initiation pathway for cancer Di Natale, Francesco; Bhatia, Harsh; Carpenter, Timothy S. SC '19: The International Conference for High Performance Computing, Networking, Storage, and Analysis, Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis https://doi.org/10.1145/3295500.3356197	conference	November 2019
An automatic design optimization tool and its application to computational fluid dynamics Abramson, David; Lewis, Andrew; Peachey, Tom Proceedings of the 2001 ACM/IEEE conference on Supercomputing (CDROM) - Supercomputing '01 https://doi.org/10.1145/582034.582059	conference	January 2001
Approximate Bayesian Computation Beaumont, Mark A. Annual Review of Statistics and Its Application, Vol. 6, Issue 1 https://doi.org/10.1146/annurev-statistics-030718-105212	journal	March 2019
Pegasus: A Framework for Mapping Complex Scientific Workflows onto Distributed Systems Deelman, Ewa; Singh, Gurmeet; Su, Mei-Hui Scientific Programming, Vol. 13, Issue 3 https://doi.org/10.1155/2005/128026	journal	January 2005
MASON: A Multiagent Simulation Environment Luke, Sean; Cioffi-Revilla, Claudio; Panait, Liviu SIMULATION, Vol. 81, Issue 7 https://doi.org/10.1177/0037549705058073	journal	July 2005
Parallel agent-based simulation with Repast for High Performance Computing Collier, Nicholson; North, Michael SIMULATION, Vol. 89, Issue 10 https://doi.org/10.1177/0037549712462620	journal	November 2012
Complex adaptive systems modeling with Repast Simphony North, Michael J.; Collier, Nicholson T.; Ozik, Jonathan Complex Adaptive Systems Modeling, Vol. 1, Issue 1 https://doi.org/10.1186/2194-3206-1-3	journal	March 2013
Microsimulation model calibration using incremental mixture approximate Bayesian computation Rutter, Carolyn M.; Ozik, Jonathan; DeYoreo, Maria The Annals of Applied Statistics, Vol. 13, Issue 4 https://doi.org/10.1214/19-AOAS1279	journal	December 2019
Bayesian Optimization Frazier, Peter I. Recent Advances in Optimization and Modeling of Contemporary Problems https://doi.org/10.1287/educ.2018.0188	book	October 2018
Building Predictive Models in R Using the caret Package Kuhn, Max Journal of Statistical Software, Vol. 28, Issue 5 https://doi.org/10.18637/jss.v028.i05	journal	January 2008
DiceKriging , DiceOptim : Two R Packages for the Analysis of Computer Experiments by Kriging-Based Metamodeling and Optimization Roustant, Olivier; Ginsbourger, David; Deville, Yves Journal of Statistical Software, Vol. 51, Issue 1 https://doi.org/10.18637/jss.v051.i01	journal	January 2012
laGP : Large-Scale Spatial Modeling via Local Approximate Gaussian Processes in R Gramacy, Robert B. Journal of Statistical Software, Vol. 72, Issue 1 https://doi.org/10.18637/jss.v072.i01	journal	January 2016
GPareto : An R Package for Gaussian-Process-Based Multi-Objective Optimization and Analysis Binois, Mickaël; Picheny, Victor Journal of Statistical Software, Vol. 89, Issue 8 https://doi.org/10.18637/jss.v089.i08	journal	January 2019
Dakota, a multilevel parallel object-oriented framework for design optimization, parameter estimation, uncertainty quantification, and sensitivity analysis : Adams, Brian; Ebeida, Mohamed; Eldred, Michael https://doi.org/10.2172/1177077	report	May 2014
Active Learning Settles, Burr Synthesis Lectures on Artificial Intelligence and Machine Learning, Vol. 6, Issue 1 https://doi.org/10.2200/S00429ED1V01Y201207AIM018	journal	June 2012
Introducing Parsl: A Python Parallel Scripting Library Babuji, Yadu; Brizius, Alison; Chard, Kyle Zenodo https://doi.org/10.5281/zenodo.853491	text	January 2017
Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence Holland, John H. The MIT Press https://doi.org/10.7551/mitpress/1090.001.0001	book	January 1992
Inference and control of the nosocomial transmission of methicillin-resistant Staphylococcus aureus Pei, Sen; Morone, Flaviano; Liljeros, Fredrik eLife, Vol. 7 https://doi.org/10.7554/eLife.40977	journal	December 2018

Similar Records

Computational epidemiological tools for pandemic analysis, understanding, and response

Software · 2025 · OSTI ID:code-157326

Butts, David; Germann, Timothy; Kitson, Joy; +6 more

Bayesian Calibration of Stochastic Agent Based Model via Random Forest

Journal Article · 2025 · Statistics in Medicine · OSTI ID:2540219

Robertson, Connor; Safta, Cosmin; Collier, Nicholson; +2 more

Epidemiological surveillance of common respiratory viruses in patients with suspected COVID-19 in Southwest China

Journal Article · 2020 · BMC Infectious Diseases · OSTI ID:1664501

Si, Yanjun; Zhao, Zhenzhen; Chen, Rong; +6 more

Title: A population data-driven workflow for COVID-19 modeling and learning

Citation Formats

References (50)

Similar Records

Related Subjects