ABSTRACT
A deterministic model with testing of infected individuals has been proposed to investigate the potential consequences of the impact of testing strategy. The model exhibits global dynamics concerning the disease-free and a unique endemic equilibrium depending on the basic reproduction number when the recruitment of infected individuals is zero; otherwise, the model does not have a disease-free equilibrium, and disease never dies out in the community. Model parameters have been estimated using the maximum likelihood method with respect to the data of early COVID-19 outbreak in India. The practical identifiability analysis shows that the model parameters are estimated uniquely. The consequences of the testing rate for the weekly new cases of early COVID-19 data in India tell that if the testing rate is increased by 20% and 30% from its baseline value, the weekly new cases at the peak are decreased by 37.63% and 52.90%; and it also delayed the peak time by four and fourteen weeks, respectively. Similar findings are obtained for the testing efficacy that if it is increased by 12.67% from its baseline value, the weekly new cases at the peak are decreased by 59.05% and delayed the peak by 15 weeks. Therefore, a higher testing rate and efficacy reduce the disease burden by tumbling the new cases, representing a real scenario. It is also obtained that the testing rate and efficacy reduce the epidemic's severity by increasing the final size of the susceptible population. The testing rate is found more significant if testing efficacy is high. Global sensitivity analysis using partial rank correlation coefficients (PRCCs) and Latin hypercube sampling (LHS) determine the key parameters that must be targeted to worsen/contain the epidemic.
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In this paper, we propose, analyze and simulate a time delay differential equation to investigate the transmission and spread of Coronavirus disease (COVID-19). The basic reproduction number of the model is determined and qualitatively used to investigate the global stability of the model's steady states. We use numerical simulations to support the analytical results in the study. From the simulation results, we note that whenever the basic reproduction number is greater than unity, the model solutions will be associated with periodic oscillations for a considerable time scale from the start before attaining stability. This suggests that the inclusion of the time delay factor destabilizes the endemic equilibrium point leading to periodic solutions that arise due to Hopf bifurcations for a certain time frame.
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In this paper, a new method for obtaining the basic reproduction number is proposed, called the path analysis method. Compared with the traditional next-generation method, this method is more convenient and less error-prone. We develop a general model that includes most of the epidemiological characteristics and enumerate all disease transmission paths. The path analysis method is derived by combining the next-generation method and the disease transmission paths. Three typical examples verify the effectiveness and convenience of the method. It is important to note that the path analysis method is only applicable to epidemic models with bilinear incidence rates. The Volterra-type Lyapunov function is given to prove the global stability of the system. The simulations prove the correctness of our conclusions.
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Mathematical modeling techniques have been used extensively during the human immunodeficiency virus (HIV) epidemic. Drug injection causes increased HIV spread in most countries globally. The media is crucial in spreading health awareness by changing mixing behavior. The published studies show some of the ways that differential equation models can be employed to explain how media awareness programs influence the spread and containment of disease (Greenhalgh et al. Appl Math Comput. 2015;251:539–563). Here we build a differential equation model which shows how disease awareness programs can alter the HIV prevalence in a group of people who inject drugs (PWIDs). This builds on previous work by Greenhalgh and Hay (1997) and Liang et al. (2016). We have constructed a mathematical model to describe the improved model that reduces the spread of the diseases through the effect of awareness of disease on sharing needles and syringes among the PWID population. The model supposes that PWIDs clean their needles before use rather than after. We carry out a steady state analysis and examine local stability. Our discussion has been focused on two ways of studying the influence of awareness of infection levels in epidemic modeling. The key biological parameter of our model is the basic reproductive number R0$$ {R}_0 $$. R0$$ {R}_0 $$ is a crucial number which determines the behavior of the infection. We find that if R0$$ {R}_0 $$ is less than one then the disease-free steady state is the unique steady state and moreover whatever the initial fraction of infected individuals then the disease will die out as time becomes large. If R0$$ {R}_0 $$ exceeds one there is the disease-free steady state and a unique steady state with disease present. We also showed that the disease-free steady state is locally asymptotically stable if R0$$ {R}_0 $$ is less than one, neutrally stable if R0$$ {R}_0 $$ is equal to one and unstable if R0$$ {R}_0 $$ exceeds one. In the last case, when R0$$ {R}_0 $$ is greater than one the endemic steady state was locally asymptotically stable. Our analytical results are confirmed by using simulation with realistic parameter values. In nontechnical terms, the number R0$$ {R}_0 $$ is a critical value describing how the disease will spread. If R0$$ {R}_0 $$ is less than or equal to one then the disease will always die out but if R0$$ {R}_0 $$ exceeds one and disease is present the disease will sustain itself and moreover the numbers of PWIDs with disease will tend to a unique nonzero value.
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In this paper, COVID-19 SEIQR model which can cause death is studied. The virus has infectivity in both latent period and infectious period, the existence, local stability and global asymptotic stability of disease-free equilibrium point and local equilibrium point are proved. © 2022 IEEE.
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In this paper, we study a nonlinear mathematical model which addresses the transmission dynamics of COVID-19. The considered model consists of susceptible (S), exposed (E), infected (I), and recovered (R) individuals. For simplicity, the model is abbreviated as SEIR. Immigration rates of two kinds are involved in susceptible and infected individuals. First of all, the model is formulated. Then via classical analysis, we investigate its local and global stability by using the Jacobian matrix and Lyapunov function method. Further, the fundamental reproduction number ℛ0 is computed for the said model. Then, we simulate the model through the Runge–Kutta method of order two abbreviated as RK2. Finally, we switch over to the fractional order model and investigate its numerical simulations corresponding to different fractional orders by using the fractional order version of the aforementioned numerical method. Finally, graphical presentations are given for the approximate solution of various compartments of the proposed model. Also, a comparison with real data has been shown. [ FROM AUTHOR] Copyright of Fractals is the property of World Scientific Publishing Company 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.)
ABSTRACT
In this paper, we formulate and analyze a class of discrete state-structured epidemic models that spread through both horizontal and vertical transmissions on networks, where infected individuals can move from one infected state to any other state so that our models include all possible state-transfers (disease deterioration and amelioration) among different states. Many epidemic transmissions with or without vertical transmission in nature can be analyzed by referring to our models, such as HIV-1, viral hepatitis, and Covid-19. We derive the basic reproduction number R0= Rh+ Rv, and prove that the global dynamics are completely determined by the basic reproduction number: if R0 ≤ 1, the disease-free equilibrium is globally asymptotically stable and the disease always dies out;if R0 > 1, the disease-free equilibrium is unstable, and there exists a unique endemic equilibrium that is globally asymptotically stable, and the disease persists at a positive level in the population. It also implies that vertical transmission has an impact on maintaining infectious diseases when horizontal transmission cannot sustain the disease on its own. The proof of global stability is based on the graph-theoretic approach and answer the open problem left in [1]. Finally, numerical simulations are performed to illustrate the theoretical results. © 2023 American Institute of Mathematical Sciences. All rights reserved.
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The emergence of COVID-19 pandemic has been a major social as well as economic challenges around the globe. Infections from the infected surfaces have also been identified as drivers of COVID-19 transmission, but most of the epidemic models do not include the effect of environmental contamination to account for the indirect transmission of the disease. The present study is devoted to the investigation of the effect of environmental contamination on the spread of the coronavirus pandemic by means of a mathematical model. We also consider the impact of vaccination coverage as an effective control measure against COVID-19. The proposed model is analyzed to discuss the feasibility as well as stability of the disease-free and endemic equilibria;an epidemic threshold in the form of basic reproduction number is obtained. Further, we incorporate the effect of seasonal periodic changes by letting the rate of direct transmission of disease as time dependent, and find sufficient conditions for the global attractivity of the positive periodic solution. We employ sophisticated techniques of sensitivity analysis to identify model parameters which significantly alter the epidemic threshold and the disease prevalence. We find that by enhancing the vaccination of the susceptible population and hospitalization of the symptomatic/asymptomatic individuals, the basic reproduction number can be lowered to a value less than unity. The findings show that the prevalence of disease can be potentially suppressed by increasing the vaccination of susceptible population, hospitalization of infected people and depletion of environmental contamination. Moreover, we observe that seasonal pattern in the disease transmission causes persistence of the pandemic in the population for a longer period. © 2023 John Wiley & Sons, Ltd.
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In this paper, a SEIR model proposed in the article "Dynamic analysis of mathematical model with health care capacity for COVID-19 pandemic” by S. Çakan (2020) is analysed. The model describes COVID-19 pandemic spread affected by healthcare capacity and is expressed by a system of delay differential equations. To prove the local stability of stationary states, S. Çakan uses linearization technique, though she does this as if the equations did not depend on the delay. Additionally, it is shown that the crucial argument used by S. Çakan to prove boundedness of the solutions is not correct, which implies that the proofs of global stability in the original article are not correct either. In this paper, improved proofs of local and global stability of the stationary states are provided. For local stability of the stationary states a standard linearization technique is used. Global stability of the stationary states is proved based on Lyapunov's functionals. Although the functionals are the same as those proposed by S. Çakan, additional properties of the solutions (in the case of disease-free stationary state) and the functional (in the case of the endemic stationary state) are proved. © 2022 Polish Mathematical Society. All rights reserved.
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In this paper, we propose and study a Middle East respiratory syndrome coronavirus (MERS-CoV) infection model with cytotoxic T lymphocyte (CTL) immune response and intracellular delay. This model includes five compartments: uninfected cells, infected cells, viruses, dipeptidyl peptidase 4 (DPP4), and CTL immune cells. We obtained an immunity-inactivated reproduction number (Formula presented.) and an immunity-activated reproduction number (Formula presented.). By analyzing the distributions of roots of the corresponding characteristic equations, the local stability results of the infection-free equilibrium, the immunity-inactivated equilibrium, and the immunity-activated equilibrium were obtained. Moreover, by constructing suitable Lyapunov functionals and combining LaSalle's invariance principle and Barbalat's lemma, some sufficient conditions for the global stability of the three types of equilibria were obtained. It was found that the infection-free equilibrium is globally asymptotically stable if (Formula presented.) and unstable if (Formula presented.) ;the immunity-inactivated equilibrium is locally asymptotically stable if (Formula presented.) and globally asymptotically stable if (Formula presented.) and condition (H1) holds, but unstable if (Formula presented.) ;and the immunity-activated equilibrium is locally asymptotically stable if (Formula presented.) and is globally asymptotically stable if (Formula presented.) and condition (H1) holds. © 2023 by the authors.
ABSTRACT
The coronavirus disease 2019 (COVID-19) is a respiratory disease that appeared in 2019 caused by a virus called severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). COVID-19 is still spreading and causing deaths around the world. There is a real concern of SARS-CoV-2 coinfection with other infectious diseases. Tuberculosis (TB) is a bacterial disease caused by Mycobacterium tuberculosis (Mtb). SARS-CoV-2 coinfection with TB has been recorded in many countries. It has been suggested that the coinfection is associated with severe disease and death. Mathematical modeling is an effective tool that can help understand the dynamics of coinfection between new diseases and well-known diseases. In this paper, we develop an in-host TB and SARS-CoV-2 coinfection model with cytotoxic T lymphocytes (CTLs). The model investigates the interactions between healthy epithelial cells (ECs), latent Mtb-infected ECs, active Mtb-infected ECs, SARS-CoV-2-infected ECs, free Mtb, free SARS-CoV-2, and CTLs. The model's solutions are proved to be nonnegative and bounded. All equilibria with their existence conditions are calculated. Proper Lyapunov functions are selected to examine the global stability of equilibria. Numerical simulations are implemented to verify the theoretical results. It is found that the model has six equilibrium points. These points reflect two states: the mono-infection state where SARS-CoV-2 or TB occurs as a single infection, and the coinfection state where the two infections occur simultaneously. The parameters that control the movement between these states should be tested in order to develop better treatments for TB and COVID-19 coinfected patients. Lymphopenia increases the concentration of SARS-CoV-2 particles and thus can worsen the health status of the coinfected patient. © 2023 by the authors.
ABSTRACT
Human immunodeficiency virus type 1 (HIV-1) and human T-lymphotropic virus type I (HTLV-I) are two retroviruses that have a similar fashion of transmission via sharp objects contaminated by viruses, transplant surgery, transfusion, and sexual relations. Simultaneous infections with HTLV-I and HIV-1 usually occur in areas where both viruses have become endemic. CD4+T cells are the main targets of HTLV-I, while HIV-1 can infect CD4+T cells and macrophages. It is the aim of this study to develop a model of HTLV-I and HIV-1 coinfection that describes the interactions of nine compartments: susceptible cells of both CD4+T cells and macrophages, HIV-1-infected cells that are latent/active in both CD4+T cells and macrophages, HTLV-I-infected CD4+T cells that are latent/active, and free HIV-1 particles. The well-posedness, existence of equilibria, and global stability analysis of our model are investigated. The Lyapunov function and LaSalle's invariance principle were used to study the global asymptotic stability of all equilibria. The theoretically predicted outcomes were verified by utilizing numerical simulations. The effect of including the macrophages and latent reservoirs in the HTLV-I and HIV-1 coinfection model is discussed. We show that the presence of macrophages makes a coinfection model more realistic when the case of the coexistence of HIV-1 and HTLV-I is established. Moreover, we have shown that neglecting the latent reservoirs in HTLV-I and HIV-1 coinfection modeling will lead to the design of an overflow of anti-HIV-1 drugs.
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In this study, an epidemic mathematical model for Omicron, denoted as B.1.1.529 SARS-Cov-2 Variant, is constructed. Covid-19 vaccines are considered here to ensure the host population's safety across the model. The fundamentals of positivity and boundedness in this model have been investigated and validated. The reproduction number was calculated to determine whether or not the disease would spread further in Tamilnadu. Infection-free steady-state solutions that exist are asymptotically stable locally when R0 < 1 and globally when R0 > 1. Also Infection-present steady-state solutions that are locally stable are discovered when R0 < 1. Finally, the current Omicron variant pandemic data from Tamilnadu, India, is validated. © 2022 S. Dickson et al., published by De Gruyter.
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The whole world had been plagued by the COVID-19 pandemic. It was first detected in the Wuhan city of China in December 2019, and has then spread worldwide. It has affected each one of us in the worst possible way. In the current study, a differential equation-based mathematical model is proposed. The present model highlights the infection dynamics of the COVID-19 spread taking hospitalization into account. The basic reproduction number is calculated. This is a crucial indicator of the outcome of the COVID-19 dynamics. Local stability of the equilibrium points has been studied. Global stability of the model is proven using the Lyapunov second method and the LaSalle invariance principle. Sensitivity analysis of the model is performed to distinguish the factor responsible for the faster spread of the infection. Finally, the theoretical aspects have been corroborated via numerical simulations performed for various initial conditions and different values of the parameters. © 2023 the author(s).
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Medical treatment, vaccination, and quarantine are the most efficacious controls in preventing the spread of contagious epidemics such as COVID-19. In this paper, we demonstrate the global stability of the endemic and disease-free equilibrium by using the Lyapunov function. Moreover, we apply the three measures to minimize the density of infected people and also reduce the cost of controls. Furthermore, we use the Pontryagin Minimum Principle in order to characterize the optimal controls. Finally, we execute some numerical simulations to approve and verify our theoretical results using the fourth order Runge-Kutta approximation through Matlab. © 2023 the author(s).
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In this paper, we investigate the effect of partially vaccinated and fully vaccinated individuals in pre-venting the transmit of COVID-19, especially in the regions of Tamil Nadu, Maharashtra, West Bengal and Delhi. Here we construct an SEIR model and analyse the behaviour. We obtained R0 by using next generation matrix approach. Also, our system shows two types of equilibria, namely disease free and endemic equilibrium. For both disease free and endemic equilibrium, local and global stability is obtained here. Our disease-free equilibrium is locally asymptotically stable whenever R0 is less than one, whereas the endemic equilibrium is locally asymptotically stable whenever R0 is greater than one. Furthermore, the global stability of disease-free equilibrium has been proven by using Lyapunov function and the global stability of endemic equilibrium has been obtained by using Poincare Bendixson technique. Also, we enhance our analytic results by numerical simulation. At the end we have attempted to fit our proposed model with the real-world data. © 2023 the author(s).
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In this paper, a non-singular SIR model with the Mittag-Leffler law is proposed. The nonlinear Beddington-DeAngelis infection rate and Holling type II treatment rate are used. The qualitative properties of the SIR model are discussed in detail. The local and global stability of the model are analyzed. Moreover, some conditions are developed to guarantee local and global asymptotic stability. Finally, numerical simulations are provided to support the theoretical results and used to analyze the impact of face masks, social distancing, quarantine, lockdown, immigration, treatment rate of the disease, and limitation in treatment resources on COVID-19. The graphical results show that face masks, social distancing, quarantine, lockdown, immigration, and effective treatment rates significantly reduce the infected population over time. In contrast, limitation in the availability of treatment raises the infected population. © 2023 The Author(s). Published by IOP Publishing Ltd.
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This paper develops and analyzes a SARS-CoV-2 dynamics model with logistic growth of healthy epithelial cells, CTL immune and humoral (antibody) immune responses. The model is incorporated with four mixed (distributed/discrete) time delays, delay in the formation of latent infected epithelial cells, delay in the formation of active infected epithelial cells, delay in the activation of latent infected epithelial cells, and maturation delay of new SARS-CoV-2 particles. We establish that the model's solutions are non-negative and ultimately bounded. We deduce that the model has five steady states and their existence and stability are perfectly determined by four threshold parameters. We study the global stability of the model's steady states using Lyapunov method. The analytical results are enhanced by numerical simulations. The impact of intracellular time delays on the dynamical behavior of the SARS-CoV-2 is addressed. We noted that increasing the time delay period can suppress the viral replication and control the infection. This could be helpful to create new drugs that extend the delay time period.
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The COVID-19 pandemic has brought a serious threat to human life safety worldwide. SARS-CoV-2 virus mainly binds to the target cell surface receptor ACE2 (Angiotensin-converting enzyme 2 ) through the S protein expressed on the surface of the virus, resulting in infection of target cells. During this infection process, the target cell ACE2 receptor plays a very important mediating role. In this paper, a delay differential equation model containing the mediated effect of target cell receptor is established according to the mechanism of SARS-CoV-2 virus invasion of target cells, and the global stability of the infection-free equilibrium and the infected equilibrium of the model is obtained by using the basic reproduction number â 0 and constructing the appropriate Lyapunov functional. The expression of the basic reproduction number â 0 intuitively gives the dependence on the expression ratio of the target cell surface ACE2 receptor, which is helpful for the understanding of the mechanism of SARS-CoV-2 virus infection.
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The study explores the dynamics of a COVID-19 epidemic in multiple susceptible populations, including the various stages of vaccination administration. In the model, there are eight human compartments: completely susceptible;susceptible with dose-1 vaccination;susceptible with dose-2 vaccination;susceptible with booster dose vaccination;exposed;infected with and without symptoms, and recovered compartments. The biological feasibility of the model is analysed. The threshold value, R0, is derived using the next-generation matrix. The stability analysis of the equilibrium points was performed locally and globally using the threshold parameter of the model. The conditions determining disease persistence is obtained. The model is subjected to sensitivity analysis, and the most sensitive parameters are identified. Also, MATLAB is used to verify the mathematical outcomes of the system's dynamic behaviour and suggests that necessary steps should be taken to keep the spread of the omicron variant infectious disease under control. The findings of this study could aid health officials in their efforts to combat the spread of COVID-19. © 2022 International Association for Mathematics and Computers in Simulation (IMACS)