open access publication

Article, 2024

SARS-CoV-2 Delta and Omicron community transmission networks as added value to contact tracing

Journal of Infection, ISSN 0163-4453, 1532-2742, Volume 88, 2, Pages 173-179, 10.1016/j.jinf.2024.01.004

Contributors

Murray, John Michael 0000-0001-9314-2283 (Corresponding author) [1] Murray, Daniel D [2] [3] Schvoerer, Evelyne 0000-0003-0290-4532 [4] [5] Akand, Elma H 0000-0002-1710-3693 [1]

Affiliations

  1. [1] UNSW Sydney
    2. [NORA names: Australia; Oceania; OECD];
  2. [2] Rigshospitalet
    3. [NORA names: Capital Region of Denmark; Hospital; Denmark; Europe, EU; Nordic; OECD];
  3. [3] University of Copenhagen
    4. [NORA names: KU University of Copenhagen; University; Denmark; Europe, EU; Nordic; OECD];
  4. [4] Centre Hospitalier Universitaire de Nancy
    5. [NORA names: France; Europe, EU; OECD];
  5. [5] Laboratory of Virology, University Hospital of Nancy Brabois, F-54500 Vandoeuvre-les-Nancy, France; Lorraine University, Laboratory of Physical Chemistry and Microbiology for Materials and the Environment, LCPME UMR 7564, CNRS, 405 Rue de Vandoeuvre, F-54600 Villers-lès-Nancy, France.
    6. [NORA names: France; Europe, EU; OECD]

Abstract

OBJECTIVES: Calculations of SARS-CoV-2 transmission networks at a population level have been limited. Networks that estimate infections between individuals and whether this results in a mutation, can be a way to evaluate fitness of a mutational clone by how much it expands in number as well as determining the likelihood a transmission results in a new variant. METHODS: Australian Delta and Omicron SARS-CoV-2 sequences were downloaded from GISAID. Transmission networks of infection between individuals were estimated using a novel mathematical method. RESULTS: Many of the sequences were identical, with clone sizes following power law distributions driven by negative binomial probability distributions for both the number of infections per individual and the number of mutations per transmission (median 0.74 nucleotide changes for Delta and 0.71 for Omicron). Using these distributions, an agent-based model was able to replicate the observed clonal network structure, providing a basis for more detailed COVID-19 modelling. Possible recombination events, tracked by insertion/deletion (indel) patterns, were identified for each variant in these outbreaks. CONCLUSIONS: This modelling approach reveals key transmission characteristics of SARS-CoV-2 and may complement traditional contact tracing. This methodology can also be applied to other diseases as genetic sequencing of viruses becomes more commonplace.

Keywords

COVID-19, COVID-19 model, GISAID, Omicron, SARS-CoV-2, SARS-CoV-2 Delta, SARS-CoV-2 sequences, agent-based model, approach, binomial probability distribution, calculations, characteristics of SARS-CoV-2, clone size, clones, delta, disease, distribution, estimate infection, evaluate fitness, events, fitness, genetic sequences, genetic sequences of viruses, individuals, infection, insertion/deletion, law distribution, levels, likelihood, mathematical methods, method, methodology, model, modeling approach, mutated clones, mutations, negative binomial probability distribution, network, network structure, outbreak, population, population level, power, power-law distribution, probability distribution, recombination, recombination events, results, sequence, sequences of viruses, size, structure, transmission, transmission characteristics, transmission characteristics of SARS-CoV-2, transmission network, variants, virus

Funders

  • Australian Research Council

Data Provider: Digital Science

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