Related Work from RESUME Team

The RESUME research team has had extensive involvement with multiple aspects of COVID-19 prediction and prevention in diverse geographic locations throughout the pandemic. We highlight key projects and publications that support our RESUME thrusts here.

Related Publications

Supporting public health with epidemiological modeling, intervention development, and community engagement

Assembling surveillance data for modeling

Chen, Y.; Ren, R.; Pu, H.; Guo, X.; Chang, J.; Zhou, G.; Mao, S.; Kron, M.; Chen, J. Field-Effect Transistor Biosensor for Rapid Detection of Ebola Antigen. Sci Rep 2017, 7 (1), 10974. https://doi.org/10.1038/s41598-017-11387-7

Rimer, S. P. Building Smarter, Dynamic, and More Resilient Urban Watersheds. SUS-RURI: Proceedings of a Workshop on Developing a Convergence Sustainable Urban Systems Agenda for Redesigning the Urban-Rural Interface along the Mississippi River Watershed held in Ames, Iowa, August 12–13, 2019 2019. https://doi.org/10.31274/3d9ea6a4.3068a56d.

Tilmon, S.; Aronsohn, A.; Boodram, B.; Canary, L.; Goel, S.; Hamlish, T.; Kemble, S.; Lauderdale, D. S.; Layden, J.; Lee, K.; Millman, A. J.; Nelson, N.; Ritger, K.; Rodriguez, I.; Shurupova, N.; Wolf, J.; Johnson, D. HepCCATT: A Multilevel Intervention for Hepatitis C among Vulnerable Populations in Chicago. J Public Health (Oxf) 2021, fdab190. https://doi.org/10.1093/pubmed/fdab190.

Fadikar, A.; Higdon, D.; Chen, J.; Lewis, B.; Venkatramanan, S.; Marathe, M. Calibrating a Stochastic, Agent-Based Model Using Quantile-Based Emulation. SIAM/ASA J. Uncertainty Quantification 2018, 6 (4), 1685–1706. https://doi.org/10.1137/17M1161233.

Armstrong, E.; Runge, M.; Gerardin, J. Identifying the Measurements Required to Estimate Rates of COVID-19 Transmission, Infection, and Detection, Using Variational Data Assimilation. Infectious Disease Modelling 2021, 6, 133–147. https://doi.org/10.1016/j.idm.2020.10.010.

Morozova, O.; Cohen, T.; Crawford, F. W. Risk Ratios for Contagious Outcomes. J. R. Soc. Interface. 2018, 15 (138), 20170696. https://doi.org/10.1098/rsif.2017.0696.

Martinez-Moyano, I. J. System Dynamics in Action. In Routledge Handbook of Systems Thinking; Cabrera, D., Cabrera, L., Midgley, G., Eds.; Taylor & Francis: London, UK, forthcoming.

Ramanathan, A.; Pullum, L. L.; Hobson, T. C.; Steed, C. A.; Quinn, S. P.; Chennubhotla, C. S.; Valkova, S. ORBiT: Oak Ridge Biosurveillance Toolkit for Public Health Dynamics. BMC Bioinformatics 2015, 16 (S17), S4. https://doi.org/10.1186/1471-2105-16-S17-S4.

Foster, I.; Ainsworth, M.; Bessac, J.; Cappello, F.; Choi, J.; Di, S.; Di, Z.; Gok, A. M.; Guo, H.; Huck, K. A.; Kelly, C.; Klasky, S.; Kleese van Dam, K.; Liang, X.; Mehta, K.; Parashar, M.; Peterka, T.; Pouchard, L.; Shu, T.; Tugluk, O.; van Dam, H.; Wan, L.; Wolf, M.; Wozniak, J. M.; Xu, W.; Yakushin, I.; Yoo, S.; Munson, T. Online Data Analysis and Reduction: An Important Co-Design Motif for Extreme-Scale Computers. The International Journal of High Performance Computing Applications 2021, 109434202110235. https://doi.org/10.1177/10943420211023549.

Predicting future pathogens

Additional Related Work

In addition to pandemic prevention work, members of the RESUME team have extensive experience developing the tools and methodologies that enable computational epidemiology at scale. Here, we describe the key research areas and capabilities the team has developed that support PIPP efforts.

Agent-Based Modeling Frameworks

Agent-based modeling (ABM) is a method of computing the potential system-level consequences of the behaviors of sets of individuals. ABMs allow modelers to specify each agent’s individual behavioral rules; to describe the circumstances in which the individuals reside; and then to execute the rules to determine possible system-level results. Members of the RESUME team have worked with ABMs in a variety of application domains for many years and have developed a suite of open-source frameworks that enable rapid deployment of these models.

ChiSIM, the Chicago Social Interaction Model, is an agent-based model framework for implementing agent-based models that simulate the mixing of a synthetic population at city or larger scales. In a ChiSIM based model, each agent, that is, each person in the simulated population, resides in a place (a household, dormitory or retirement home/long term care facility, for example), and moves among other places such as workplaces, homes, clinics, and community resources. Agents typically move between places according to their domain specific activity profile, such that each agent has a profile that determines at what times throughout the day they occupy a particular location. Once in a place, an agent mixes with other agents in some model or domain-specific way. For example, an agent may expose other agents to a disease in an epidemiological model.

ABM Related Publications

North, Michael J, Nicholson T Collier, Jonathan Ozik, Eric R Tatara, Charles M Macal, Mark Bragen, and Pam Sydelko. 2013. “Complex Adaptive Systems Modeling with Repast Simphony.” Complex Adaptive Systems Modeling 1 (1): 3. https://doi.org/10.1186/2194-3206-1-3.

Collier, Nicholson, and Michael North. 2013. “Parallel Agent-Based Simulation with Repast for High Performance Computing.” SIMULATION 89 (10): 1215–35. https://doi.org/10.1177/0037549712462620.

Collier, Nicholson, and Jonathan Ozik. 2022. “Distributed Agent-Based Simulation with Repast4Py.” In 2022 Winter Simulation Conference (WSC), 192–206. Singapore: IEEE. https://doi.org/10.1109/WSC57314.2022.10015389. 

Macal, C. M., N. T. Collier, J. Ozik, E. R. Tatara, and J. T. Murphy. 2018. “ChiSIM: An Agent-Based Simulation Model of Social Interactions in a Large Urban Area.” In 2018 Winter Simulation Conference (WSC), 810–20. https://doi.org/10.1109/WSC.2018.8632409.

Large-Scale Model Exploration

Modern computational studies, involving simulation, AI/ML, or other black-box models, are campaigns consisting of large numbers of these models with many possible variations. The models may be run with different parameters, possibly as part of an automated model parameter optimization, classification, or, more generally, model exploration (ME). At scale, ME can be prohibitively computationally demanding, as it requires repeated runs of expensive models over large parameter spaces. Our team has developed both algorithms and HPC workflows capabilities to facilitate large-scale model exploration across many scientific domains, including computational epidemiology. 

Model Exploration Algorithms

Stochastic simulators, like ABMs, present unique analytical challenges due to the randomness in their outputs. Gaussian Process-based surrogate models are often used to emulate stochastic simulators for statistical analysis, and our team has developed a variety of algorithms and methods in this space.

ME Algorithm Related Publications

Fadikar, Arindam, Dave Higdon, Jiangzhuo Chen, Bryan Lewis, Srinivasan Venkatramanan, and Madhav Marathe. 2018. “Calibrating a Stochastic, Agent-Based Model Using Quantile-Based Emulation.” SIAM/ASA Journal on Uncertainty Quantification 6 (4): 1685–1706. https://doi.org/10.1137/17M1161233. 

Binois, Mickaël, Jiangeng Huang, Robert B. Gramacy, and Mike Ludkovski. 2019. “Replication or Exploration? Sequential Design for Stochastic Simulation Experiments.” Technometrics 61 (1): 7–23. https://doi.org/10.1080/00401706.2018.1469433.

Binois, Mickaël, and Robert B. Gramacy. 2021. “HetGP: Heteroskedastic Gaussian Process Modeling and Sequential Design in R”. Journal of Statistical Software 98 (13):1-44. https://doi.org/10.18637/jss.v098.i13.

Baker, Evan, Pierre Barbillon, Arindam Fadikar, Robert B. Gramacy, Radu Herbei, David Higdon, Jiangeng Huang, Leah R. Johnson, Pulong Ma, Anirban Mondal, Bianica Pires, Jerome Sacks, and Vadim Sokolov. 2022. “Analyzing Stochastic Computer Models: A Review with Opportunities.” Statistical Science 37 (1). https://doi.org/10.1214/21-STS822.

HPC Workflows for Model Exploration

Constructing the software to run such studies at the requisite computational scales is often unnecessarily time-consuming and the resulting software artifacts are typically difficult to generalize and package for other users. Supported by the Swift/T parallel scripting language, developed by members of our team (and winner of a 2018 R&D 100 award), we developed the Extreme-scale Model Exploration with Swift/T (EMEWS). EMEWS enables the integration of cutting edge ME algorithms to coordinate highly concurrent ME experiments, automating the running and evaluation of large numbers of models, such as epidemiological simulations. The team has also extended EMEWS to facilitate cross framework integration with other parallel scripting languages, such as Parsl and Globus Compute (formerly funcX).

HPC Workflows for Model Exploration Related Publications

Ozik, Jonathan, Nicholson T. Collier, Justin M. Wozniak, and Carmine Spagnuolo. 2016. “From Desktop to Large-Scale Model Exploration with Swift/T.” In 2016 Winter Simulation Conference (WSC), 206–20. https://doi.org/10.1109/WSC.2016.7822090. 

Wozniak, J. M., T. G. Armstrong, M. Wilde, D. S. Katz, E. Lusk, and I. T. Foster. 2013. “Swift/T: Large-Scale Application Composition via Distributed-Memory Dataflow Processing.” In 2013 13th IEEE/ACM International Symposium on Cluster, Cloud, and Grid Computing, 95–102. Delft: IEEE. https://doi.org/10.1109/CCGrid.2013.99.

Babuji, Yadu, Anna Woodard, Zhuozhao Li, Daniel S. Katz, Ben Clifford, Rohan Kumar, Lukasz Lacinski, Ryan Chard, Justin M. Wozniak, Ian Foster, Michael Wilde, and Kyle Chard. 2019. “Parsl: Pervasive Parallel Programming in Python.” In Proceedings of the 28th International Symposium on High-Performance Parallel and Distributed Computing, 25–36. HPDC ’19. Phoenix, AZ, USA: Association for Computing Machinery. https://doi.org/10.1145/3307681.3325400.

Chard, Ryan, Yadu Babuji, Zhuozhao Li, Tyler Skluzacek, Anna Woodard, Ben Blaiszik, Ian Foster, and Kyle Chard. 2020. “funcX: A Federated Function Serving Fabric for Science.” In Proceedings of the 29th International Symposium on High-Performance Parallel and Distributed Computing, 65–76. Stockholm Sweden: ACM. https://doi.org/10.1145/3369583.3392683.