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AMTraC-19 Source Code: Agent-based Model of Transmission and Control of the COVID-19 pandemic in Australia

Authors: Chang, Sheryl L.; Harding, Nathan; Zachreson, Cameron; Cliff, Oliver M.; Prokopenko, Mikhail;

AMTraC-19 Source Code: Agent-based Model of Transmission and Control of the COVID-19 pandemic in Australia

Abstract

The software implements an agent-based model 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 and traces various scenarios of the COVID-19 pandemic in Australia. The 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. The paper describing the model and the scenarios investigated with AMTRaC-19 (v7_7d): S. L. Chang, C. Zachreson, O. M. Cliff, M. Prokopenko, Simulating transmission scenarios of the Delta variant of SARS-CoV-2 in Australia, Frontiers in Public Health, 10, 10.3389/fpubh.2022.823043, 2022. Please cite it, as well as other publications referenced below, when using the software. The dataset generated during this study is also available on Zenodo: S. L. Chang, O. M. Cliff, C. Zachreson & M. Prokopenko. (2021). AMTraC-19 (v7.7d) Dataset: Simulating transmission scenarios of the Delta variant of SARS-CoV-2 in Australia (Version v1) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.5726241

{"references": ["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.", "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 \u2013 Western Pacific, 14: 100224, 2021.", "S. L. Chang, C. Zachreson, O. M. Cliff, M. Prokopenko, Simulating transmission scenarios of the Delta variant of SARS-CoV-2 in Australia, Frontiers in Public Health, 10, 10.3389/fpubh.2022.823043, 2022.", "S. L. Chang, O. M. Cliff, C. Zachreson, M. Prokopenko. (2021). AMTraC-19 (v7.7d) Dataset: Simulating transmission scenarios of the Delta variant of SARS-CoV-2 in Australia (Version v1) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.5726241"]}

This work was partially supported by the Australian Research Council grant DP200103005 (MP and SLC). Additionally, CZ is supported in part by National Health and Medical Research Council project grant (APP1165876). AMTraC-19 is registered under The University of Sydney's invention disclosure CDIP Ref. 2020-018.

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Keywords

pandemic model, pandemic intervention, SARS-CoV-2, discrete-time simulation, social distancing, COVID-19, computational epidemiology, vaccination, agent-based model

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This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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