Journal Article

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

Yamana, Teresa K., Harris, Rebecca, Feder, Andries, Coffin, Susan, Rajagopal, Swetha, Pei, Sen, Mell, Joshua Chang, Bianco, Colleen, Moustafa, Ahmed M., Hall, Donald C., Shaman, Jeffrey, Campbell, Amy E., Planet, Paul J., Ahmed, Azad

2024

Overview

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.

• Publisher: BioMed Central,BioMed Central Ltd,Springer Nature B.V,BMC
• Subject: Analysis / Communicable diseases / COVID-19 / COVID-19 - epidemiology / COVID-19 - transmission / COVID-19 - virology / Data analysis / Data assimilation / Disease control / Disease transmission / Epidemiology / Gene sequencing / Genomes / Genomic analysis / Genomics / Health aspects / Humans / Identification methods / Impact analysis / Infections / Infectious disease modelling / Infectious Diseases / Inference / Internal Medicine / Kalman filters / Local population / Medical Microbiology / Medicine / Medicine & Public Health / Metapopulations / Pandemics / Parameter estimation / Parameter identification / Parasitology / Philadelphia - epidemiology / Public health / Real time / Risk factors / SARS-COV-2 / SARS-CoV-2 - classification / SARS-CoV-2 - genetics / Severe acute respiratory syndrome coronavirus 2 / Strain analysis / Tropical Medicine / Viral diseases / Virulence / Virus research / Whole genome sequencing