A modelling approach to estimate the transmissibility of SARS-CoV-2 during periods of high, low, and zero case incidence
- Curtin University, Australia
- University of Melbourne, Australia
- 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
Article and author information
Author details
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
-
- 1,146
- views
-
- 181
- downloads
-
- 19
- 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)
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
Further reading
-
- Epidemiology and Global Health
- Microbiology and Infectious Disease
Background:
In many settings, a large fraction of the population has both been vaccinated against and infected by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Hence, quantifying the protection provided by post-infection vaccination has become critical for policy. We aimed to estimate the protective effect against SARS-CoV-2 reinfection of an additional vaccine dose after an initial Omicron variant infection.
Methods:
We report a retrospective, population-based cohort study performed in Shanghai, China, using electronic databases with information on SARS-CoV-2 infections and vaccination history. We compared reinfection incidence by post-infection vaccination status in individuals initially infected during the April–May 2022 Omicron variant surge in Shanghai and who had been vaccinated before that period. Cox models were fit to estimate adjusted hazard ratios (aHRs).
Results:
275,896 individuals were diagnosed with real-time polymerase chain reaction-confirmed SARS-CoV-2 infection in April–May 2022; 199,312/275,896 were included in analyses on the effect of a post-infection vaccine dose. Post-infection vaccination provided protection against reinfection (aHR 0.82; 95% confidence interval 0.79–0.85). For patients who had received one, two, or three vaccine doses before their first infection, hazard ratios for the post-infection vaccination effect were 0.84 (0.76–0.93), 0.87 (0.83–0.90), and 0.96 (0.74–1.23), respectively. Post-infection vaccination within 30 and 90 days before the second Omicron wave provided different degrees of protection (in aHR): 0.51 (0.44–0.58) and 0.67 (0.61–0.74), respectively. Moreover, for all vaccine types, but to different extents, a post-infection dose given to individuals who were fully vaccinated before first infection was protective.
Conclusions:
In previously vaccinated and infected individuals, an additional vaccine dose provided protection against Omicron variant reinfection. These observations will inform future policy decisions on COVID-19 vaccination in China and other countries.
Funding:
This study was funded the Key Discipline Program of Pudong New Area Health System (PWZxk2022-25), the Development and Application of Intelligent Epidemic Surveillance and AI Analysis System (21002411400), the Shanghai Public Health System Construction (GWVI-11.2-XD08), the Shanghai Health Commission Key Disciplines (GWVI-11.1-02), the Shanghai Health Commission Clinical Research Program (20214Y0020), the Shanghai Natural Science Foundation (22ZR1414600), and the Shanghai Young Health Talents Program (2022YQ076).
-
- Epidemiology and Global Health
Background:
The role of circulating metabolites on child development is understudied. We investigated associations between children’s serum metabolome and early childhood development (ECD).
Methods:
Untargeted metabolomics was performed on serum samples of 5004 children aged 6–59 months, a subset of participants from the Brazilian National Survey on Child Nutrition (ENANI-2019). ECD was assessed using the Survey of Well-being of Young Children’s milestones questionnaire. The graded response model was used to estimate developmental age. Developmental quotient (DQ) was calculated as the developmental age divided by chronological age. Partial least square regression selected metabolites with a variable importance projection ≥1. The interaction between significant metabolites and the child’s age was tested.
Results:
Twenty-eight top-ranked metabolites were included in linear regression models adjusted for the child’s nutritional status, diet quality, and infant age. Cresol sulfate (β=–0.07; adjusted-p <0.001), hippuric acid (β=–0.06; adjusted-p <0.001), phenylacetylglutamine (β=–0.06; adjusted-p <0.001), and trimethylamine-N-oxide (β=–0.05; adjusted-p=0.002) showed inverse associations with DQ. We observed opposite directions in the association of DQ for creatinine (for children aged –1 SD: β=–0.05; pP=0.01;+1 SD: β=0.05; p=0.02) and methylhistidine (–1 SD: β = - 0.04; p=0.04;+1 SD: β=0.04; p=0.03).
Conclusions:
Serum biomarkers, including dietary and microbial-derived metabolites involved in the gut-brain axis, may potentially be used to track children at risk for developmental delays.
Funding:
Supported by the Brazilian Ministry of Health and the Brazilian National Research Council.
