Modeling COVID-19 Disease with Deterministic and Data-Driven Models Using Daily Empirical Data in the United Kingdom

Janet O. Agbaje, Oluwatosin Babasola, Kabiru Michael Adeyemo, Abraham Baba Zhiri, Aanuoluwapo Joshua Adigun, Samuel Adefisoye Lawal, Oluwole Adegoke Nuga, Roseline Toyin Abah, Umar Muhammad Adam, Kayode Oshinubi

Research output: Contribution to journalArticlepeer-review


The COVID-19 pandemic has had a significant impact on countries worldwide, including the United Kingdom (UK). The UK has faced numerous challenges, but its response, including the rapid vaccination campaign, has been noteworthy. While progress has been made, the study of the pandemic is important to enable us to properly prepare for future epidemics. Collaboration, vigilance, and continued adherence to public health measures will be crucial in navigating the path to recovery and building resilience for the future. In this article, we propose an overview of the COVID-19 situation in the UK using both mathematical (a nonlinear differential equation model) and statistical (time series modeling on a moving window) models on the transmission dynamics of the COVID-19 virus from the beginning of the pandemic up until July 2022. This is achieved by integrating a hybrid model and daily empirical case and death data from the UK. We partition this dataset into before and after vaccination started in the UK to understand the influence of vaccination on disease dynamics. We used the mathematical model to present some mathematical analyses and the calculation of the basic reproduction number ((Formula presented.)). Following the sensitivity analysis index, we deduce that an increase in the rate of vaccination will decrease (Formula presented.). Also, the model was fitted to the data from the UK to validate the mathematical model with real data, and we used the data to calculate time-varying (Formula presented.). The homotopy perturbation method (HPM) was used for the numerical simulation to demonstrate the dynamics of the disease with varying parameters and the importance of vaccination. Furthermore, we used statistical modeling to validate our model by performing principal component analysis (PCA) to predict the evolution of the spread of the COVID-19 outbreak in the UK on some statistical predictor indicators from time series modeling on a 14-day moving window for detecting which of these indicators capture the dynamics of the disease spread across the epidemic curve. The results of the PCA, the index of dispersion, the fitted mathematical model, and the mathematical model simulation are all in agreement with the dynamics of the disease in the UK before and after vaccination started. Conclusively, our approach has been able to capture the dynamics of the pandemic at different phases of the disease outbreak, and the result presented will be useful to understand the evolution of the disease in the UK and future and emerging epidemics.

Original languageEnglish
Pages (from-to)289-316
Number of pages28
Issue number2
Early online date18 Feb 2024
Publication statusPublished - 29 Feb 2024

Data Availability Statement

The data used for this research are available in public databases.


  • data fitting
  • homotopy perturbation method (HPM)
  • index of dispersion
  • numerical simulation
  • principal component analysis (PCA)
  • SARS-CoV-2
  • sensitivity index
  • statistical modeling

ASJC Scopus subject areas

  • Immunology and Microbiology (miscellaneous)
  • Medicine (miscellaneous)
  • Infectious Diseases

Cite this