A modelling approach to estimate the transmissibility of SARS-CoV-2 during periods of high, low, and zero case incidence

  1. Nick Golding  Is a corresponding author
  2. David J Price
  3. Gerard Ryan
  4. Jodie McVernon
  5. James M McCaw
  6. Freya M Shearer
  1. Curtin University, Australia
  2. University of Melbourne, Australia
  3. Telethon Kids Institute, Australia

Abstract

Against a backdrop ofwidespread global transmission, a number of countries have successfully brought large outbreaks of COVID-19 under control and maintained near-elimination status. A key element of epidemic response is the tracking of disease transmissibility in near real-time. During major out-breaks, the effective reproduction number can be estimated froma time-series of case, hospitalisation or death counts. In low or zero incidence settings, knowing the potential for the virus to spread is a response priority. Absence of case data means that this potential cannot be estimated directly. We present a semi-mechanisticmodelling framework that draws on time-series of both behavioural data and case data (when disease activity is present) to estimate the transmissibility of SARS-CoV-2 fromperiods of high to low- or zero- case incidence, with a coherent transition in interpretation across the changing epidemiological situations. Of note, during periods of epidemic activity, our analysis recovers the effective reproduction number, while during periods of low- or zero- case incidence, it provides an estimate of transmission risk. This enables tracking and planning of progress towards the control of large outbreaks, maintenance of virus suppression, and monitoring the risk posed by re-introduction of the virus. We demonstrate the value of our methods by reporting on their use throughout 2020 in Australia, where they have become a central component of the national COVID-19 response.

Data availability

Datasets analysed and generated during this study are available at the following link: https://figshare.com/s/0e13ccc2f731149d45d1. For estimates of the time-varying effective reproduction number and transmission potential (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 (Data files 1 and 2) which, when supplemented with the estimated distribution of the delay from symptom onset to notification as in Figure 3D and H (provided in Data files 3 and 4), and Data files 5-10, analyses of the time-varying effective reproduction number and transmission potential can be performed. Data files 5-10 contain the numerical data, output from each of the model components, used to generate Figure 3. For access to the raw data, a request must be submitted via NNDSS.datarequests@health.gov.au which will be assessed by a data committee.Model code for performing the analyses and generating the figures is available at: https://github.com/goldingn/covid19_australia_interventions

The following data sets were generated

Article and author information

Author details

  1. Nick Golding

    Spatial Ecology and Epidemiology Group, Curtin University, Bentley, Australia
    For correspondence
    nick.golding.research@gmail.com
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-8916-5570
  2. David J Price

    Melbourne School of Population and Global Health, University of Melbourne, Parkville, Australia
    Competing interests
    No competing interests declared.
  3. Gerard Ryan

    Telethon Kids Institute, Nedlands, Australia
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-0183-7630
  4. Jodie McVernon

    Melbourne School of Population and Global Health, University of Melbourne, Parkville, Australia
    Competing interests
    No competing interests declared.
  5. James M McCaw

    School of Mathematics and Statistics, University of Melbourne, Parkville, Australia
    Competing interests
    James M McCaw, Reviewing editor, eLife.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-2452-3098
  6. Freya M Shearer

    School of Population and Global Health, University of Melbourne, Parkville, Australia
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-9600-3473

Funding

Australian Government

  • Nick Golding
  • David J Price
  • Gerard Ryan
  • Jodie McVernon
  • James M McCaw
  • Freya M Shearer

Australian Research Council (DE180100635)

  • Nick Golding

National Health and Medical Research Council (GNT1170960)

  • Jodie McVernon
  • James M McCaw

National Health and Medical Research Council (GNT1117140)

  • Jodie McVernon

National Health and Medical Research Council (2021/GNT2010051)

  • Freya M Shearer

World Health Organization

  • Nick Golding
  • David J Price
  • Gerard Ryan
  • Jodie McVernon
  • James M McCaw
  • Freya M Shearer

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

Reviewing Editor

  1. Caroline Colijn, Simon Fraser University, Canada

Ethics

Human subjects: The study was undertaken as urgent public health action to support Australia's COVID-19 pandemic response. The study used data from the Australian National Notifiable Disease Surveillance System (NNDSS) provided to the Australian Government Department of Health under the National Health Security Agreement for the purposes of national communicable disease surveillance. Data from the NNDSS were supplied after de-identification to the investigator team for the purposes of provision of epidemiological advice to government. Contractual obligations established strict data protection protocols agreed between the University of Melbourne and sub-contractors and the Australian Government Department of Health, with oversight and approval for use in supporting Australia's pandemic response and for publication provided by the data custodians represented by the Communicable Diseases Network of Australia. The ethics of the use of these data for these purposes, including publication, was agreed by the Department of Health with the Communicable Diseases Network of Australia.

Version history

  1. Preprint posted: November 29, 2021 (view preprint)
  2. Received: February 22, 2022
  3. Accepted: January 16, 2023
  4. Accepted Manuscript published: January 20, 2023 (version 1)
  5. Version of Record published: March 8, 2023 (version 2)

Copyright

© 2023, Golding 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. Nick Golding
  2. David J Price
  3. Gerard Ryan
  4. Jodie McVernon
  5. James M McCaw
  6. Freya M Shearer
(2023)
A modelling approach to estimate the transmissibility of SARS-CoV-2 during periods of high, low, and zero case incidence
eLife 12:e78089.
https://doi.org/10.7554/eLife.78089

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https://doi.org/10.7554/eLife.78089

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