DOI: 10.5281/zenodo.5726240
title: AMTraC-19 (v7.7d) Dataset: Simulating transmission scenarios of the Delta variant of SARS-CoV-2 in Australia

description: A preprint paper describing scenarios which generated this dataset can be accessed here: https://arxiv.org/abs/2107.06617. Please cite this work when using the dataset:<br> S. L. Chang, C. Zachreson, O. M. Cliff, M. Prokopenko, Simulating transmission scenarios of the Delta variant of SARS-CoV-2 in Australia, arXiv: 2107.06617, 2021. Abstract. An outbreak of the Delta (B.1.617.2) variant of SARS-CoV-2 that began around mid-June 2021 in Sydney, Australia, quickly developed into a nation-wide epidemic. The ongoing epidemic is of major concern as the Delta variant is more infectious than previous variants that circulated in Australia in 2020. Using a re-calibrated agent-based model, we explored a feasible range of non-pharmaceutical interventions, including case isolation, home quarantine, school closures, and stay-at-home restrictions (i.e., "social distancing"). Our modelling indicated that the levels of reduced interactions in workplaces and across communities attained in Sydney and other parts of the nation were inadequate for controlling the outbreak. A counter-factual analysis suggested that if 70% of the population followed tight stay-at-home restrictions, then at least 45 days would have been needed for new daily cases to fall from their peak to below ten per day. Our model successfully predicted that, under a progressive vaccination rollout, if 40-50% of the Australian population follow stay-at-home restrictions, the incidence will peak by mid-October 2021. We also quantified an expected burden on the healthcare system and potential fatalities across Australia. The AMTraC-19 source code (v7.7d) is released on Zenodo: https://zenodo.org/record/5778218
description: 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.
description: {"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, 10.3389/fpubh.2022.823043, 2022.", "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, 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\u20131404, 2020.", "K. M. Fair, C. Zachreson, M. Prokopenko, Creating a surrogate commuter network from Australian Bureau of Statistics census data, Scientific Data, 6, 150, 2019.", "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"]}