We will consider pros and cons of agent-based models for studying pandemics, tracing a spatiotemporal spread of infection across a nation. In doing so, we will introduce our open-source software, AMTraC-19 (Agent-based Model of Transmission and Control of the COVID-19 pandemic in Australia).
Highlights
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We will present an agent-based modelling (ABM) framework for studying pandemics, exemplified by the COVID-19, as well as the AMTraC-19 software which implements an ABM for a fine-grained computational simulation of the COVID-19 pandemic in Australia. This model is calibrated to reproduce several features of COVID-19 transmission, including its age-dependent epidemiological characteristics. The individual-based epidemiological model accounts for mobility (worker and student commuting) patterns and human interactions derived from the Australian census and other national data sources. The high-precision simulation comprises approximately 24 million stochastically generated software agents, representing the population of Australia, and traces various scenarios of the COVID-19 pandemic in Australia.
The AMTraC-19 software has been used to evaluate various intervention strategies, including (1) non-pharmaceutical interventions, such as restrictions on international air travel, case isolation, home quarantine, school closures, and stay-at-home restrictions with varying levels of compliance (i.e., "social distancing"), and (2) pharmaceutical interventions, such as pre-pandemic vaccination phase and progressive vaccination rollout.
Highlights
- introduction to key epidemiological concepts
- large-scale agent-based modelling
- introduction to open-source software, AMTraC-19
Download slides
We will present an agent-based modelling (ABM) framework for studying pandemics, exemplified by the COVID-19, as well as the AMTraC-19 software which implements an ABM for a fine-grained computational simulation of the COVID-19 pandemic in Australia. This model is calibrated to reproduce several features of COVID-19 transmission, including its age-dependent epidemiological characteristics. The individual-based epidemiological model accounts for mobility (worker and student commuting) patterns and human interactions derived from the Australian census and other national data sources. The high-precision simulation comprises approximately 24 million stochastically generated software agents, representing the population of Australia, and traces various scenarios of the COVID-19 pandemic in Australia.
The AMTraC-19 software has been used to evaluate various intervention strategies, including (1) non-pharmaceutical interventions, such as restrictions on international air travel, case isolation, home quarantine, school closures, and stay-at-home restrictions with varying levels of compliance (i.e., "social distancing"), and (2) pharmaceutical interventions, such as pre-pandemic vaccination phase and progressive vaccination rollout.
References
- S. L. Chang, O. M. Cliff, C. Zachreson, M. Prokopenko, Simulating transmission scenarios of the Delta variant of SARS-CoV-2 in Australia, Frontiers in Public Health, 10.3389, 2022 (also on arXiv).
- C. Zachreson, S. L. Chang, O. M. Cliff, M. Prokopenko, How will mass-vaccination change COVID-19 lockdown requirements in Australia?, The Lancet Regional Health – Western Pacific, 14: 100224, 2021.
- S. L. Chang, N. Harding, C. Zachreson, O. M. Cliff, M. Prokopenko, Modelling transmission and control of the COVID-19 pandemic in Australia, Nature Communications, 11, 5710, 2020.
- R. J. Rockett, A. Arnott, C. Lam, R. Sadsad, V. Timms, K.-A. Gray, J.-S. Eden, S. L. Chang, M. Gall, J. Draper, E. Sim, N. L. Bachmann, I. Carter, K. Basile, R. Byun, M. V. O. Sullivan, S. C. A. Chen, S. Maddocks, T. C. Sorrell, D. E. Dwyer, E. C. Holmes, J. Kok, M. Prokopenko, V. Sintchenko, Revealing COVID-19 transmission by SARS-CoV-2 genome sequencing and agent based modelling, Nature Medicine, 26: 1398–1404, 2020.
- C. Zachreson, K. M. Fair, N. Harding, M. Prokopenko, Interfering with influenza: nonlinear coupling of reactive and static mitigation strategies, Journal of Royal Society Interface, 17(165): 20190728, 2020.
- N. Harding, R. E. Spinney, M. Prokopenko, Phase transitions in spatial connectivity during influenza pandemics, Entropy, 22(2), 133, 2020.
- C. Zachreson, K. M. Fair, O. M. Cliff, N. Harding, M. Piraveenan, M. Prokopenko, Urbanization affects peak timing, prevalence, and bimodality of influenza pandemics in Australia: Results of a census-calibrated model, Science Advances, 4(12), eaau5294, 2018.
- O. M. Cliff, N. Harding, M. Piraveenan, E. Y. Erten, M. Gambhir, M. Prokopenko, Investigating Spatiotemporal Dynamics and Synchrony of Influenza Epidemics in Australia: An Agent-Based Modelling Approach, Simulation Modelling Practice and Theory, 87, 412-431, 2018 (also on arXiv).