CitySEIRCast: an agent-based city digital twin for pandemic analysis and simulation
Abstract The COVID-19 pandemic has dramatically highlighted the importance of developing simulation systems for quickly characterizing and providing spatio-temporal forecasts of infection spread dynamics that take specific accounts of the population and spatial heterogeneities that govern pathogen t...
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| Format: | Article |
| Language: | English |
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Springer
2024-12-01
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| Series: | Complex & Intelligent Systems |
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| Online Access: | https://doi.org/10.1007/s40747-024-01683-x |
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| _version_ | 1832571207321387008 |
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| author | Shakir Bilal Wajdi Zaatour Yilian Alonso Otano Arindam Saha Ken Newcomb Soo Kim Jun Kim Raveena Ginjala Derek Groen Edwin Michael |
| author_facet | Shakir Bilal Wajdi Zaatour Yilian Alonso Otano Arindam Saha Ken Newcomb Soo Kim Jun Kim Raveena Ginjala Derek Groen Edwin Michael |
| author_sort | Shakir Bilal |
| collection | DOAJ |
| description | Abstract The COVID-19 pandemic has dramatically highlighted the importance of developing simulation systems for quickly characterizing and providing spatio-temporal forecasts of infection spread dynamics that take specific accounts of the population and spatial heterogeneities that govern pathogen transmission in real-world communities. Developing such computational systems must also overcome the cold-start problem related to the inevitable scarce early data and extant knowledge regarding a novel pathogen’s transmissibility and virulence, while addressing changing population behavior and policy options as a pandemic evolves. Here, we describe how we have coupled advances in the construction of digital or virtual models of real-world cities with an agile, modular, agent-based model of viral transmission and data from navigation and social media interactions, to overcome these challenges in order to provide a new simulation tool, CitySEIRCast, that can model viral spread at the sub-national level. Our data pipelines and workflows are designed purposefully to be flexible and scalable so that we can implement the system on hybrid cloud/cluster systems and be agile enough to address different population settings and indeed, diseases. Our simulation results demonstrate that CitySEIRCast can provide the timely high resolution spatio-temporal epidemic predictions required for supporting situational awareness of the state of a pandemic as well as for facilitating assessments of vulnerable sub-populations and locations and evaluations of the impacts of implemented interventions, inclusive of the effects of population behavioral response to fluctuations in case incidence. This work arose in response to requests from county agencies to support their work on COVID-19 monitoring, risk assessment, and planning, and using the described workflows, we were able to provide uninterrupted bi-weekly simulations to guide their efforts for over a year from late 2021 to 2023. We discuss future work that can significantly improve the scalability and real-time application of this digital city-based epidemic modelling system, such that validated predictions and forecasts of the paths that may followed by a contagion both over time and space can be used to anticipate the spread dynamics, risky groups and regions, and options for responding effectively to a complex epidemic. |
| format | Article |
| id | doaj-art-efe277e251d44c5bb7e87e4cf376e86f |
| institution | Kabale University |
| issn | 2199-4536 2198-6053 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Springer |
| record_format | Article |
| series | Complex & Intelligent Systems |
| spelling | doaj-art-efe277e251d44c5bb7e87e4cf376e86f2025-02-02T12:49:30ZengSpringerComplex & Intelligent Systems2199-45362198-60532024-12-0111112910.1007/s40747-024-01683-xCitySEIRCast: an agent-based city digital twin for pandemic analysis and simulationShakir Bilal0Wajdi Zaatour1Yilian Alonso Otano2Arindam Saha3Ken Newcomb4Soo Kim5Jun Kim6Raveena Ginjala7Derek Groen8Edwin Michael9Center for Global Health Infectious Disease Research, College of Public Health, University of South FloridaCenter for Global Health Infectious Disease Research, College of Public Health, University of South FloridaCenter for Global Health Infectious Disease Research, College of Public Health, University of South FloridaCenter for Global Health Infectious Disease Research, College of Public Health, University of South FloridaCenter for Global Health Infectious Disease Research, College of Public Health, University of South FloridaCenter for Global Health Infectious Disease Research, College of Public Health, University of South FloridaCenter for Global Health Infectious Disease Research, College of Public Health, University of South FloridaDepartment of Computer Science & Engineering, University of South FloridaModeling & Simulation Group, Department of Computer Science, Brunel University LondonCenter for Global Health Infectious Disease Research, College of Public Health, University of South FloridaAbstract The COVID-19 pandemic has dramatically highlighted the importance of developing simulation systems for quickly characterizing and providing spatio-temporal forecasts of infection spread dynamics that take specific accounts of the population and spatial heterogeneities that govern pathogen transmission in real-world communities. Developing such computational systems must also overcome the cold-start problem related to the inevitable scarce early data and extant knowledge regarding a novel pathogen’s transmissibility and virulence, while addressing changing population behavior and policy options as a pandemic evolves. Here, we describe how we have coupled advances in the construction of digital or virtual models of real-world cities with an agile, modular, agent-based model of viral transmission and data from navigation and social media interactions, to overcome these challenges in order to provide a new simulation tool, CitySEIRCast, that can model viral spread at the sub-national level. Our data pipelines and workflows are designed purposefully to be flexible and scalable so that we can implement the system on hybrid cloud/cluster systems and be agile enough to address different population settings and indeed, diseases. Our simulation results demonstrate that CitySEIRCast can provide the timely high resolution spatio-temporal epidemic predictions required for supporting situational awareness of the state of a pandemic as well as for facilitating assessments of vulnerable sub-populations and locations and evaluations of the impacts of implemented interventions, inclusive of the effects of population behavioral response to fluctuations in case incidence. This work arose in response to requests from county agencies to support their work on COVID-19 monitoring, risk assessment, and planning, and using the described workflows, we were able to provide uninterrupted bi-weekly simulations to guide their efforts for over a year from late 2021 to 2023. We discuss future work that can significantly improve the scalability and real-time application of this digital city-based epidemic modelling system, such that validated predictions and forecasts of the paths that may followed by a contagion both over time and space can be used to anticipate the spread dynamics, risky groups and regions, and options for responding effectively to a complex epidemic.https://doi.org/10.1007/s40747-024-01683-xAgent-based modelingCity-scale digital twinsDisease transmissionEpidemiologyGeospatial modelingHealthcare interventions |
| spellingShingle | Shakir Bilal Wajdi Zaatour Yilian Alonso Otano Arindam Saha Ken Newcomb Soo Kim Jun Kim Raveena Ginjala Derek Groen Edwin Michael CitySEIRCast: an agent-based city digital twin for pandemic analysis and simulation Complex & Intelligent Systems Agent-based modeling City-scale digital twins Disease transmission Epidemiology Geospatial modeling Healthcare interventions |
| title | CitySEIRCast: an agent-based city digital twin for pandemic analysis and simulation |
| title_full | CitySEIRCast: an agent-based city digital twin for pandemic analysis and simulation |
| title_fullStr | CitySEIRCast: an agent-based city digital twin for pandemic analysis and simulation |
| title_full_unstemmed | CitySEIRCast: an agent-based city digital twin for pandemic analysis and simulation |
| title_short | CitySEIRCast: an agent-based city digital twin for pandemic analysis and simulation |
| title_sort | cityseircast an agent based city digital twin for pandemic analysis and simulation |
| topic | Agent-based modeling City-scale digital twins Disease transmission Epidemiology Geospatial modeling Healthcare interventions |
| url | https://doi.org/10.1007/s40747-024-01683-x |
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