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.

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.

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.

Metrics

  • 1,039
    views
  • 174
    downloads
  • 16
    citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Open citations (links to open the citations from this article in various online reference manager services)

Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)

  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

Share this article

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

Further reading

    1. Epidemiology and Global Health
    Jie Liang, Yang Pan ... Fanfan Zheng
    Research Article

    Background:

    The associations of age at diagnosis of breast cancer with incident myocardial infarction (MI) and heart failure (HF) remain unexamined. Addressing this problem could promote understanding of the cardiovascular impact of breast cancer.

    Methods:

    Data were obtained from the UK Biobank. Information on the diagnosis of breast cancer, MI, and HF was collected at baseline and follow-ups (median = 12.8 years). The propensity score matching method and Cox proportional hazards models were employed.

    Results:

    A total of 251,277 female participants (mean age: 56.8 ± 8.0 years), of whom 16,241 had breast cancer, were included. Among breast cancer participants, younger age at diagnosis (per 10-year decrease) was significantly associated with elevated risks of MI (hazard ratio [HR] = 1.36, 95% confidence interval [CI] 1.19–1.56, p<0.001) and HF (HR = 1.31, 95% CI 1.18–1.46, p<0.001). After propensity score matching, breast cancer patients with younger diagnosis age had significantly higher risks of MI and HF than controls without breast cancer.

    Conclusions:

    Younger age at diagnosis of breast cancer was associated with higher risks of incident MI and HF, underscoring the necessity to pay additional attention to the cardiovascular health of breast cancer patients diagnosed at younger age to conduct timely interventions to attenuate the subsequent risks of incident cardiovascular diseases.

    Funding:

    This study was supported by grants from the National Natural Science Foundation of China (82373665 and 81974490), the Nonprofit Central Research Institute Fund of Chinese Academy of Medical Sciences (2021-RC330-001), and the 2022 China Medical Board-open competition research grant (22-466).

    1. Epidemiology and Global Health
    2. Genetics and Genomics
    Wei Q Deng, Nathan Cawte ... Sonia S Anand
    Research Article

    Background:

    Maternal smoking has been linked to adverse health outcomes in newborns but the extent to which it impacts newborn health has not been quantified through an aggregated cord blood DNA methylation (DNAm) score. Here, we examine the feasibility of using cord blood DNAm scores leveraging large external studies as discovery samples to capture the epigenetic signature of maternal smoking and its influence on newborns in White European and South Asian populations.

    Methods:

    We first examined the association between individual CpGs and cigarette smoking during pregnancy, and smoking exposure in two White European birth cohorts (n=744). Leveraging established CpGs for maternal smoking, we constructed a cord blood epigenetic score of maternal smoking that was validated in one of the European-origin cohorts (n=347). This score was then tested for association with smoking status, secondary smoking exposure during pregnancy, and health outcomes in offspring measured after birth in an independent White European (n=397) and a South Asian birth cohort (n=504).

    Results:

    Several previously reported genes for maternal smoking were supported, with the strongest and most consistent association signal from the GFI1 gene (6 CpGs with p<5 × 10-5). The epigenetic maternal smoking score was strongly associated with smoking status during pregnancy (OR = 1.09 [1.07, 1.10], p=5.5 × 10-33) and more hours of self-reported smoking exposure per week (1.93 [1.27, 2.58], p=7.8 × 10-9) in White Europeans. However, it was not associated with self-reported exposure (p>0.05) among South Asians, likely due to a lack of smoking in this group. The same score was consistently associated with a smaller birth size (–0.37±0.12 cm, p=0.0023) in the South Asian cohort and a lower birth weight (–0.043±0.013 kg, p=0.0011) in the combined cohorts.

    Conclusions:

    This cord blood epigenetic score can help identify babies exposed to maternal smoking and assess its long-term impact on growth. Notably, these results indicate a consistent association between the DNAm signature of maternal smoking and a small body size and low birth weight in newborns, in both White European mothers who exhibited some amount of smoking and in South Asian mothers who themselves were not active smokers.

    Funding:

    This study was funded by the Canadian Institutes of Health Research Metabolomics Team Grant: MWG-146332.