The stochastic bifurcation analysis and stochastic delayed optimal control for epidemic model with general incidence function

Stochastic optimal control of pre-exposure prophylaxis for HIV infection for a jump model

We analyze a stochastic optimal control problem for the PReP vaccine in a model for the spread of HIV. To do so, we use a stochastic model for HIV/AIDS with PReP, where we include jumps in the model. This generalizes previous works in the field. First, we prove that there exists a positive, unique, global solution to the system of stochastic differential equations which makes up the model. Further, we introduce a stochastic control problem for dynamically choosing an optimal percentage of the population to receive PReP. By using the stochastic maximum principle, we derive an explicit expression for the stochastic optimal control. Furthermore, via a generalized Lagrange multiplier method in combination with the stochastic maximum principle, we study two types of budget constraints. We illustrate the results by numerical examples, both in the fixed control case and in the stochastic control case.

Bifurcation and onset of chaos in an eco-epidemiological system with the influence of time delay

In the present article, we investigated a delay-based eco-epidemic prey-predator system in the presence of environmental fluctuations where predators engage with susceptible and infected prey, adopting Holling type II and ratio-dependent functional responses, respectively. During the study of the considered model, we identify each potential equilibrium point and its local stability criterion. The basic reproduction number has been computed, and the backward bifurcation about the disease-free equilibrium point was analyzed. The article illustrates Hopf bifurcation, global stability at the endemic equilibrium point, and their graphical depiction. We look over the variations in the dynamics of non-delay, delayed, and stochastic systems, revealing that a fixed level of temporal delay results in chaotic motion for the increasing strength of the saturation constant yet is potentially controlled by the predator growth rate. To study the dynamic behavior of the solution of the considered system and verify all theoretical results, we use numerical simulation and minutely analyze the influence of model parameters on the solution of the considered system. The stochastic transition is studied by varying the strength of stochastic fluctuation and the effect of delay.

Introducing a novel mean-reverting Ornstein-Uhlenbeck process based stochastic epidemic model

The major objective of this paper is to examine a novel mean-reverting Ornstein-Uhlenbeck process-based stochastic SIRD model for transmission the epidemic disease that is a great crisis in numerous societies. For this purpose, the deterministic model is further converted into the stochastic form by allowing the infection rate satisfies the mean-reverting Ornstein-Uhlenbeck process to account the uncertainties involved in epidemic spread. At first using Lyapunov functions, the solution's uniqueness and positivity will be demonstrated. Subsequently, the stochastic epidemic threshold [Formula: see text] that controls the disease's extinction and persistence in the mean is identified analytically. It has been established that when [Formula: see text] the disease will extinguish, whereas if [Formula: see text] the disease is persistent. At last, several numerical simulations are presented to demonstrate the findings of the hypothetical investigation results. These simulations served to vividly illustrate and validate the implications derived from the hypothetical analysis.

The stability analysis of a co-circulation model for COVID-19, dengue, and zika with nonlinear incidence rates and vaccination strategies

This paper aims to study the impacts of COVID-19 and dengue vaccinations on the dynamics of zika transmission by developing a vaccination model with the incorporation of saturated incidence rates. Analyses are performed to assess the qualitative behavior of the model. Carrying out bifurcation analysis of the model, it was concluded that co-infection, super-infection and also re-infection with same or different disease could trigger backward bifurcation. Employing well-formulated Lyapunov functions, the model's equilibria are shown to be globally stable for a certain scenario. Moreover, global sensitivity analyses are performed out to assess the impact of dominant parameters that drive each disease's dynamics and its co-infection. Model fitting is performed on the actual data for the state of Amazonas in Brazil. The fittings reveal that our model behaves very well with the data. The significance of saturated incidence rates on the dynamics of three diseases is also highlighted. Based on the numerical investigation of the model, it was observed that increased vaccination efforts against COVID-19 and dengue could positively impact zika dynamics and the co-spread of triple infections.

A fractional-order mathematical model for lung cancer incorporating integrated therapeutic approaches

This paper addresses the dynamics of lung cancer by employing a fractional-order mathematical model that investigates the combined therapy of surgery and immunotherapy. The significance of this study lies in its exploration of the effects of surgery and immunotherapy on tumor growth rate and the immune response to cancer cells. To optimize the treatment dosage based on tumor response, a feedback control system is designed using control theory, and Pontryagin's Maximum Principle is utilized to derive the necessary conditions for optimality. The results reveal that the reproduction number [Formula: see text] is 2.6, indicating that a lung cancer cell would generate 2.6 new cancer cells during its lifetime. The reproduction coefficient [Formula: see text] is 0.22, signifying that cancer cells divide at a rate that is 0.22 times that of normal cells. The simulations demonstrate that the combined therapy approach yields significantly improved patient outcomes compared to either treatment alone. Furthermore, the analysis highlights the sensitivity of the steady-state solution to variations in [Formula: see text] (the rate of division of cancer stem cells) and [Formula: see text] (the rate of differentiation of cancer stem cells into progenitor cells). This research offers clinicians a valuable tool for developing personalized treatment plans for lung cancer patients, incorporating individual patient factors and tumor characteristics. The novelty of this work lies in its integration of surgery, immunotherapy, and control theory, extending beyond previous efforts in the literature.

Multi-field coupled dynamics for a movable tooth drive system integrated with shape memory alloys

The harmonic movable tooth drive system integrated with shape memory alloys has a small size and a large output torque. Its dynamics performance is the key factor for evaluating the drive system. Here, for the drive system, based on its structure and working principle, the coupled dynamics equations are deduced. Using the equations, changes of the natural frequencies of the drive system during the operation are investigated. Effects of the system parameters and SMA wires phase change process on the natural frequencies are analyzed. The nonlinear resonant frequencies of the drive system and its amplitude-frequency relationship are studied. Results show that natural frequencies of the drive system change periodically which is caused by SMA phase transformation during operation. The eccentricity, movable tooth radius, the wave generator radius and SMA wire length have also important effects on the natural frequencies of the drive system. The nonlinear resonant frequencies are smaller than linear resonant frequencies. In the design of the drive system, the coupled nonlinear effects of the temperature, phase change, stress and strain of the SMA wires, and the system parameters of the movable tooth drive system should be considered. In this paper, the coupled nonlinear dynamics model of the harmonic movable tooth drive system integrated with shape memory alloys is proposed in which the coupled effects of the temperature, phase change, stress and strain of the SMA, and the system parameters of the movable tooth drive system are considered.

Mathematical analysis of heat and mass transfer on unsteady stagnation point flow of Riga plate with binary chemical reaction and thermal radiation effects

To aid in the prevention of reaction explosions, chemical engineers and scientists must analyze the Arrhenius kinetics and activation energies of chemical reactions involving binary chemical mixtures. Nanofluids with an Arrhenius kinetic are crucial for a broad variety of uses in the industrial sector, involving the manufacture of chemicals, thermoelectric sciences, biomedical devices, polymer extrusion, and the enhancement of thermal systems via technology. The goal of this study is to determine how the presence of thermal radiation influences heat and mass transfer during free convective unsteady stagnation point flow across extending/shrinking vertical Riga plate in the presence of a binary chemical reaction where the activation energy of the reaction is known in advance. For the purpose of obtaining numerical solutions to the mathematical model of the present issue the Runge-Kutta (RK-IV) with shooting technique in Mathematica was used. Heat and mass transfer processes, as well as interrupted flow phenomena, are characterized and explained by diagrams in the suggested suction variables along boundary surface in the stagnation point flow approaching a permeable stretching/shrinking Riga Plate. Graphs illustrated the effects of many other factors on temperature, velocity, concentration, Sherwood and Nusselt number as well as skin friction in detail. Velocity profile increased with Z , λ and S and decreased with ε . Increasing values of ε , λ and S decline the temperature profile. The concentration profile boosts up with Z , α and slow down with ε , S c , β , δ and n 1 parameters. Skin friction profile increased with Z and S and decreased with ε . Nusselt number profile increased with S , Z , ε and radiation. Sherwood number profile shows upsurges with ε , Z , α , S c , β , S and n 1 whereas slow down with δ . So that the verdicts could be confirmed, a study was done to compare the most recent research with the results that had already been published for a certain case. The outcomes demonstrated strong concordance between the two sets of results.

Estimation of the serial interval of monkeypox during the early outbreak in 2022

With increased transmissibility and novel transmission mode, monkeypox poses new threats to public health globally in the background of the ongoing COVID-19 pandemic. Estimates of the serial interval, a key epidemiological parameter of infectious disease transmission, could provide insights into the virus transmission risks. As of October 2022, little was known about the serial interval of monkeypox due to the lack of contact tracing data. In this study, public-available contact tracing data of global monkeypox cases were collected and 21 infector-infectee transmission pairs were identified. We proposed a statistical method applied to real-world observations to estimate the serial interval of the monkeypox. We estimated a mean serial interval of 5.6 days with the right truncation and sampling bias adjusted and calculated the reproduction number of 1.33 for the early monkeypox outbreaks at a global scale. Our findings provided a preliminary understanding of the transmission potentials of the current situation of monkeypox outbreaks. We highlighted the need for continuous surveillance of monkeypox for transmission risk assessment.

Bioinformatics Analysis Revealing the Correlation between NF-κB Signaling Pathway and Immune Infiltration in Gastric Cancer

Although the emerging of immunotherapy conferred a new landscape of gastric cancer (GC) treatment, its response rate was of significant individual differences. Insight into GC immune microenviroment may contribute to breaking the dilemma. To this end, the enrichment score of NF-κB signaling pathway was calculated in each GC sample from The Cancer Genome Atlas (TCGA) via ssGSEA algorithm, and its association with immune infiltration was estimated. Based on NF-κB-related genes, a risk score was established and its involvement in immune infiltration, tumor mutational burden (TMB), and N6-methyladenosine (M6A) modification was analyzed in GC. The results showed that NF-κB signaling pathway promoted the infiltration of immune cells in GC. In addition, GC samples were divided into low- and high-risk groups according to a seven-gene (CARD11, CCL21, GADD45B, LBP, RELB, TRAF1, and VCAM1) risk score. Although the high-risk group displayed high immune infiltration and high expression of M6A regulatory genes, it remains in an immunosuppressive microenviroment and whereby suffers a poorer outcome. Of note, most of hub genes were related to immune infiltration and could serve as an independent prognostic biomarker. Conclusively, our study emphasized the crucial role of NF-κB signaling pathway in GC immune microenviroment and provided several candidate genes that may participate in immune infiltration.

Dynamics of a stochastic COVID-19 epidemic model considering asymptomatic and isolated infected individuals

Coronavirus disease (COVID-19) has a strong influence on the global public health and economics since the outbreak in 2020. In this paper, we study a stochastic high-dimensional COVID-19 epidemic model which considers asymptomatic and isolated infected individuals. Firstly we prove the existence and uniqueness for positive solution to the stochastic model. Then we obtain the conditions on the extinction of the disease as well as the existence of stationary distribution. It shows that the noise intensity conducted on the asymptomatic infections and infected with symptoms plays an important role in the disease control. Finally numerical simulation is carried out to illustrate the theoretical results, and it is compared with the real data of India.

Modeling the dynamics of coronavirus with super-spreader class: A fractal-fractional approach

Super-spreaders of the novel coronavirus disease (or COVID-19) are those with greater potential for disease transmission to infect other people. Understanding and isolating the super-spreaders are important for controlling the COVID-19 incidence as well as future infectious disease outbreaks. Many scientific evidences can be found in the literature on reporting and impact of super-spreaders and super-spreading events on the COVID-19 dynamics. This paper deals with the formulation and simulation of a new epidemic model addressing the dynamics of COVID-19 with the presence of super-spreader individuals. In the first step, we formulate the model using classical integer order nonlinear differential system composed of six equations. The individuals responsible for the disease transmission are further categorized into three sub-classes, i.e., the symptomatic, super-spreader and asymptomatic. The model is parameterized using the actual infected cases reported in the kingdom of Saudi Arabia in order to enhance the biological suitability of the study. Moreover, to analyze the impact of memory index, we extend the model to fractional case using the well-known Caputo-Fabrizio derivative. By making use of the Picard-Lindelöf theorem and fixed point approach, we establish the existence and uniqueness criteria for the fractional-order model. Furthermore, we applied the novel fractal-fractional operator in Caputo-Fabrizio sense to obtain a more generalized model. Finally, to simulate the models in both fractional and fractal-fractional cases, efficient iterative schemes are utilized in order to present the impact of the fractional and fractal orders coupled with the key parameters (including transmission rate due to super-spreaders) on the pandemic peaks.

Solution of a dynamical memory effect COVID-19 infection system with leaky vaccination efficacy by non-singular kernel fractional derivatives

In this paper, the recent trends of COVID-19 infection spread have been studied to explore the advantages of leaky vaccination dynamics in SEVR (Susceptible Effected Vaccinated Recovered) compartmental model with the help of Caputo-Fabrizio (CF) and Atangana-Baleanu derivative in the Caputo sense (ABC) non-singular kernel fractional derivative operators with memory effect within the model to show possible long-term approaches of the infection along with limited defensive vaccine efficacy that can be designed numerically over the closed interval ranging from 0 to 1. One of the main goals is to provide a stepping information about the usefulness of the aforementioned non-singular kernel fractional approaches for a lenient case as well as a critical case in COVID-19 infection spread. Another is to investigate the effect of death rate on state variables. The estimation of death rate for state variables with suitable vaccine efficacy has a significant role in the stability of state variables in terms of basic reproduction number that is derived using next generation matrix method, and order of the fractional derivative. For non-integral orders the pandemic modeling sense viz, CF and ABC, has been compared thoroughly. Graphical presentations together with numerical results have proposed that the methodology is powerful and accurate which can provide new speculations for COVID-19 dynamical systems.

Threshold dynamics of a stochastic SIHR epidemic model of COVID-19 with general population-size dependent contact rate

In this paper, we propose a stochastic SIHR epidemic model of COVID-19. A basic reproduction number $ R_{0}^{s} $ is defined to determine the extinction or persistence of the disease. If $ R_{0}^{s} < 1 $, the disease will be extinct. If $ R_{0}^{s} > 1 $, the disease will be strongly stochastically permanent. Based on realistic parameters of COVID-19, we numerically analyze the effect of key parameters such as transmission rate, confirmation rate and noise intensity on the dynamics of disease transmission and obtain sensitivity indices of some parameters on $ R_{0}^{s} $ by sensitivity analysis. It is found that: 1) The threshold level of deterministic model is overestimated in case of neglecting the effect of environmental noise; 2) The decrease of transmission rate and the increase of confirmed rate are beneficial to control the spread of COVID-19. Moreover, our sensitivity analysis indicates that the parameters $ \beta $, $ \sigma $ and $ \delta $ have significantly effects on $ R_0^s $.