Dynamical analysis of coronavirus disease with crowding effect, and vaccination: a study of third strain

Abstract

Countries affected by the coronavirus epidemic have reported many infected cases and deaths based on world health statistics. The crowding factor, which we named "crowding effects," plays a significant role in spreading the diseases. However, the introduction of vaccines marks a turning point in the rate of spread of coronavirus infections. Modeling both effects is vastly essential as it directly impacts the overall population of the studied region. To determine the peak of the infection curve by considering the third strain, we develop a mathematical model (susceptible–infected–vaccinated–recovered) with reported cases from August 01, 2021, till August 29, 2021. The nonlinear incidence rate with the inclusion of both effects is the best approach to analyze the dynamics. The model's positivity, boundedness, existence, uniqueness, and stability (local and global) are addressed with the help of a reproduction number. In addition, the strength number and second derivative Lyapunov analysis are examined, and the model was found to be asymptotically stable. The suggested parameters efficiently control the active cases of the third strain in Pakistan. It was shown that a systematic vaccination program regulates the infection rate. However, the crowding effect reduces the impact of vaccination. The present results show that the model can be applied to other countries' data to predict the infection rate.

Numerical Investigation of Malaria Disease Dynamics in Fuzzy Environment

“…At this stage, the resulting graphs of the NSFD method are not different from the previous cases. The classical standard difference schemes in the literature can cause chaos and misleading fluctuations for some passions of discretization constraints [34][35][36][37][38][39][40][41][42][43][44][45][46][47][48][49]. From all these results, we can conclude that the developed method can be considered an appropriate strategy to investigate the spread of malaria in the human population.…”

Numerical Analysis for the Effect of Irresponsible Immigrants on HIV/AIDS Dynamics

“…Iqbal [22,23]. The well-known methods and infectious diseases models are studied in [24][25][26][27][28][29][30][31][32][33][34][35]. The rest of the paper is designed: An HIV/AIDS model is considered in Section 2.…”

An Analytical Framework for Understanding Infection Progression Under Social Mitigation Measures

“…Progress has been made in various directions, e.g. by taking into account the effects of quarantine [1,2], temporary immunity [3], recurrent outbreaks [4], vaccination [5][6][7][8], different levels of population susceptibility [9], comorbidities [8,10], stratification by age [11][12][13], competitive virus strains of different severity and/or transmissibility [14,15], spatial diffusion [16], human mobility between different regions [17][18][19], availability of testing kits [20], hospital infrastructure [21] and media coverage [22,23] to name a few. In particular, the attempts to combat the disease led to the introduction of social distancing measures on the scales hardly imaginable before [24].…”