tetano
Editor, Senior Moderator
Sci Rep
. 2025 Mar 12;15(1):8526.
doi: 10.1038/s41598-025-90260-4. Application and significance of SIRVB model in analyzing COVID-19 dynamics
Pavithra Ariyaratne[SUP] 1 [/SUP], Lumbini P Ramasinghe[SUP] 1 [/SUP], Jonathan S Ayyash[SUP] 1 [/SUP], Tyler M Kelley[SUP] 1 [/SUP], Terry A Plant-Collins[SUP] 1 [/SUP], Logan W Shinkle[SUP] 1 [/SUP], Aoife M Zuercher[SUP] 1 [/SUP], Jixin Chen[SUP] 2 [/SUP]
Affiliations
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.
Keywords: COVID kinetic models; Global data analysis; SIR; SIRV; SIRVB; Undergraduate teaching.
. 2025 Mar 12;15(1):8526.
doi: 10.1038/s41598-025-90260-4. Application and significance of SIRVB model in analyzing COVID-19 dynamics
Pavithra Ariyaratne[SUP] 1 [/SUP], Lumbini P Ramasinghe[SUP] 1 [/SUP], Jonathan S Ayyash[SUP] 1 [/SUP], Tyler M Kelley[SUP] 1 [/SUP], Terry A Plant-Collins[SUP] 1 [/SUP], Logan W Shinkle[SUP] 1 [/SUP], Aoife M Zuercher[SUP] 1 [/SUP], Jixin Chen[SUP] 2 [/SUP]
Affiliations
- PMID: 40075115
- DOI: 10.1038/s41598-025-90260-4
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.
Keywords: COVID kinetic models; Global data analysis; SIR; SIRV; SIRVB; Undergraduate teaching.