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Modelling transmission and control of the COVID-19 pandemic in Australia.
Nat Commun. 2020 11 11; 11(1):5710.NC

Abstract

There is a continuing debate on relative benefits of various mitigation and suppression strategies aimed to control the spread of COVID-19. Here we report the results of agent-based modelling using a fine-grained computational simulation of the ongoing COVID-19 pandemic in Australia. This model is calibrated to match key characteristics of COVID-19 transmission. An important calibration outcome is the age-dependent fraction of symptomatic cases, with this fraction for children found to be one-fifth of such fraction for adults. We apply the model to compare several intervention strategies, including restrictions on international air travel, case isolation, home quarantine, social distancing with varying levels of compliance, and school closures. School closures are not found to bring decisive benefits unless coupled with high level of social distancing compliance. We report several trade-offs, and an important transition across the levels of social distancing compliance, in the range between 70% and 80% levels, with compliance at the 90% level found to control the disease within 13-14 weeks, when coupled with effective case isolation and international travel restrictions.

Authors+Show Affiliations

Centre for Complex Systems, Faculty of Engineering, University of Sydney, Sydney, NSW, 2006, Australia.Centre for Complex Systems, Faculty of Engineering, University of Sydney, Sydney, NSW, 2006, Australia.Centre for Complex Systems, Faculty of Engineering, University of Sydney, Sydney, NSW, 2006, Australia.Centre for Complex Systems, Faculty of Engineering, University of Sydney, Sydney, NSW, 2006, Australia.Centre for Complex Systems, Faculty of Engineering, University of Sydney, Sydney, NSW, 2006, Australia. mikhail.prokopenko@sydney.edu.au. Marie Bashir Institute for Infectious Diseases and Biosecurity, University of Sydney, Westmead, NSW, 2145, Australia. mikhail.prokopenko@sydney.edu.au.

Pub Type(s)

Journal Article
Research Support, Non-U.S. Gov't

Language

eng

PubMed ID

33177507

Citation

Chang, Sheryl L., et al. "Modelling Transmission and Control of the COVID-19 Pandemic in Australia." Nature Communications, vol. 11, no. 1, 2020, p. 5710.
Chang SL, Harding N, Zachreson C, et al. Modelling transmission and control of the COVID-19 pandemic in Australia. Nat Commun. 2020;11(1):5710.
Chang, S. L., Harding, N., Zachreson, C., Cliff, O. M., & Prokopenko, M. (2020). Modelling transmission and control of the COVID-19 pandemic in Australia. Nature Communications, 11(1), 5710. https://doi.org/10.1038/s41467-020-19393-6
Chang SL, et al. Modelling Transmission and Control of the COVID-19 Pandemic in Australia. Nat Commun. 2020 11 11;11(1):5710. PubMed PMID: 33177507.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Modelling transmission and control of the COVID-19 pandemic in Australia. AU - Chang,Sheryl L, AU - Harding,Nathan, AU - Zachreson,Cameron, AU - Cliff,Oliver M, AU - Prokopenko,Mikhail, Y1 - 2020/11/11/ PY - 2020/04/23/received PY - 2020/10/09/accepted PY - 2020/11/12/entrez PY - 2020/11/13/pubmed PY - 2020/11/25/medline SP - 5710 EP - 5710 JF - Nature communications JO - Nat Commun VL - 11 IS - 1 N2 - There is a continuing debate on relative benefits of various mitigation and suppression strategies aimed to control the spread of COVID-19. Here we report the results of agent-based modelling using a fine-grained computational simulation of the ongoing COVID-19 pandemic in Australia. This model is calibrated to match key characteristics of COVID-19 transmission. An important calibration outcome is the age-dependent fraction of symptomatic cases, with this fraction for children found to be one-fifth of such fraction for adults. We apply the model to compare several intervention strategies, including restrictions on international air travel, case isolation, home quarantine, social distancing with varying levels of compliance, and school closures. School closures are not found to bring decisive benefits unless coupled with high level of social distancing compliance. We report several trade-offs, and an important transition across the levels of social distancing compliance, in the range between 70% and 80% levels, with compliance at the 90% level found to control the disease within 13-14 weeks, when coupled with effective case isolation and international travel restrictions. SN - 2041-1723 UR - https://www.unboundmedicine.com/medline/citation/33177507/Modelling_transmission_and_control_of_the_COVID_19_pandemic_in_Australia_ DB - PRIME DP - Unbound Medicine ER -