Mathematical modelling of unemployment as the effect of COVID-19 pandemic in middle-income countries

Abstract

WHO (World Health Organization) has stamped Coronavirus disease 2019 (COVID-19) as a pandemic. This disease has affected most of the population in every aspect. One of those main aspects is the economical burden on the middle class and poor people. To overcome these issues, there should be multi-stage precautions, and awareness was required apart from these, there should be a multi-level posterior effort on facing the challenges that were much needed. In that way, to join the dealing of facing the posterior challenges of this disease, we try to help the society in our way of modelling approach, so that we developed a mathematical model to identify the complexities of the virus and its actual phenomena along with its economical oriented effects and perspectives. During the time of the COVID-19 pandemic situation, millions of employees lost their jobs irrespective of age consideration. More youngsters in many countries have been looking for a new jobs even after facing so much of struggles as the effect of this pandemic. In these facts, a system of ordinary differential equations (ODEs) model is constructed and analysed the outbreak of COVID-19 and its effects on employment. The basic reproduction number R0 has determined using the next-generation matrix technique. If R0 ≤ 1 disease-free equilibrium is globally asymptotically stable. If R0 ≥ 1, the endemic equilibrium is global asymptotic stability is achieved. To show the analytic findings, numerical simulations were performed. We hope that this work is suitable to control unemployment for all kinds of income populations.

Poverty and the Effects of Drug Addiction in a Deterministic and Stochastic Model

“…Now p1>0, p3>0, p4 and p1p2p3> 𝑝 3 2 + 𝑝 1 2 𝑝 4 . Hence the equilibrium point E1 is locally asymptotically stable [17].…”

Dynamics of the COVID-19 pandemic: nonlinear approaches on the modelling, prediction and control

“…The results can be effective for understanding the uncertainty of a noise-induced epidemic model. A mathematical model of unemployment as a consequence of the COVID-19 pandemic in middle-income countries is developed by Chinnadurai et al [3], using population dynamics. The results show that the model is effective in controlling unemployment in all kinds of populations.…”

“…This special issue is a compilation of original research articles that address the dynamics and applications of COVID-19 through nonlinear dynamics. The articles are organized in five sections, comprising mathematical modeling and epidemics [1][2][3][4][5][6][7], the dynamics of several waves and transmission [8][9][10][11][12][13][14][15][16][17], neural network and deep learning related to COVID-19 [18][19][20][21][22][23][24], predictions and estimations related to COVID-19 [25][26][27][28][29][30], and detailed analysis on the pandemic and its applications [31][32][33][34][35].…”