Search Results (553)

This study introduces a novel Fuzzy Piecewise Fractional Derivative (FPFD) framework to enhance epidemiological modeling, specifically for the multi-phasic co-infection dynamics of Omicron and malaria. We address the limitations of traditional models by incorporating two key realities. First, we use fuzzy set theory to manage the inherent uncertainty in biological parameters. Second, we employ piecewise fractional operators to capture the dynamic, phase-dependent nature of epidemics. The framework utilizes a fuzzy classical derivative for initial memoryless spread and transitions to a fuzzy Atangana–Baleanu–Caputo (ABC) fractional derivative to capture post-intervention memory effects. We establish the mathematical rigor of the FPFD model through proofs of positivity, boundedness, and stability of equilibrium points, including the basic reproductive number (R0). A hybrid numerical scheme, combining Fuzzy Runge–Kutta and Fuzzy Fractional Adams–Bashforth–Moulton algorithms, is developed for solving the system. Simulations show that the framework successfully models dynamic shifts while propagating uncertainty. This provides forecasts that are more robust and practical, directly informing public health interventions. Full article

This study develops a stochastic SIQR epidemic model with mean-reverting Ornstein–Uhlenbeck (OU) processes for both transmission rate $\beta \left(t\right)$ and quarantine release rate $k\left(t\right)$; this is distinct from existing non-white-noise stochastic epidemic models, most of which focus on single-parameter perturbation or only stability analysis. It synchronously embeds OU dynamics into two core epidemic parameters to capture asynchronous fluctuations between infection spread and control measures. It adopts a rare measure solution framework to derive rigorous infection extinction conditions, linking OU’s ergodicity to long-term ${\beta }^{+}\left(t\right)$ averages. It obtains the explicit probability density function of the four-dimensional SIQR system, filling the gap of lacking quantifiable density dynamics in prior studies. Simulations validate that $R_0^d<1$ ensures almost sure extinction, while $R_0^e>1$ leads to stable stochastic persistence. Full article

We propose the Clustered Link-Prediction SEIRS model with Temporal Node Activation (CLP-SEIRS-T), a novel epidemiological framework that integrates community structure, link prediction, and temporal activation schedules to simulate malware propagation in urban communication networks. Unlike traditional static or homogeneous models, our approach captures the heterogeneous community structure of the network (modular connectivity), along with evolving connectivity (emergent links) and periodic device-usage patterns (online/offline cycles), providing a more realistic portrayal of how computer viruses spread. Simulation results demonstrate that strong community modularity and intermittent connectivity significantly slow and localize outbreaks. For instance, when devices operate on staggered duty cycles (asynchronous online schedules), malware transmission is fragmented into multiple smaller waves with lower peaks, often confining infections to isolated communities. In contrast, near-continuous and synchronized connectivity produces rapid, widespread contagion akin to classic epidemic models, overcoming community boundaries and infecting the majority of nodes in a single wave. Furthermore, by incorporating a common-neighbor link-prediction mechanism, CLP-SEIRS-T accounts for future connections that can bridge otherwise disconnected clusters. This inclusion significantly increases the reach and persistence of malware spread, suggesting that ignoring evolving network topology may underestimate outbreak risk. Our findings underscore the importance of considering temporal usage patterns and network evolution in malware epidemiology. The proposed model not only elucidates how timing and community structure can flatten or exacerbate infection curves, but also offers practical insights for enhancing the resilience of urban communication networks—such as staggering device online schedules, limiting inter-community links, and anticipating new connections—to better contain fast-spreading cyber threats. Full article

Our paper employs complex network theory and the SEIQRS epidemic model based on the dynamics of differential equations to investigate the contagion mechanisms of financial risk within banking systems and to evaluate rescue strategies. A scale-free interbank network of 36 listed Chinese banks is constructed using the minimum-density method. Under the SEIQRS epidemic model, we simulate risk propagation pathways and analyze how key parameters affect systemic risk. Simulation of various rescue interventions demonstrates that, building on the existing support framework, coordinated adjustment of the quarantine rate, exposed-to-infectious transition rate, and quarantine-recovery rate can substantially curb the spread of risk. Among the strategies tested, the high-degree-first rescue strategy yields the best outcomes but requires precise timing, specifically, implementation at the first non-worsening time point. Finally, we offer some policy recommendations, which provide theoretical support and practical enlightenment for preventing cross-system financial risk contagion. Full article

A spatiotemporal transmission epidemic model is proposed based on human mobility, spatial factors of population migration across multiple regions, individual protection, and government quarantine measures. First, the model’s basic reproduction number and disease-free equilibrium are derived, and the relationship between the basic reproduction number in a single region and that across multiple regions is explored. Second, the global asymptotic stability of both the disease-free equilibrium and the endemic equilibrium is proved by constructing a Lyapunov function. The impact of population migration on the spread of the virus is revealed by numerical simulations, and the global sensitivity of the model parameters is analyzed for a single region. Finally, a protection isolation strategy based on the optimal path is proposed. The experimental results indicate that increasing the isolation rate, improving the treatment rate, enhancing personal protection, and reducing the infection rate can effectively prevent and control the spread of the epidemic. Population migration accelerates the spread of the virus from high-infected areas to low-infected areas, aggravating the epidemic situation. However, effective public health measures in low-infected areas can prevent transmission and reduce the basic reproduction number. Furthermore, if the inflow migration rate exceeds the outflow rate, the number of infected individuals in the region increases. Full article

In this study, we formulate and analyze a susceptible–infected–susceptible (SIS) dynamic on metapopulation networks, where each node has a finite carrying capacity and the motion of individuals is modulated by vacant space at the destination. We obtain that the vacancy-dependent mobility pattern results in various asymptotic population distributions on heterogeneous metapopulation networks. The resulting population distributions have remarkable impact on the behavior of SIS dynamics. We show that, for the given total number of individuals, higher heterogeneity in population distributions facilitates epidemic spreading in terms of both a smaller epidemic threshold and larger macroscopic incidence. Moreover, we analytically obtain a sufficient condition that the disease-free equilibrium becomes unstable and an endemic state arises. Contrary to the absence of an epidemic threshold in the standard diffusion case without excluded-volume effects, the finite carrying capacity induces a nonzero epidemic threshold under certain conditions in the limit of infinite network sizes with an unbounded maximum degree. Our analytical results agree well with numerical simulations. Full article

The increasing complexity of maritime traffic, driven by the expansion of international trade and growing shipping demand, has resulted in frequent ship collisions with significant consequences. This paper evaluates the credibility of the risk, calculated using the automatic identification system (AIS), in busy waterways and integrates AIS data with video surveillance data to comprehensively analyze the risk of ship collision. Specifically, this study utilizes the IALA Waterways Risk Assessment Program (IWRAP) tool to simulate maritime traffic flow and assess collision risk probabilities across various study areas and time periods. In addition, we analyze data from 2019 to 2022 to explore the impact of the COVID-19 pandemic on maritime traffic and find that the number of ship arrivals during the epidemic has decreased, resulting in a decrease in accident risk. We identify four traffic conflict areas in the real-world study area and point out that there are multi-directional ship interactions in these areas, but compliance with traffic rules can effectively reduce the risk of accidents. Additionally, simulations suggest that even a 13.5% increase in ocean-going vessel (OGV) traffic would raise collision risk by only 0.0247 incidents/year. To more accurately analyze the risk of waterways, we investigate the capture of dynamic information for ships in waterways by using the learning-driven detection model for real-time ship detection. These findings highlight the effectiveness of combining AIS and visual data for waterway risk assessment, offering critical insights for improving safety measures and informing policy development. Full article

This study investigates the dynamic behavior of a discrete epidemic model as affected by media coverage through integrated analytical and numerical methods. The main objective is to quantitatively assess the impact of media coverage on disease outbreak models through mathematical modeling. We use the central manifold theorem and bifurcation theory to perform a rigorous analysis of the periodic solutions, focusing on the coefficients and conditions governing the flip bifurcation. On this basis, state feedback and hybrid control are utilized to control the system chaotically. Under certain conditions, the chaos and bifurcation of the system can be stabilized by the control strategy. Numerical simulations further reveal the bifurcation dynamics, chaotic behavior, and control techniques. Our results show that media coverage is a key factor in regulating the intensity and chaos of disease transmission. Control techniques can effectively prevent large-scale outbreaks of epidemics. Notably, enhanced media coverage can effectively increase public awareness and defensive behaviors, thus contributing to mitigating disease spread. Full article

The epidemic modeling of computer virus propagation is identified as an effective approach to understanding the mechanism of virus spread. Fraction-order virus spread models exhibit remarkable advantages over their integer-order counterparts. Based on a type of bursting virus, a fractional computer virus propagation model with saturation effect is suggested. The basic properties of the model are discussed. The basic reproduction number of the model is determined. The virus–endemic equilibria of the model are determined. A criterion for the global asymptotic stability of the virus-free equilibrium is derived. For a pair of potential virus–endemic equilibria, criteria for the local asymptotic stability are presented. Some interesting properties of the model, ranging from the impact of the fractional order and the saturation index on virus spread to their coupling effect, are revealed through numerical simulations. This work helps gain a deep insight into the laws governing virus propagation. Full article

This study investigates the dynamic behaviors of the discrete epidemic model influenced by media coverage through integrated analytical and numerical approaches. The primary objective is to quantitatively assess the impact of media coverage on disease outbreak patterns using mathematical modeling. Firstly, the Euler method is used to discretize the model (2), and the periodic solution is strictly analyzed. Secondly, the coefficients and conditions of restricted flip and Neimark–Sacker bifurcation are studied by using the center manifold theorem and bifurcation theory. By calculating the largest Lyapunov exponent near the critical bifurcation point, the occurrence of chaos and limit cycles is proved. On this basis, the chaotic control of the system is carried out by using state feedback and hybrid control. Under certain conditions, the chaos and bifurcation of the system can be stabilized by control strategies. Numerical simulations further reveal bifurcation dynamics, chaotic behaviors, and control technologies. Our results show that media coverage is a key factor in regulating the intensity of disease transmission and chaos. The control technology can effectively prevent the large-scale outbreak of epidemic diseases. Importantly, enhanced media coverage can effectively promote public awareness and defensive behaviors, thereby contributing to the mitigation of disease transmission. Full article

The global fight against HIV/AIDS has been significantly bolstered by the development and implementation of pre-exposure prophylaxis (PrEP), yet innovation in PrEP interventions, improved adherence and greater access are still needed to maximize its benefit. Nonhuman primate (NHP) infection with simian immunodeficiency virus (SIV) has served as an instrumental animal model in advancing HIV PrEP research. This review comprehensively examines the utility of NHP models in evaluating the efficacy, pharmacokinetics, and safety of diverse PrEP strategies, including oral, injectable, implantable, and topical formulations. It discusses the development of diverse challenge models that simulate human transmission routes and the advantages of NHPs in enabling controlled and mechanistically informative studies. It also highlights the successful translation of pivotal NHP studies evaluating tenofovir-based regimens as well the long-acting agents, cabotegravir and lenacapavir, into the clinical settings, emphasizing the consistently high predictive power of the NHP models for the HIV PrEP clinical efficacy. Finally, it underscores the importance of species-specific pharmacologic considerations and the value of NHP data in informing clinical trial design. As the global community strives to end the HIV epidemic as a public health threat in the absence of an efficacious prophylactic vaccine, NHP models make a critical contribution in the development of next-generation HIV prevention tools. Full article

The COVID-19 pandemic highlighted the need for a scientific framework that enables quantitative assessment and control of airborne infection risks in indoor environments. This study identifies limitations in the traditional Wells–Riley model—specifically its assumptions of perfect mixing and steady-state conditions—and addresses these shortcomings by adopting the REHVA (Federation of European Heating, Ventilation and Air Conditioning Associations) infection risk assessment model. We propose a five-tier risk classification system (Monitor, Caution, Alert, High Risk, Critical) based on two key metrics: the probability of infection (Pₙ) and the event reproduction number (R_event). Unlike the classical model, our approach integrates airborne virus removal mechanisms—such as natural decay, gravitational settling, and filtration—with occupant dynamics to reflect realistic contagion scenarios. Simulations were conducted across 10 representative indoor settings—such as classrooms, hospital waiting rooms, public transit, and restaurants—considering ventilation rates and activity-specific viral emission patterns. The results quantify how environmental variables (ventilation, occupancy, time) impact each setting’s infection risk level. Our findings indicate that static mitigation measures such as mask-wearing or physical distancing are insufficient without dynamic, model-based risk evaluation. We emphasize the importance of incorporating real-time crowd density, occupancy duration, and movement trajectories into risk scoring. To support this, we propose integrating computer vision (CCTV-based crowd detection) and entry/exit counting sensors within a live airborne risk assessment framework. This integrated system would enable proactive, science-driven epidemic control strategies, supporting real-time adaptive interventions in indoor spaces. The proposed platform could serve as a practical tool for early warning and management during future airborne disease outbreaks. Full article

The application of contagion risk spread modeling within the banking sector is a relatively recent development, emerging as a response to the persistent threat of liquidity risk that has affected financial institutions globally. Liquidity risk is recognized as one of the most destructive financial threats to banks, capable of causing severe and irreparable damage if overlooked or underestimated. This study aims to identify the most effective control strategy for managing financial contagion using a Susceptible–Infected–Recovered (SIR) epidemic model, incorporating time delays in both state and control variables. The proposed strategy seeks to maximize the number of resilient (vulnerable) banks while minimizing the number of infected institutions at risk of bankruptcy. Our goal is to formulate intervention policies that can curtail the propagation of financial contagion and mitigate associated systemic risks. Our model remains a simplification of reality. It does not account for strategic interactions between banks (e.g., panic reactions, network coordination), nor for adaptive regulatory mechanisms. The integration of these aspects will be the subject of future work. We establish the existence of an optimal control strategy and apply Pontryagin’s Maximum Principle to characterize and analyze the control dynamics. To numerically solve the control system, we employ a discretization approach based on forward and backward finite difference approximations. Despite the model’s simplifications, it captures key dynamics relevant to major European banks. Simulations performed using Python 3.12 yield significant results across three distinct scenarios. Notably, in the most severe case (${\alpha }_3=1.0$), the optimal control strategy reduces bankruptcies from 25% to nearly 0% in Spain, and from 12.5% to 0% in France and Germany, demonstrating the effectiveness of timely intervention in containing financial contagion. Full article

The Working Set concept, originally introduced by P. Denning for memory management, defines a dynamic subset of system elements actively in use. Designed to reduce page faults and prevent thrashing, it has proven effective in optimizing memory performance. This study explores the interdisciplinary potential of the Working Set by applying it to two distinct domains: virtual memory systems and epidemiological modeling. We demonstrate that focusing on the active subset of a system enables optimization in both contexts—minimizing page faults and containing epidemics via dynamic isolation. The effectiveness of this approach is validated through memory access simulations and agent-based epidemic modeling. The advantages of using the Working Set as a general framework for describing the behavior of dynamic systems are discussed, along with its applicability across a wide range of scientific and engineering problems. Full article

This study presents an extended approach to compartmental modeling of infectious disease spread, focusing on regional heterogeneity within affected areas. Using classical SIS, SIR, and SEIR frameworks, we simulate the dynamics of COVID-19 across two major regions of Ukraine—Dnipropetrovsk and Kharkiv—during the period 2020–2024. The proposed mathematical model incorporates regionally distributed subpopulations and applies a system of differential equations solved using the classical fourth-order Runge–Kutta method. The simulations are validated against real-world epidemiological data from national and international sources. The SEIR model demonstrated superior performance, achieving maximum relative errors of 4.81% and 5.60% in the Kharkiv and Dnipropetrovsk regions, respectively, outperforming the SIS and SIR models. Despite limited mobility and social contact data, the regionally adapted models achieved acceptable accuracy for medium-term forecasting. This validates the practical applicability of extended compartmental models in public health planning, particularly in settings with constrained data availability. The results further support the use of these models for estimating critical epidemiological indicators such as infection peaks and hospital resource demands. The proposed framework offers a scalable and computationally efficient tool for regional epidemic forecasting, with potential applications to future outbreaks in geographically heterogeneous environments. Full article