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To have a more realistic model, in this paper, This manuscript is devoted to investigating a fractional-order mathematical model of Kouidere et al. That describes the dynamics of spread of African swine fever virus (ASFV). The aim of this work is to protect susceptible pigs from the virus, In our model, by including three controls which represent: the iron fencing and spraying pesticides and get rid.

This paper focuses on the urgent issue of minimising the impact of pollutants on aquatic life in river ecosystems. Our innovative approach involves the integration of mathematical modelling and strategic control methods to counteract the negative consequences of industrial and agricultural activities. The model, developed in a one-dimensional context, captures the complex dynamics of species population and pollutant concentration. Using an optimisation framework, we strive to achieve a harmonious balance that limits pollution, enhances species diversity and optimises control expenditure. Ultimately, we seek to harmonise industrial progress with ecological vitality, promoting the sustainability of river ecosystems for generations to come. [ABSTRACT FROM AUTHOR]

Managing infections within populations poses significant challenges, particularly in achieving controllability over nonlinear models within epidemiological systems. In this study, the challenge is addressed by introducing a novel control function tailored to enhance the management of infections. The approach revolves around leveraging a nonlinear SIR epidemiological model, enabling the derivation of explicit solutions and fine-tuning of control parameters to align with predefined objectives. Specifically, the focus lies on guiding the number of infected individuals towards a predetermined threshold at a specified time for all initial values. Through rigorous numerical simulations, the effectiveness of the proposed control strategy in achieving greater controllability and regulating the spread of infection over time is depicted, For example, our simulations show that starting with an initial infected population of 150 individuals in a population of 25,150, the control strategy can reduce the number of infected individuals to below 40 within 30 days. The quantitative results presented underscore the efficacy of the approach, highlighting its potential to significantly impact disease management strategies. [ABSTRACT FROM AUTHOR]

Introduction: The first case of COVID-19 in Kenya was reported on March 13, 2020, prompting the collection of baseline data during the initial spread of the disease. Subsequently, the Kenyan government implemented non-pharmaceutical interventions (NPIs) on April 9, 2020, to mitigate disease transmission over a two-month period. These measures were later gradually relaxed starting from June 9, 2020. Methods: We applied a deterministic mathematical model to simulate the dynamics of COVID-19 transmission in Kenya. Using baseline data, we estimated transmission and recovery rates and proposed a mathematical model of how NPIs affect disease transmission rates. The model extends to interventions that yield an increase in disease transmission, unlike previous models that were limited to a decrease in transmission. We computed the mitigation and relaxation fractions and hence deduced the impact of the interventions. Results: The mitigation measures imposed from April 9, 2020, reduced the disease transmission by 43.7% from the baseline level, while the relaxation from June 9, 2020, increased the transmission by 32% over the mitigation level. Without intervention, the model predicts that infections would have peaked at 30% by late May 2020. However, due to the combined effect of mitigation and relaxation, the epidemic peaked at 13% infection in mid-July 2020. Discussion: The model's projections closely align with observed data, providing valuable insights for planning. Ongoing research aims to refine the model to capture sub-waves and spikes, as well as simulate multiple waves of infection. These efforts will enhance our understanding of COVID-19 dynamics and inform effective public health strategies. The estimated basic reproduction number = 2.76, consistent with previous findings, underscores the validity of our model and its relevance in predicting disease transmission dynamics. [ABSTRACT FROM AUTHOR]

In this paper, we aim to investigate optimal control to a mathematical model that describes agreedisagree opinions during polls in a multi-zone framework. We first present the model and recall its different compartments and we present some properties of the multi-zone model. We formulate the optimal control problem by supplementing our model with a functional objective. Optimal control strategies are proposed to reduce the number of disagreeing people in a targeted zone and the cost of interventions. We prove the existence of solutions to the control problem, and we employ the Pontryagin’s maximum principle to find the necessary conditions for the existence of the optimal controls and Runge- Kutta forward-backward sweep numerical approximation method is used to solve the optimal control system, and perform numerical simulations using various initial conditions and parameters to investigate several scenarios. [ABSTRACT FROM AUTHOR]

The rapid spread of inaccurate information through social media has become a major concern in recent years. This paper presents a mathematical model for analyzing the impact of inaccurate information on the spread of accurate information in a network. We aim to evaluate the effectiveness of three different strategies of control: media literacy programs and fact-checking and a combination of both. The model is analyzed using the maximum principle of Pontryagin to derive the optimal control strategies for minimizing the spread of inaccurate information while maximizing the spread of accurate information. A numerical simulation is presented to illustrate the effectiveness of the control strategies, and statistical analysis is performed to compare the impact of the different control strategies on the spread of information. The results demonstrate that the combination of media literacy programs and fact-checking is the most effective strategy for increasing the spread of accurate information and reducing the spread of inaccurate information. These findings have important implications for the design of effective strategies to combat the spread of misinformation and promote the spread of accurate information in a network. [ABSTRACT FROM AUTHOR]

In this paper. we propose a mathematical model that describes the dynamics of transmission of COVID-19 between potential people and infected ones, as well as between bats as virus carriers and people. This article deals with optimal control applied to vaccination, treatment strategies, and the hunt for a SIR-B epidemic model with logistic growth. The global stability of the disease-free and endemic equilibrium is verified. The existence of optimal control is demonstrated. The Pontryagin maximum principle is employed to describe these optimal controls. To validate our previous theoretical results. the optimality system is numerically resolved using a Matlab algorithm based on the Runge Kutta approximation 4. [ABSTRACT FROM AUTHOR]

In this paper, the observer-based control for a class of uncertain linear systems is considered. Exponential stabilizability for the system is studied and reduced-order observer is discussed. Numerical examples are given to illustrate obtained results.

In this paper, we focus on identifying the transmission rate associated with a COVID-19 mathematical model by using a predefined prevalence function. To do so, we use a Python code to extract the Lagrange interpolation polynomial from real daily data corresponding to an appropriate period in Morocco. The existence of a perfect control scheme is demonstrated. The Pontryagin maximum technique is used to explain these optimal controls. The optimality system is numerically solved using the 4th-order Runge-Kutta approximation. [ABSTRACT FROM AUTHOR]