A two-variant model of SARS-COV-2 transmission: estimating the characteristics of a newly emerging strain.

Autor: Yamana TK; Columbia University, New York, NY, USA. tky2104@cumc.columbia.edu., Rajagopal S; Children's Hospital of Philadelphia, Philadelphia, PA, USA., Hall DC Jr; Drexel University, Philadelphia, PA, USA., Moustafa AM; Children's Hospital of Philadelphia, Philadelphia, PA, USA.; Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA., Feder A; Children's Hospital of Philadelphia, Philadelphia, PA, USA., Ahmed A; Drexel University, Philadelphia, PA, USA., Bianco C; Children's Hospital of Philadelphia, Philadelphia, PA, USA., Harris R; Children's Hospital of Philadelphia, Philadelphia, PA, USA., Coffin S; Children's Hospital of Philadelphia, Philadelphia, PA, USA., Campbell AE; Children's Hospital of Philadelphia, Philadelphia, PA, USA., Pei S; Columbia University, New York, NY, USA., Mell JC; Drexel University, Philadelphia, PA, USA., Planet PJ; Children's Hospital of Philadelphia, Philadelphia, PA, USA. planetp@chop.edu.; Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA. planetp@chop.edu., Shaman J; Columbia University, New York, NY, USA.
Jazyk: angličtina
Zdroj: BMC infectious diseases [BMC Infect Dis] 2024 Sep 09; Vol. 24 (1), pp. 938. Date of Electronic Publication: 2024 Sep 09.
DOI: 10.1186/s12879-024-09823-x
Abstrakt: Background: The Covid-19 pandemic has been characterized by the emergence of novel SARS-CoV-2 variants, each with distinct properties influencing transmission dynamics, immune escape, and virulence, which, in turn, influence their impact on local populations. Swift analysis of the properties of newly emerged variants is essential in the initial days and weeks to enhance readiness and facilitate the scaling of clinical and public health system responses.
Methods: This paper introduces a two-variant metapopulation compartmental model of disease transmission to simulate the dynamics of disease transmission during a period of transition to a newly dominant strain. Leveraging novel S-gene dropout analysis data and genomic sequencing data, combined with confirmed Covid-19 case data, we estimate the epidemiological characteristics of the Omicron variant, which replaced the Delta variant in late 2021 in Philadelphia, PA. We utilized a grid-search method to identify plausible combinations of model parameters, followed by an ensemble adjustment Kalman filter for parameter inference.
Results: The model successfully estimated key epidemiological parameters; we estimated the ascertainment rate of 0.22 (95% credible interval 0.15-0.29) and transmission rate of 5.0 (95% CI 2.4-6.6) for the Omicron variant.
Conclusions: The study demonstrates the potential for this model-inference framework to provide real-time insights during the emergence of novel variants, aiding in timely public health responses.
(© 2024. The Author(s).)
Databáze: MEDLINE
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