Article,
SARS-CoV-2 Delta and Omicron community transmission networks as added value to contact tracing
Affiliations
- [1] UNSW Sydney, Sch Math & Stat, Sydney, NSW 2052, Australia [NORA names: Australia; Oceania; OECD];
- [2] Univ Copenhagen, Rigshosp, Ctr Excellence Hlth Immun & Infect CHIP, Copenhagen, Denmark [NORA names: Capital Region of Denmark; Hospital; Denmark; Europe, EU; Nordic; OECD];
- [3] Univ Copenhagen, Rigshosp, Ctr Excellence Hlth Immun & Infect CHIP, Copenhagen, Denmark [NORA names: KU University of Copenhagen; University; Denmark; Europe, EU; Nordic; OECD];
- [4] Univ Hosp Nancy, Bacteriol Lab, F-54500 Vandoeuvre Les Nancy, France [NORA names: France; Europe, EU; OECD];
- [5] Lorraine Univ, CNRS, UMR 7564, Lab Chim Phys & Microbiol Mat & Environm LCPME, 405 Rue Vandoeuvre, F-54600 Villers Les Nancy, France [NORA names: France; Europe, EU; OECD];
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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. (c) 2024 The Author(s). Published by Elsevier Ltd on behalf of The British Infection Association. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).