Application and significance of SIRVB model in analyzing COVID-19 dynamics.

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Tác giả: Pavithra Ariyaratne, Jonathan S Ayyash, Jixin Chen, Tyler M Kelley, Terry A Plant-Collins, Lumbini P Ramasinghe, Logan W Shinkle, Aoife M Zuercher

Ngôn ngữ: eng

Ký hiệu phân loại: 949.59012 *Greece

Thông tin xuất bản: England : Scientific reports , 2025

Mô tả vật lý:

Bộ sưu tập: NCBI

ID: 704056

In the summer of 2024, COVID-19 positive cases spiked in many countries, but it is no longer a deadly pandemic thanks to global herd immunity to the SARS-CoV-2 viruses. In our physical chemistry lab in spring 2024, students practice kinetic models, SIR (Susceptible, Infected, and Recovered) and SIRV (Susceptible, Infected, Recovered, Vaccinated) using COVID-19 positive cases and vaccination data from World Health Organization (WHO). In this report, we further introduce virus breakthrough to the existing model updating it the SIRVB (Susceptible, Infectious, Recovered, Vaccinated, Breakthrough) model. We believe this is the simplest model possible to explain the COVID-19 kinetics/dynamics in all countries in the past four years. Parameters obtained from such practice correlate with many indices of different countries. These models and parameters have significant value to researchers and policymakers in predicting the stages of future outbreaks of infectious diseases.
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