Your browser doesn't support javascript.
loading
Vaccination compartmental epidemiological models for the delta and omicron SARS-CoV-2 variants.
Cuevas-Maraver, J; Kevrekidis, P G; Chen, Q Y; Kevrekidis, G A; Drossinos, Y.
Afiliação
  • Cuevas-Maraver J; Grupo de Física No Lineal, Departamento de Física Aplicada I, Universidad de Sevilla. Escuela Politécnica Superior, C/ Virgen de África, 7, 41011 Sevilla, Spain; Instituto de Matemáticas de la Universidad de Sevilla (IMUS), Edificio Celestino Mutis. Avda. Reina Mercedes s/n, 41012 Sevilla, Spain. El
  • Kevrekidis PG; Department of Mathematics and Statistics, University of Massachusetts Amherst, Amherst, MA 01003, USA.
  • Chen QY; Department of Mathematics and Statistics, University of Massachusetts Amherst, Amherst, MA 01003, USA.
  • Kevrekidis GA; Department of Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, MD 21218, USA; Los Alamos National Laboratory, Los Alamos, NM, USA; Mathematical Institute for Data Science, Johns Hopkins University, Baltimore MD, USA.
  • Drossinos Y; Thermal Hydraulics & Multiphase Flow Laboratory, Institute of Nuclear & Radiological Sciences and Technology, Energy & Safety, N.C.S.R. "Demokritos", GR 15341, Agia Paraskevi, Greece.
Math Biosci ; 367: 109109, 2024 Jan.
Article em En | MEDLINE | ID: mdl-37981262
We explore the inclusion of vaccination in compartmental epidemiological models concerning the delta and omicron variants of the SARS-CoV-2 virus that caused the COVID-19 pandemic. We expand on our earlier compartmental-model work by incorporating vaccinated populations. We present two classes of models that differ depending on the immunological properties of the variant. The first one is for the delta variant, where we do not follow the dynamics of the vaccinated individuals since infections of vaccinated individuals were rare. The second one for the far more contagious omicron variant incorporates the evolution of the infections within the vaccinated cohort. We explore comparisons with available data involving two possible classes of counts, fatalities and hospitalizations. We present our results for two regions, Andalusia and Switzerland (including the Principality of Liechtenstein), where the necessary data are available. In the majority of the considered cases, the models are found to yield good agreement with the data and have a reasonable predictive capability beyond their training window, rendering them potentially useful tools for the interpretation of the COVID-19 and further pandemic waves, and for the design of intervention strategies during these waves.
Assuntos
Palavras-chave

Texto completo: 1 Base de dados: MEDLINE Assunto principal: COVID-19 Idioma: En Ano de publicação: 2024 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: COVID-19 Idioma: En Ano de publicação: 2024 Tipo de documento: Article