1. Epidemiology and Global Health
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Early analysis of the Australian COVID-19 epidemic

  1. David J Price  Is a corresponding author
  2. Freya M Shearer  Is a corresponding author
  3. Michael T Meehan
  4. Emma McBryde
  5. Robert Moss
  6. Nick Golding
  7. Eamon J Conway
  8. Peter Dawson
  9. Deborah Cromer
  10. James Wood
  11. Sam Abbott
  12. Jodie McVernon
  13. James M McCaw
  1. The University of Melbourne, Australia
  2. James Cook University, Australia
  3. Wellcome Trust Centre for Human Genetics, United Kingdom
  4. Department of Defence, Australia
  5. University of New South Wales, Australia
  6. London School of Hygiene and Tropical Medicine, United Kingdom
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Cite this article as: eLife 2020;9:e58785 doi: 10.7554/eLife.58785

Abstract

As of 1 May 2020, there had been 6,808 confirmed cases of COVID-19 in Australia. Of these, 98 had died from the disease. The epidemic had been in decline since mid-March, with 308 cases confirmed nationally since 14 April. This suggests that the collective actions of the Australian public and government authorities in response to COVID-19 were sufficiently early and assiduous to avert a public health crisis — for now. Analysing factors that contribute to individual country experiences of COVID-19, such as the intensity and timing of public health interventions, will assist in the next stage of response planning globally. We describe how the epidemic and public health response unfolded in Australia up to 13 April. We estimate that the effective reproduction number was likely below 1 in each Australian state since mid-March and forecast that clinical demand would remain below capacity thresholds over the forecast period (from mid-to-late April).

Data availability

Analysis code is included in the supplementary materials. Datasets analysed and generated during this study are included in the supplementary materials. For estimates of the time-varying effective reproduction number (Figure 2), the complete line listed data within the Australian national COVID-19 database are not publicly available. However, we provide the cases per day by notification date and state (as shown in Figures 1 and S1) which, when supplemented with the estimated distribution of the delay from symptom onset to notification (samples from this distribution are provided as a data file), analyses of the time-varying effective reproduction number can be performed.

Article and author information

Author details

  1. David J Price

    Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Australia
    For correspondence
    david.price1@unimelb.edu.au
    Competing interests
    David J Price, This work was undertaken with direct funding support from the Australian Government Department of Health, Office of Health Protection and has assisted the Australian Government in its epidemic response activities..
  2. Freya M Shearer

    School of Population and Global Health, The University of Melbourne, Melbourne, Australia
    For correspondence
    freya.shearer@unimelb.edu.au
    Competing interests
    Freya M Shearer, This work was undertaken with direct funding support from the Australian Government Department of Health, Office of Health Protection and has assisted the Australian Government in its epidemic response activities..
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-9600-3473
  3. Michael T Meehan

    Australian Institute of Tropical Health and Medicine, James Cook University, Townsville, Australia
    Competing interests
    Michael T Meehan, This work was undertaken with direct funding support from the Australian Government Department of Health, Office of Health Protection and has assisted the Australian Government in its epidemic response activities..
  4. Emma McBryde

    Australian Institute of Tropical Health and Medicine, James Cook University, Townsville, Australia
    Competing interests
    Emma McBryde, This work was undertaken with direct funding support from the Australian Government Department of Health, Office of Health Protection and has assisted the Australian Government in its epidemic response activities..
  5. Robert Moss

    School of Population and Global Health, The University of Melbourne, Melbourne, Australia
    Competing interests
    Robert Moss, This work was undertaken with direct funding support from the Australian Government Department of Health, Office of Health Protection and has assisted the Australian Government in its epidemic response activities..
  6. Nick Golding

    Spatial Ecology and Epidemiology Group, Wellcome Trust Centre for Human Genetics, Oxford, United Kingdom
    Competing interests
    Nick Golding, This work was undertaken with direct funding support from the Australian Government Department of Health, Office of Health Protection and has assisted the Australian Government in its epidemic response activities..
  7. Eamon J Conway

    Victorian Infectious Diseases Reference Laboratory Epidemiology Unit at The Peter Doherty Institute for Infection and Immunity, The University of Melbourne, Melbourne, Australia
    Competing interests
    Eamon J Conway, This work was undertaken with direct funding support from the Australian Government Department of Health, Office of Health Protection and has assisted the Australian Government in its epidemic response activities..
  8. Peter Dawson

    Defence Science and Technology, Department of Defence, Melbourne, Australia
    Competing interests
    Peter Dawson, This work was undertaken with direct funding support from the Australian Government Department of Health, Office of Health Protection and has assisted the Australian Government in its epidemic response activities..
  9. Deborah Cromer

    Infection Analytics Program, Kirby Institute for Infection and Immunity, University of New South Wales, Sydney, Australia
    Competing interests
    Deborah Cromer, This work was undertaken with direct funding support from the Australian Government Department of Health, Office of Health Protection and has assisted the Australian Government in its epidemic response activities..
  10. James Wood

    School of Public Health and Community Medicine, University of New South Wales, Sydney, Australia
    Competing interests
    James Wood, This work was undertaken with direct funding support from the Australian Government Department of Health, Office of Health Protection and has assisted the Australian Government in its epidemic response activities..
  11. Sam Abbott

    Centre for the Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, United Kingdom
    Competing interests
    Sam Abbott, This work was undertaken with direct funding support from the Australian Government Department of Health, Office of Health Protection and has assisted the Australian Government in its epidemic response activities..
  12. Jodie McVernon

    Population health, The University of Melbourne, Parkville, Australia
    Competing interests
    Jodie McVernon, This work was undertaken with direct funding support from the Australian Government Department of Health, Office of Health Protection and has assisted the Australian Government in its epidemic response activities..
  13. James M McCaw

    School of Mathematics and Statistics, The University of Melbourne, Melbourne, Australia
    Competing interests
    James M McCaw, This work was undertaken with direct funding support from the Australian Government Department of Health, Office of Health Protection and has assisted the Australian Government in its epidemic response activities..
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-2452-3098

Funding

Department of Health, Australian Government (NA)

  • James M McCaw

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Reviewing Editor

  1. Ben S Cooper, Mahidol University, Thailand

Publication history

  1. Received: May 11, 2020
  2. Accepted: August 12, 2020
  3. Accepted Manuscript published: August 13, 2020 (version 1)
  4. Version of Record published: August 26, 2020 (version 2)

Copyright

© 2020, Price et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

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  1. Further reading

Further reading

    1. Computational and Systems Biology
    2. Epidemiology and Global Health
    Ewan Colman et al.
    Research Article

    Humans and other group-living animals tend to distribute their social effort disproportionately. Individuals predominantly interact with a small number of close companions while maintaining weaker social bonds with less familiar group members. By incorporating this behavior into a mathematical model, we find that a single parameter, which we refer to as social fluidity, controls the rate of social mixing within the group. Large values of social fluidity correspond to gregarious behavior, whereas small values signify the existence of persistent bonds between individuals. We compare the social fluidity of 13 species by applying the model to empirical human and animal social interaction data. To investigate how social behavior influences the likelihood of an epidemic outbreak, we derive an analytical expression of the relationship between social fluidity and the basic reproductive number of an infectious disease. For species that form more stable social bonds, the model describes frequency-dependent transmission that is sensitive to changes in social fluidity. As social fluidity increases, animal-disease systems become increasingly density-dependent. Finally, we demonstrate that social fluidity is a stronger predictor of disease outcomes than both group size and connectivity, and it provides an integrated framework for both density-dependent and frequency-dependent transmission.

    1. Epidemiology and Global Health
    Daniel J Laydon et al.
    Research Article Updated

    Background:

    Sanofi-Pasteur’s CYD-TDV is the only licensed dengue vaccine. Two phase three trials showed higher efficacy in seropositive than seronegative recipients. Hospital follow-up revealed increased hospitalisation in 2–5- year-old vaccinees, where serostatus and age effects were unresolved.

    Methods:

    We fit a survival model to individual-level data from both trials, including year 1 of hospital follow-up. We determine efficacy by age, serostatus, serotype and severity, and examine efficacy duration and vaccine action mechanism.

    Results:

    Our modelling indicates that vaccine-induced immunity is long-lived in seropositive recipients, and therefore that vaccinating seropositives gives higher protection than two natural infections. Long-term increased hospitalisation risk outweighs short-lived immunity in seronegatives. Independently of serostatus, transient immunity increases with age, and is highest against serotype 4. Benefit is higher in seropositives, and risk enhancement is greater in seronegatives, against hospitalised disease than against febrile disease.

    Conclusions:

    Our results support vaccinating seropositives only. Rapid diagnostic tests would enable viable ‘screen-then-vaccinate’ programs. Since CYD-TDV acts as a silent infection, long-term safety of other vaccine candidates must be closely monitored.

    Funding:

    Bill & Melinda Gates Foundation, National Institute for Health Research, UK Medical Research Council, Wellcome Trust, Royal Society.

    Clinical trial number:

    NCT01373281 and NCT01374516.