ABSTRACT
The spreading of COVID-19 became a global issue that had a significant impact on health, life, and economic sectors. Efforts from all over the world are focused on discussing a variety of healthcare approaches to reduce the effect of COVID-19 among individuals. Mathematical tools with numerical simulations are important approaches that help international efforts to determine critical transmission factors as well as controlling the virus spread. In this paper, we develop a mathematical model that considers a vaccination compartment in terms of ordinary differential equations. This study focuses on the real data of confirmed cases in Kurdistan Region of Iraq from July 17th, 2021 to January 1st, 2022. Model results and real data for the total number of infected people were compared using computational tools in MATLAB. Additionally, non-normalization, half-normalization, and full-normalization methods are used to determine the local sensitivities between model variables and parameters. Interestingly, computational results show that the dynamics of model results and real confirmed cases are very close to each other. Accordingly, the elasticity coefficients provide a great understanding of the impact of vaccination on transmissions. The model results here can also help international efforts for further suggestions and improvements to control this disease more effectively. © Palestine Polytechnic University-PPU 2023.
ABSTRACT
Spreading COVID-19 pandemic has been considered as a global issue. Many international efforts including mathematical approaches have been recently discussed to control this disease more effectively. In this study, we have developed our previous SIUWR model and some transmission parameters are added. Accordingly, the basic reproduction number and elasticity coefficients are calculated at the equilibrium points. Then, some key critical model parameters are identified based on local sensitivities. In addition, the validation of the suggested model is checked by comparing some collected real data in Iraq and France from January 1st, 2021 to December 25th, 2021. Interestingly, there are good agreements between the model results and the real confirmed data using computational simulations in MATLAB. Results provide some biological interpretations and they can be used to control this pandemic more effectively. The model results will be used for both countries in minimizing the impact of this virus on their communities.
ABSTRACT
The spreading of COVID-19 has been considered a worldwide issue, and many global efforts have been suggested. Suggested control strategies to minimize the impact of the disease have effectively worked with computational simulations and mathematical models. Model critical transmissions and sensitivities are also key elements to study this pandemic more widely. This work reviews and discusses susceptible-exposed-infected-recovered (SEIR) model to predict the spreading of this disease. Accordingly, the basic reproduction number and its parameter elasticity are considered at the equilibrium points. Furthermore, the real data of confirmed cases in the Kurdistan region of Iraq are used in estimating model parameters and model validating. Computational model results provide some important model improvements and suggest control strategies. Firstly, the model population states have different model dynamics using the estimated parameters and the initial values. Another result is that almost all model states are sensitive to the model parameters at different levels. Interestingly, contact rate, transition rate from exposed class to the infected class and natural recovery rate are the most important controllable parameters to reduce the basic reproduction number R o , and they become the model critical parameters. More interestingly, computational results for the real data provide that the basic reproduction number in the Kurdistan Region was about 1.28, which is greater than unity. This means that the new coronavirus still has a high potential to spread among individuals, and it will require more interventions and new strategies to control this disease further.