Identifying key critical model parameters in spreading of COVID-19 pandemic

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.