Dynamical behaviours and stability analysis of a generalized fractional model with a real case study.

INTRODUCTION: Mathematical modelling is a rapidly expanding field that offers new and interesting opportunities for both mathematicians and biologists. Concerning COVID-19, this powerful tool may help humans to prevent the spread of this disease, which has affected the livelihood of all people badly. OBJECTIVES: The main objective of this research is to explore an efficient mathematical model for the investigation of COVID-19 dynamics in a generalized fractional framework. METHODS: The new model in this paper is formulated in the Caputo sense, employs a nonlinear time-varying transmission rate, and consists of ten population classes including susceptible, infected, diagnosed, ailing, recognized, infected real, threatened, diagnosed recovered, healed, and extinct people. The existence of a unique solution is explored for the new model, and the associated dynamical behaviours are discussed in terms of equilibrium points, invariant region, local and global stability, and basic reproduction number. To implement the proposed model numerically, an efficient approximation scheme is employed by the combination of Laplace transform and a successive substitution approach; besides, the corresponding convergence analysis is also investigated. RESULTS: Numerical simulations are reported for various fractional orders, and simulation results are compared with a real case of COVID-19 pandemic in Italy. By using these comparisons between the simulated and measured data, we find the best value of the fractional order with minimum absolute and relative errors. Also, the impact of different parameters on the spread of viral infection is analyzed and studied. CONCLUSION: According to the comparative results with real data, we justify the use of fractional concepts in the mathematical modelling, for the new non-integer formalism simulates the reality more precisely than the classical framework.

Comparative Analysis of Mathematical Model of Covid-Sars Using Atangana-Baleanu and Yang-Abdel-Cattani Fractional Derivative Operators

Today's world is suffering from a disease known as the Corona Virus (COVID-19). Since this virus has turned into a pandemic at a global level, it is required to investigate the virus and its related attributes to anticipate future outbreaks and also to make strategies for its control through mathematical models. In this article, we perform a comparative analysis of the model using the Atangana-Baleanu and Yang-Abdel-Cattani fractional derivative operators with the help of Sumudu transform. We also compute the numerical results with graphical representation to show the behavior of the operators.

On the existence and stability of fuzzy CF variable fractional differential equation for COVID-19 epidemic.

In this paper, we convert the recent COVID-19 model with the use of the most influential theories, such as variable fractional calculus and fuzzy theory. We propose the fuzzy variable fractional differential equation for the COVID-19 model in which the variable fractional-order derivative is described using the Caputo-Fabrizio in the Caputo sense. Furthermore, we provide the results on the existence and uniqueness using Lipschitz conditions. Also, discuss the stability analysis of the present new COVID-19 model by employing Hyers-Ulam stability.

On a reaction–diffusion system modelling infectious diseases without lifetime immunity

In this paper, we study a mathematical model for an infectious disease caused by a virus such as Cholera without lifetime immunity. Due to the different mobility for susceptible, infected human and recovered human hosts, the diffusion coefficients are assumed to be different. The resulting system is governed by a strongly coupled reaction–diffusion system with different diffusion coefficients. Global existence and uniqueness are established under certain assumptions on known data. Moreover, global asymptotic behaviour of the solution is obtained when some parameters satisfy certain conditions. These results extend the existing results in the literature. The main tool used in this paper comes from the delicate theory of elliptic and parabolic equations. Moreover, the energy method and Sobolev embedding are used in deriving a priori estimates. The analysis developed in this paper can be employed to study other epidemic models in biological, ecological and health sciences.

Design and Analysis of a New COVID-19 Model with Comparative Study of Control Strategies

The COVID-19 pandemic has become a worldwide concern and has caused great frustration in the human community. Governments all over the world are struggling to combat the disease. In an effort to understand and address the situation, we conduct a thorough study of a COVID-19 model that provides insights into the dynamics of the disease. For this, we propose a new L S H S E A I H R COVID-19 model, where susceptible populations are divided into two sub-classes: low-risk susceptible populations, L S , and high-risk susceptible populations, H S . The aim of the subdivision of susceptible populations is to construct a model that is more reliable and realistic for disease control. We first prove the existence of a unique solution to the purposed model with the help of fundamental theorems of functional analysis and show that the solution lies in an invariant region. We compute the basic reproduction number and describe constraints that ensure the local and global asymptotic stability at equilibrium points. A sensitivity analysis is also carried out to identify the model's most influential parameters. Next, as a disease transmission control technique, a class of isolation is added to the intended L S H S E A I H R model. We suggest simple fixed controls through the adjustment of quarantine rates as a first control technique. To reduce the spread of COVID-19 as well as to minimize the cost functional, we constitute an optimal control problem and develop necessary conditions using Pontryagin's maximum principle. Finally, numerical simulations with and without controls are presented to demonstrate the efficiency and efficacy of the optimal control approach. The optimal control approach is also compared with an approach where the state model is solved numerically with different time-independent controls. The numerical results, which exhibit dynamical behavior of the COVID-19 system under the influence of various parameters, suggest that the implemented strategies, particularly the quarantine of infectious individuals, are effective in significantly reducing the number of infected individuals and achieving herd immunity. [ FROM AUTHOR] Copyright of Mathematics (2227-7390) is the property of MDPI and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

A numerical study of COVID-19 epidemic model with vaccination and diffusion.

The coronavirus infectious disease (or COVID-19) is a severe respiratory illness. Although the infection incidence decreased significantly, still it remains a major panic for human health and the global economy. The spatial movement of the population from one region to another remains one of the major causes of the spread of the infection. In the literature, most of the COVID-19 models have been constructed with only temporal effects. In this paper, a vaccinated spatio-temporal COVID-19 mathematical model is developed to study the impact of vaccines and other interventions on the disease dynamics in a spatially heterogeneous environment. Initially, some of the basic mathematical properties including existence, uniqueness, positivity, and boundedness of the diffusive vaccinated models are analyzed. The model equilibria and the basic reproductive number are presented. Further, based upon the uniform and non-uniform initial conditions, the spatio-temporal COVID-19 mathematical model is solved numerically using finite difference operator-splitting scheme. Furthermore, detailed simulation results are presented in order to visualize the impact of vaccination and other model key parameters with and without diffusion on the pandemic incidence. The obtained results reveal that the suggested intervention with diffusion has a significant impact on the disease dynamics and its control.

On the Modeling of COVID-19 Spread via Fractional Derivative: A Stochastic Approach

: The coronavirus disease (COVID-19) pandemic has caused more harm than expected in developed and developing countries. In this work, a fractional stochastic model of COVID-19 which takes into account the random nature of the spread of disease, is formulated and analyzed. The existence and uniqueness of solutions were established using the fixed-point theory. Two different fractional operators', namely, power-law and Mittag–Leffler function, numerical schemes in the stochastic form, are utilized to obtain numerical simulations to support the theoretical results. It is observed that the fractional order derivative has effect on the dynamics of the spread of the disease. © 2023, Pleiades Publishing, Ltd.

Dynamics and application of a generalized SIQR epidemic model with vaccination and treatment

In this paper, we propose and investigate the SIQR epidemic model with a generalized incidence rate function, a general treatment function and vaccination term. We firstly consider the existence and uniqueness of the global nonnegative solution to the deterministic model. Further, we show the locally asymptotic stability of the disease-free equilibrium and endemic equilibrium of the deterministic model, and obtain the basic reproduction number R0. Then we study the existence and uniqueness of the global positive solution to the stochastic model with any positive initial value. Meanwhile, we obtain sufficient conditions for the extinction of the disease in the stochastic epidemic model, and find that the large noise can make the disease die out exponentially. Finally, we make an empirical analysis by the COVID-19 data of Russia and Serbia. By the performance comparison of different models, it shows that the model with vaccination and treatment we proposed is better for the real situation, which is also verified by different estimation methods. Especially, that shows the recovery rate of the infected increases by 0.042 and the death rate of the recovered is 1.525 times that of normal human in Russia. Through statistical analysis, the short-term trend of epidemic transmission is predicted: under the condition of unchanged prevention and control policies, it may reach a stable endemic equilibrium state in Russia and the epidemic will eventually extinct in Serbia. © 2023 Elsevier Inc.

Fractional order mathematical modeling of novel corona virus (COVID-19).

In this manuscript, the mathematical model of COVID-19 is considered with eight different classes under the fractional-order derivative in Caputo sense. A couple of results regarding the existence and uniqueness of the solution for the proposed model is presented. Furthermore, the fractional-order Taylor's method is used for the approximation of the solution of the concerned problem. Finally, we simulate the results for 50 days with the help of some available data for fractional differential order to display the excellency of the proposed model.

Theoretical Analysis of a COVID-19 CF-Fractional Model to Optimally Control the Spread of Pandemic

In this manuscript, we formulate a mathematical model of the deadly COVID-19 pandemic to understand the dynamic behavior of COVID-19. For the dynamic study, a new SEIAPHR fractional model was purposed in which infectious individuals were divided into three sub-compartments. The purpose is to construct a more reliable and realistic model for a complete mathematical and computational analysis and design of different control strategies for the proposed Caputo–Fabrizio fractional model. We prove the existence and uniqueness of solutions by employing well-known theorems of fractional calculus and functional analyses. The positivity and boundedness of the solutions are proved using the fractional-order properties of the Laplace transformation. The basic reproduction number for the model is computed using a next-generation technique to handle the future dynamics of the pandemic. The local–global stability of the model was also investigated at each equilibrium point. We propose basic fixed controls through manipulation of quarantine rates and formulate an optimal control problem to find the best controls (quarantine rates) employed on infected, asymptomatic, and "superspreader” humans, respectively, to restrict the spread of the disease. For the numerical solution of the fractional model, a computationally efficient Adams–Bashforth method is presented. A fractional-order optimal control problem and the associated optimality conditions of Pontryagin maximum principle are discussed in order to optimally reduce the number of infected, asymptomatic, and superspreader humans. The obtained numerical results are discussed and shown through graphs. © 2023 by the authors.

A New Incommensurate Fractional-Order COVID 19: Modelling and Dynamical Analysis

Nowadays, a lot of research papers are concentrating on the diffusion dynamics of infectious diseases, especially the most recent one: COVID-19. The primary goal of this work is to explore the stability analysis of a new version of the (Formula presented.) model formulated with incommensurate fractional-order derivatives. In particular, several existence and uniqueness results of the solution of the proposed model are derived by means of the Picard–Lindelöf method. Several stability analysis results related to the disease-free equilibrium of the model are reported in light of computing the so-called basic reproduction number, as well as in view of utilising a certain Lyapunov function. In conclusion, various numerical simulations are performed to confirm the theoretical findings. © 2023 by the authors.

Stability analysis of COVID-19 outbreak using Caputo-Fabrizio fractional differential equation

The main aim of this paper is to construct a mathematical model for the spread of SARS-CoV-2 infection. We discuss the modified COVID-19 and change the model to fractional order form based on the Caputo-Fabrizio derivative. Also several definitions and theorems of fractional calculus, fuzzy theory and Laplace transform are illustrated. The existence and uniqueness of the solution of the model are proved based on the Banach's unique fixed point theory. Moreover Hyers-Ulam stability analysis is studied. The obtained results show the efficiency and accuracy of the model. © 2023 the Author(s), licensee AIMS Press.

Pandemic portfolio choice

COVID-19 has taught us that a pandemic can significantly increase biometric risk and at the same time trigger crashes of the stock market. Taking these potential co-movements of financial and non-financial risks into account, we study the portfolio problem of an agent who is aware that a future pandemic can affect her health and personal finances. The corresponding stochastic dynamic optimization problem is complex: It is characterized by a system of Hamilton-Jacobi-Bellman equations which are coupled with optimality conditions that are only given implicitly. We prove that the agent's value function and optimal policies are determined by the unique global solution to a system of non-linear ordinary differential equations. We show that the optimal portfolio strategy is significantly affected by the mere threat of a potential pandemic.

OPTIMAL CONTROL STRATEGIES OF CELL INFECTIONS IN A COVID-19 MODEL WITH INFLAMMATORY RESPONSE

In this work, we investigate the optimal strategies to limit cell infections and virus production at low cost in a mathematical virus model for Covid-19 with inflammatory response. We consider treatment and introduce two intervention control parameters: u1 for the drug therapy efficacy in preventing new infections, and u2 for the drug therapy efficacy in inhibiting viral production (with 0⩽u1,u2⩽1). We fully analyse the optimal control problem and establish the existence and uniqueness of the optimal solution. Moreover, we show that the optimal control is not only measurable, but is also continuous. Our numerical illustrations show how therapy can overturn a lethal infection state into a survivable stage, and vice versa.

A fractional order model of the COVID-19 outbreak in Bangladesh.

In this study, we propose a Caputo-based fractional compartmental model for the dynamics of the novel COVID-19. The dynamical attitude and numerical simulations of the proposed fractional model are observed. We find the basic reproduction number using the next-generation matrix. The existence and uniqueness of the solutions of the model are investigated. Furthermore, we analyze the stability of the model in the context of Ulam-Hyers stability criteria. The effective numerical scheme called the fractional Euler method has been employed to analyze the approximate solution and dynamical behavior of the model under consideration. Finally, numerical simulations show that we obtain an effective combination of theoretical and numerical results. The numerical results indicate that the infected curve predicted by this model is in good agreement with the real data of COVID-19 cases.

Optimal control strategies for the reliable and competitive mathematical analysis of Covid-19 pandemic model.

To understand dynamics of the COVID-19 disease realistically, a new SEIAPHR model has been proposed in this article where the infectious individuals have been categorized as symptomatic, asymptomatic, and super-spreaders. The model has been investigated for existence of a unique solution. To measure the contagiousness of COVID-19, reproduction number R 0 is also computed using next generation matrix method. It is shown that the model is locally stable at disease-free equilibrium point when R 0 < 1 and unstable for R 0 > 1 . The model has been analyzed for global stability at both of the disease-free and endemic equilibrium points. Sensitivity analysis is also included to examine the effect of parameters of the model on reproduction number R 0 . A couple of optimal control problems have been designed to study the effect of control strategies for disease control and eradication from the society. Numerical results show that the adopted control approaches are much effective in reducing new infections.

A Mathematical Model of the Ongoing Coronavirus Disease (COVID-19) Pandemic: a Case Study in Turkey

Turkey reported the first case of COVID-19 on 11 March 2020 since the outbreak of the deadly coronavirus pandemic. COVID-19 spread rapidly in Turkey, where about a total of 3,208,173 cases of infected persons were registered by 29 March 2021 with 2,957,093 cases of recovered persons and 31,076 reported deaths. A new mathematical COVID-19 model containing six classes is presented. Also, the positive invariant region of the solutions, basic reproductive number, disease-free equilibrium, and its stability are highlighted. Afterward, the disease-free equilibrium is locally asymptotically stable when R0 < 1. Moreover, the proposed model was further generalized to the fractional-order derivative in the AtanganaBaleanu (ABC) context for a more successful realization. Besides, the existence and uniqueness of solutions via techniques of Schaefer's and Banach fixed point theorems were established. Based on the publicly recorded number of infected people from 1-31 July 2020 in Turkey and least-squares curve fitting techniques with fminsearch function the fractionalorders model has been validated and can better fit the data compared with the integer-order model. Also, using the Atangana-Toufik scheme, numerical solutions, as well as simulations, are presented for different values of fractional order. © 2022, Thammasat University. All rights reserved.

A mathematical system of COVID-19 disease model: Existence, uniqueness, numerical and sensitivity analysis.

A compartmental mathematical model of spreading COVID-19 disease in Wuhan, China is applied to investigate the pandemic behaviour in Iran. This model is a system of seven ordinary differential equations including individual behavioural reactions, governmental actions, holiday extensions, travel restrictions, hospitalizations, and quarantine. We fit the Chinese model to the Covid-19 outbreak in Iran and estimate the values of parameters by trial-error approach. We use the Adams-Bashforth predictor-corrector method based on Lagrange polynomials to solve the system of ordinary differential equations. To prove the existence and uniqueness of solutions of the model we use Banach fixed point theorem and Picard iterative method. Also, we evaluate the equilibrium points and the stability of the system. With estimating the basic reproduction number R 0 , we assess the trend of new infected cases in Iran. In addition, the sensitivity analysis of the model is assessed by allocating different parameters to the system. Numerical simulations are depicted by adopting initial conditions and various values of some parameters of the system.

Dynamics of a fractional order mathematical model for COVID-19 epidemic transmission.

To achieve the aim of immediately halting spread of COVID-19 it is essential to know the dynamic behavior of the virus of intensive level of replication. Simply analyzing experimental data to learn about this disease consumes a lot of effort and cost. Mathematical models may be able to assist in this regard. Through integrating the mathematical frameworks with the accessible disease data it will be useful and outlay to comprehend the primary components involved in the spreading of COVID-19. There are so many techniques to formulate the impact of disease on the population mathematically, including deterministic modeling, stochastic modeling or fractional order modeling etc. Fractional derivative modeling is one of the essential techniques for analyzing real-world issues and making accurate assessments of situations. In this paper, a fractional order epidemic model that represents the transmission of COVID-19 using seven compartments of population susceptible, exposed, infective, recovered, the quarantine population, recovered-exposed, and dead population is provided. The fractional order derivative is considered in the Caputo sense. In order to determine the epidemic forecast and persistence, we calculate the reproduction number R 0 . Applying fixed point theory, the existence and uniqueness of the solutions of fractional order derivative have been studied . Moreover, we implement the generalized Adams-Bashforth-Moulton method to get an approximate solution of the fractional-order COVID-19 model. Finally, numerical result and an outstanding graphic simulation are presented.

NUMERICAL STUDY FOR FRACTIONAL BI-MODAL 2019-nCOV SITR EPIDEMIC MODEL

Currently, the entire planet is suffering from a contagious epidemic infection, 2019-nCOV due to newly detected coronavirus. This is a lethal infectious virus that has destroyed thousands of lives all over the world. The important aim of this study is to investigate a susceptible-infected-treatment-recovered (SITR) model of coronavirus (2019-nCOV) with bi-modal virus spread in a susceptible population. The considered 2019-nCOV model is analyzed by two fractional derivatives: the Caputo and Atangana-Baleanu-Caputo (ABC). For the Caputo model, we present a few basic mathematical characteristics such as existence, positivity, boundedness and stability result for disease-free equilibria. The fixed-point principle is used to establish the existence and uniqueness conditions for the ABC model solution. We employed the Adams-Bashforth-Moulton (ABM) numerical technique for the Caputo model solution and the Toufik-Atangana (TA) numerical approach for the ABC model solution. Finally, using MATLAB, the simulation results are shown to highlight the impact of arbitrarily chosen fractional-order and model parameters on infection dynamics. © 2022 The Author(s).