How will mass-vaccination change COVID-19 lockdown requirements in Australia?

被引:32
作者
Zachreson, Cameron [1 ,2 ]
Chang, Sheryl L. [1 ]
Cliff, Oliver M. [1 ]
Prokopenko, Mikhail [1 ,3 ]
机构
[1] Univ Sydney, Fac Engn, Ctr Complex Syst, Sydney, NSW 2006, Australia
[2] Univ Melbourne, Sch Comp & Informat Syst, Parkville, Vic 3052, Australia
[3] Univ Sydney, Sydney Inst Infect Dis, Westmead, NSW 2145, Australia
来源
LANCET REGIONAL HEALTH-WESTERN PACIFIC | 2021年 / 14卷
基金
澳大利亚研究理事会;
关键词
COVID-19; SARS-CoV-2; pandemics; epidemic growth rate; interventions; mass vaccination; herd immunity; vaccine efficacy; computational epidemiology; agent-based modelling; INFLUENZA; DYNAMICS;
D O I
10.1016/j.lanwpc.2021.100224
中图分类号
R19 [保健组织与事业(卫生事业管理)];
学科分类号
摘要
Background To prevent future outbreaks of COVID-19, Australia is pursuing a mass-vaccination approach in which a targeted group of the population comprising healthcare workers, aged-care residents and other individuals at increased risk of exposure will receive a highly effective priority vaccine. The rest of the population will instead have access to a less effective vaccine. Methods We apply a large-scale agent-based model of COVID-19 in Australia to investigate the possible implications of this hybrid approach to mass-vaccination. The model is calibrated to recent epidemiological and demographic data available in Australia, and accounts for several components of vaccine efficacy. Findings Within a feasible range of vaccine efficacy values, our model supports the assertion that complete herd immunity due to vaccination is not likely in the Australian context. For realistic scenarios in which herd immunity is not achieved, we simulate the effects of mass-vaccination on epidemic growth rate, and investigate the requirements of lockdown measures applied to curb subsequent outbreaks. In our simulations, Australia's vaccination strategy can feasibly reduce required lockdown intensity and initial epidemic growth rate by 43% and 52%, respectively. The severity of epidemics, as measured by the peak number of daily new cases, decreases by up to two orders of magnitude under plausible mass-vaccination and lockdown strategies. Interpretation The study presents a strong argument for a large-scale vaccination campaign in Australia, which would substantially reduce both the intensity of future outbreaks and the stringency of non-pharmaceutical interventions required for their suppression. Funding Australian Research Council; National Health and Medical Research Council. (c) 2021 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license ( http://creativecommons.org/licenses/by-nc-nd/4.0/ )
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页数:12
相关论文
共 39 条
[1]   Vaccine Efficacy Needed for a COVID-19 Coronavirus Vaccine to Prevent or Stop an Epidemic as the Sole Intervention [J].
Bartsch, Sarah M. ;
O'Shea, Kelly J. ;
Ferguson, Marie C. ;
Bottazzi, Maria Elena ;
Wedlock, Patrick T. ;
Strych, Ulrich ;
McKinnell, James A. ;
Siegmund, Sheryl S. ;
Cox, Sarah N. ;
Hotez, Peter J. ;
Lee, Bruce Y. .
AMERICAN JOURNAL OF PREVENTIVE MEDICINE, 2020, 59 (04) :493-503
[2]  
Biddle N., 2021, Change in vaccine willingness in Australia: August 2020 to January 2021
[3]   Reproductive number of coronavirus: A systematic review and meta-analysis based on global level evidence [J].
Billah, Md. Arif ;
Miah, Md. Mamun ;
Khan, Md. Nuruzzaman .
PLOS ONE, 2020, 15 (11)
[4]  
Centers for disease control and prevention, INT GU DUR ISOL PREC
[5]  
Centers for disease control and prevention, COVID 19 INF PED HLT
[6]   Impact of network assortativity on epidemic and vaccination behaviour [J].
Chang, Sheryl L. ;
Piraveenan, Mahendra ;
Prokopenko, Mikhail .
CHAOS SOLITONS & FRACTALS, 2020, 140
[7]   Modelling transmission and control of the COVID-19 pandemic in Australia [J].
Chang, Sheryl L. ;
Harding, Nathan ;
Zachreson, Cameron ;
Cliff, Oliver M. ;
Prokopenko, Mikhail .
NATURE COMMUNICATIONS, 2020, 11 (01)
[8]   Investigating spatiotemporal dynamics and synchrony of influenza epidemics in Australia: An agent-based modelling approach [J].
Cliff, Oliver M. ;
Harding, Nathan ;
Piraveenan, Mahendra ;
Erten, E. Yagmur ;
Gambhir, Manoj ;
Prokopenko, Mikhail .
SIMULATION MODELLING PRACTICE AND THEORY, 2018, 87 :412-431
[9]   BNT162b2 mRNA Covid-19 Vaccine in a Nationwide Mass Vaccination Setting [J].
Dagan, Noa ;
Barda, Noam ;
Kepten, Eldad ;
Miron, Oren ;
Perchik, Shay ;
Katz, Mark A. ;
Hernan, Miguel A. ;
Lipsitch, Marc ;
Reis, Ben ;
Balicer, Ran D. .
NEW ENGLAND JOURNAL OF MEDICINE, 2021, 384 (15) :1412-1423
[10]   Estimated transmissibility and impact of SARS-CoV-2 lineage B.1.1.7 in England [J].
Davies, Nicholas G. ;
Abbott, Sam ;
Barnard, Rosanna C. ;
Jarvis, Christopher, I ;
Kucharski, Adam J. ;
Munday, James D. ;
Pearson, Carl A. B. ;
Russell, Timothy W. ;
Tully, Damien C. ;
Washburne, Alex D. ;
Wenseleers, Tom ;
Gimma, Amy ;
Waites, William ;
Wong, Kerry L. M. ;
van Zandvoort, Kevin ;
Silverman, Justin D. ;
Diaz-Ordaz, Karla ;
Keogh, Ruth ;
Eggo, Rosalind M. ;
Funk, Sebastian ;
Jit, Mark ;
Atkins, Katherine E. ;
Edmunds, W. John .
SCIENCE, 2021, 372 (6538) :149-+