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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Main Authors: Shakir Bilal, Wajdi Zaatour, Yilian Alonso Otano, Arindam Saha, Ken Newcomb, Soo Kim, Jun Kim, Raveena Ginjala, Derek Groen, Edwin Michael
Format: Article
Language:English
Published: Springer 2024-12-01
Series:Complex & Intelligent Systems
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Online Access:https://doi.org/10.1007/s40747-024-01683-x
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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.
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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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