Search Results (246)

This study examines how misinformation can persist when broadcast exposure and social feedback reinforce one another under stochastic platform conditions. Text classifiers and single-layer cascade models omit latent exposure, reply-driven amplification, random attention shocks, and uncertainty in intervention response. A stochastic duplex SEIR model is developed on heterogeneous networks, with an information exposure layer for broadcast and recommendation channels and a social feedback layer for replies, discussion, and amplification. The analysis combines degree-weighted mean-field equations, next-generation threshold calculations, Lyapunov stability arguments, Fokker–Planck linear noise approximation, Milstein simulation, and Wasserstein distributionally robust control. Theoretical results provide positivity, stochastic threshold conditions, extinction and persistence regimes, and sufficient conditions for stationary behavior and robust control stability. Numerical simulations show extinction–persistence transitions, cross-layer resonance, noise-induced threshold shifts, stationary bands, control cost–safety trade-offs, and sensitivity to unidentifiable stochastic parameters. A CoAID tweet–reply case study maps public interaction traces to observable duplex indicators, including tweet–reply densities, propagation elasticities, coupling proxies, and classifier features. Duplex observable features improve over a single-layer public data baseline, while model-assisted stochastic features add modest gains in the available public projection. Structural fitting of the stochastic duplex process would require time-stamped user-level multiplex trajectories, recommendation exposures, and intervention logs. The case study also clarifies the data granularity needed for future platform-level calibration and operational readiness. The framework supports data-informed platform governance by linking propagation thresholds, algorithmic down-ranking, reply thread moderation, intervention cost, and robustness bounds within a common threshold control language for practical settings. Full article

Background/Objectives: African Union (AU) guidance identifies decentralized diagnostics as central to epidemic preparedness. However, the epidemiological role of self-testing across epidemic-prone diseases remains underexplored. Drivers for the potential impact of self-testing were examined conceptually using a transmission model. Methods: A deterministic SEIR model compared standard-of-care testing with additional self-testing. Global sensitivity analysis using Latin Hypercube sampling and partial rank correlation coefficients (PRCCs) examined parameters influencing reductions in peak disease prevalence (mitigation). Dynamics were illustrated using AU pathogen archetypes (Ebola, Influenza A, Cholera, Coronavirus, and Mpox), estimating the number needed to self-test (NNST) to avert one death. Results: Epidemic mitigation was minimal (median 1.9%; IQR: 0.4–5.8%); this correlated with isolation adherence (PRCC = 0.784), self-testing intensity (PRCC = 0.617), lower R₀ (basic reproductive number; PRCC = −0.607) and greater duration of infectiousness (PRCC = 0.370). Conditional scenario exploration indicated 34 self-tests per 10,000 people per day to achieve a 10% reduction in peak prevalence at R₀ = 1.1, assuming self-test sensitivity 78.7%, specificity 99.3%. This exceeded the WHO Afro COVID-19 operational benchmark of 10 per 10,000 per week. High-mortality, moderate-transmission archetypes (e.g., Ebola) were most responsive to mortality reductions (median 1512 NNST/death averted) compared to Mpox (median 355,708 NNST/death averted). Adherence to post-test isolation exerted greater epidemiological impact than diagnostic accuracy. Conclusions: The epidemiological value of untargeted self-testing depends on pathogen characteristics and post-test behavioral adherence. Epidemic mitigation effects were limited under constrained health-system capacity. Future studies evaluating early decentralized self-testing deployment during Ebola-archetype outbreaks may identify operationally feasible deployment strategies to support mitigation and mortality reduction. Full article

This paper introduces a fractal–fractional SEIR epidemic model based on the Atangana–Baleanu derivative in the Caputo sense augmented by fractal scaling. The fractional order $\alpha \in (0,1]$ captures memory effects while the fractal dimension $\beta \in (0,1]$ accounts for irregular contact networks. We prove global existence, uniqueness, positivity, and boundedness of solutions via fixed-point arguments and establish global Ulam–Hyers stability. An adapted second-order Adams–Bashforth–Moulton predictor-corrector scheme with explicit weights is derived and verified. Numerical simulations across representative $(\alpha ,\beta )$ pairs reveal that decreasing either parameter delays epidemic peaks, reduces peak intensity (with $\beta$ exerting a stronger damping effect), prolongs tails, and induces irregular oscillations—features absent from classical or pure-fractional SEIR models. These results provide a rigorous and reproducible framework for forecasting emerging infections in heterogeneous populations and carry direct implications for targeted public health interventions. Full article

This study introduces a Caputo fractional-order version of the SEIRS-V model to investigate the spreading dynamics of worms within wireless sensor networks. Traditional integer-order worm propagation models describe the instantaneous evolution of network states; however, they do not adequately account for memory and hereditary characteristics that may influence the transmission dynamics. Consequently, their ability to represent realistic network behavior can be limited in systems where past states affect current propagation patterns. The framework divides sensor nodes into susceptible, exposed, infectious, recovered, and vaccinated classes, while explicitly incorporating worm transmission rates, temporary loss of immunity, and the impact of preventive security measures under limited resource conditions. A detailed theoretical examination is performed, covering the existence, boundedness, and uniqueness of solutions of the fractional-order system. The coupled nonlinear fractional system is solved semi-analytically by means of the Fractional Reduced Differential Transform (FRDT) technique. To confirm accuracy and robustness, the identical system is also discretized and solved using the finite difference scheme (FDS). Unlike previous studies on worm propagation models in wireless sensor networks, which are mainly limited to equilibrium point analysis and qualitative investigations without deriving explicit solutions, the present work develops an approximate semi-analytical solution for the fractional-order SEIRS-V system using the FRDTM. Comparisons between the two solution sets demonstrate excellent agreement and high precision. Numerical outcomes are presented through a series of 2D graphical profiles that illustrate the time-dependent behavior of each compartment and reveal the sensitivity of worm propagation and suppression to variations in the fractional order and key model parameters. The integrated theoretical and computational findings underscore the strong protective role of vaccination in mitigating worm outbreaks and offer valuable guidelines for strengthening cybersecurity measures in wireless sensor networks. Full article

This revised manuscript presents a real-calendar-anchored SEIR simulation benchmark for comparing manifold-constrained Gaussian process inference (MAGI) and physics-informed neural networks (PINNs). The study is explicitly positioned as an empirical benchmarking and reproducibility contribution rather than a new epidemic model or a new inference algorithm. A deterministic proportional SEIR system defines the mechanistic truth, while municipal surveillance records motivate the calendar and observation context. We compare full observation of $E, I, R$ with a missing–exposed regime in which only $I, R$ are observed. A parametric bootstrap with independent log-normal measurement noise generates an expanded ensemble ($B = 80$); this ensemble supports bootstrap medians, interquartile ranges, outlier assessment, and sensitivity analysis under the declared measurement-error model. The revision clarifies the role of the PINN data–physics weight $\lambda$, the oracle MAGI hyperparameter stabilization used in the missing-E experiment, the distinction between MAP estimates and Bayesian posterior uncertainty, and the operational role of PELT changepoint-guided sparse sampling. The results support a balanced conclusion: MAGI is stable in the fully observed setting, whereas PINNs can be competitive under appropriate $\lambda$ choices; the missing–exposed case remains ill-posed and requires cautious interpretation. Full article

An administrator-assisted CLP-SEIRS-T⁺ framework is developed to model malware propagation and urban cyber-resilience in clustered temporal communication networks. The model extends CLP-SEIRS-T by integrating community structure, predicted links, asynchronous node activation, and an endogenous defense layer in which administrator nodes remain infectable, recover faster than ordinary nodes, and trigger local patch diffusion when community-level prevalence exceeds a risk threshold. Unlike formulations that treat defense as an external or perfectly reliable safeguard, the proposed framework embeds administrator intervention directly within the epidemic state space and couples propagation dynamics with resilience-oriented performance measures, including safe functionality, absorptive capacity, spillover attenuation, recovery time, and service continuity. To keep experimental evidence scale-explicit, the validation is organized as a tiered protocol: a 48-node isolated virtual-machine cyber-range verifies safe mechanism realization; emulation-calibrated logical traces and pilot repeated comparisons examine trajectory behavior, pathway composition, and defense-component effects; and expanded numerical sweeps assess scalability, threshold sensitivity, alternative link-prediction scores, and adaptive-stress assumptions. The results show that direct links dominate local amplification, whereas predicted links contribute disproportionately to cross-community spillover. In the pilot comparison, the full CLP-SEIRS-T⁺ configuration achieves the best observed balance, reducing mean peak burden by 56.9%, shortening mean recovery time by 86.7%, increasing absorptive capacity by 37.1%, and improving service continuity by 12.0% relative to the no-intervention baseline. Larger-network sweeps over $N=48,100,150,200,$ and 500 logical hosts preserve the same qualitative mechanism ordering while keeping functionality error below 0.02. Threshold analysis indicates that intermediate trigger values provide a better burden–cost balance than either overly aggressive or delayed patching. Link-score comparisons show that local-neighborhood predictors yield consistent spillover interpretations, whereas degree-driven prediction can increase bridge exposure. Parameterized adaptive-stress tests further indicate that the mechanism remains beneficial under moderate stress but degrades under severe patch suppression, false telemetry, or intensified bridge seeking. These findings suggest that urban cyber-resilience depends jointly on network modularity, temporal availability, structurally likely bridge formation, state-dependent local defense, and the integrity of administrative response. Full article

This study examines how urban transportation systems influence the spatial spread of infectious diseases by developing a modified Susceptible–Exposed–Infected–Recovered (SEIR) model with explicit intercity travel dynamics. The model distinguishes between two mobility mechanisms: travel volume, represented by the departure rate g, and travel speed, represented by the arrival rate $\alpha$. Using the next-generation matrix (NGM) approach, we derive the basic reproduction number $R_0$ and analyse how within-city and transit-phase transmission contribute to epidemic spread. The results show that travel volume and travel speed affect mobility-driven transmission through distinct mechanisms. Increasing g increases the number of travelers entering the transit system and therefore amplifies the aggregate number of transit-mediated infections, although the per-capita transit reproduction expression is governed primarily by $\alpha$ and ${\beta }_d^T$ under the reduced next generation matrix formulation formulation. By contrast, increasing $\alpha$ shortens the time spent in transit, reduces the exposure window during travel, and lowers the per-capita contribution of transit-based infection to $R_0$. Numerical simulations illustrate these effects and support the conclusion that reducing travel volume can mitigate intercity epidemic spread by decreasing the number of potentially exposed travelers. Comparative case studies for Brazil, New Zealand, China, and Algeria are used to evaluate the model under different epidemiological settings and socioeconomic contexts. These socioeconomic indicators are treated as contextual background rather than as direct inputs to the mathematical model. The qualitative predictions of the ordinary differential equation (ODE) model are further cross-validated using an agent-based simulation implemented in NetLogo. Overall, the study shows that separating travel volume from travel speed provides a more precise understanding of mobility-driven disease transmission and can support the design of targeted travel-related control measures. Full article

We present a novel framework for epidemiological disease spread modeling that combines agent-based simulation with Boolean state-space representations and optimal filtering for state estimation under noisy observations. Our approach models individual agents in discrete Susceptible-Exposed-Infected-Recovered (SEIR) states using a compact 2-bit Boolean representation, with agent interactions governed by scheduled contact patterns. To address the challenge of inferring latent infection states from limited and noisy testing data, we develop two complementary inference approaches: (1) a Boolean Kalman particle filter for small populations that tracks the full joint distribution over agent states, and (2) a mean-field approximation for large populations that factorizes the posterior into independent marginal distributions, enabling scalability to realistic population sizes. Unlike continuous-state Kalman filters, our methods naturally handle the discrete nature of epidemiological states while accommodating realistic observation models where only a subset of agents are tested at each time step, with test results subject to false positive and false negative errors. We demonstrate that this framework enables accurate reconstruction of population-level infection dynamics and individual agent states from sparse, noisy observations across populations from 100 to 50,000 agents, providing a computationally tractable approach for real-time epidemic monitoring. Full article

Infectious disease transmission is a complex dynamic process governed by intrinsic causal mechanisms rather than simple statistical correlations. Although deep learning paradigms have demonstrated powerful nonlinear representation capabilities, their “black-box” and purely data-driven nature often lead to a severe lack of causal consistency and logical transparency. To bridge this gap, this paper proposes CCSANet (Causally Constrained SEIR-Aware Network), an interpretable forecasting framework that seamlessly embeds epidemiological priors directly into the neural architecture. The model integrates SEIR dynamics into a temporal causal discovery framework, utilizing a mechanism-aware prior loss to guide a CausalFormer in learning a global temporal causal graph from multi-source heterogeneous data. This ensures that the identified relationships strictly adhere to the fundamental evolutionary logic of contagion. Subsequently, the extracted causal subgraphs are encoded as structural priors within a Causal-SCI-Block via a specialized masking mechanism, effectively forcing information to propagate exclusively along epidemiologically legitimate pathways. To ensure deep alignment between neural representations and physical reality, a causal strength alignment loss is introduced to synchronize the network’s attention weights with actual transmission intensities. Experimental evaluations on real-world multi-city datasets demonstrate that this integrated approach significantly outperforms baselines such as LSTM, Informer, and its predecessor, ESASNet. Under a 7-day sliding window configuration, the model maintains a Coefficient of Determination $R^2$ stably above 0.97, achieving an accuracy improvement of 5.5% to 6.2% and an 8% to 10% reduction in SMAPE, thereby demonstrating that coupling causal discovery with SEIR constraints substantially enhances both predictive precision and physical interpretability. Full article

Avian influenza (AI) remains a serious threat to poultry and public health worldwide due to its zoonotic nature and pandemic potential. This paper develops and analyzes a coupled system of nonlinear ordinary differential equations and an SEIR-SEIR model that describes the transmission dynamics of avian influenza in both human and bird populations. The model incorporates multiple transmission routes (bird-to-bird, bird-to-human, human-to-human), exposed/latent compartments in both hosts, disease-induced mortality, and demographic processes. From a mathematical perspective, we present a rigorous analysis of this eight-dimensional dynamical system. We prove positivity and boundedness of solutions in ${\mathbb{R}}_{+}^8$, characterize the equilibrium points, and derive the basic reproduction numbers $R_0^b$ and $R_0^h$ using the next-generation matrix method. Local asymptotic stability of the disease-free equilibrium is established via the Routh–Hurwitz criterion. A composite Lyapunov function is constructed to prove global asymptotic stability when both reproduction numbers are less than unity—a result that exploits the cascade structure of the system and provides a template for analyzing similar multi-host models. Sensitivity analysis using normalized forward sensitivity indices identifies critical parameters. In addition, we use neural network models to validate both models and provide error analysis. These results emphasize the crucial role of controlling cross-species transmission and improving recovery efforts, which have significant implications for the design of effective intervention and surveillance programs in the context of the One Health framework. Full article

In this paper, a sugarcane pokkah boeng SEIR model with time delay due to removal or chemical treatment of diseased sugarcane plants is investigated. By analyzing the characteristic equations, the stability of each feasible equilibrium of the system is discussed, and the existence of a Hopf bifurcation at the positive equilibrium is established. Furthermore, by choosing the delay as a bifurcation parameter, we show that Hopf bifurcations can occur as $\tau$ crosses some critical values. Meanwhile, we adopt a hierarchical Bayesian model to conduct statistical inference on time delay and combine it with the former to carry out an empirical analysis on the prevention and control of sugarcane pokkah boeng. These provide a more comprehensive and effective theoretical basis for decision-making in the prevention and control of sugarcane pokkah boeng. Numerical simulations are carried out to illustrate the main theoretical results. Full article

Measles remains one of the most contagious infectious diseases globally and continues to pose substantial public health risks despite decades of effective vaccination. This narrative review examines both classical and contemporary computational approaches used for measles monitoring, prediction, and control, with particular attention given to the emerging role of artificial intelligence (AI). We synthesized findings from 46 studies; 31 focused directly on measles and 15 on methodologically relevant studies from related infectious diseases (COVID-19, influenza, malaria), selected through searches of PubMed, Scopus, Web of Science, IEEE Xplore, and preprint servers, conducted between June and December 2025. Traditional compartmental models (SIR, SEIR, MSEIR), statistical tools (ARIMA, SARIMA), and seroepidemiological analysis provide transparent, well-characterized frameworks for estimating transmission dynamics and simulating intervention scenarios. Spatial modeling, network analysis, and Monte Carlo simulations have added geographic granularity to outbreak characterization. More recently, AI and machine learning (ML) methods, including supervised algorithms (Random Forest, XGBoost, SVM), deep learning architectures (CNN, LSTM), and hybrid mechanistic ML models, have shown improved predictive performance by integrating multiple data sources: epidemiological records, demographic profiles, mobility patterns, and behavioral indicators. AI-based approaches appear most valuable for high-dimensional risk prediction and image-based diagnostic tasks, while classical models retain clear advantages for policy-oriented scenario analysis. However, no AI-based or hybrid model identified in this review has been adopted into routine national measles surveillance or used for vaccination policy decisions at scale. Important challenges remain: data quality varies across settings, model generalizability cannot be assumed, and computational infrastructure disparities limit deployment in high-burden regions. Explainable AI, federated learning, workforce training for model interpretation, and integration of vaccination registries with mobility and genomic surveillance data represent concrete future directions for strengthening computational support for measles elimination. Full article

In the existing research on information propagation modeling in social networks, hypergraphs have been widely applied to characterize the high-order interaction relationships involving multiple nodes. However, most models are still based on the assumption of undirected connections, which leads to certain limitations in depicting the information flow direction and the structural characteristics of propagation chains. To address the above problems, a social hypernetwork propagation model with directional constraints is constructed in this paper by introducing the directed hypergraph structure and combining it with the improved SEIR model. The strength of social relationships is measured by intimacy in the model, and a comprehensive characterization of the information propagation process is achieved by integrating the threshold mechanism of the directed hypergraphs with the attenuation function of information timeliness. In addition, the effectiveness of the proposed model is verified by taking the event of “imposing additional tariffs” as an example, and the evolutionary characteristics of propagation in different network structures, as well as the impacts of user confidence and information timeliness, are analyzed using simulation experiments. The results indicate that the model is applicable to characterizing the information propagation trends and dynamic characteristics in real social networks, and can provide theoretical references and methodological support for the prediction and regulation of network public opinion. Full article

Whooping cough has re-emerged as a significant global public health concern. Hence, an SEIRS model for whooping cough transmission in Japan is proposed to capture the disease dynamics because of a strong resurgence of the epidemic. The model is analyzed mathematically, establishing the non-negativity and boundedness of its solutions and investigating both the disease-free and endemic equilibria with their local and global stability. The model is fitted to actual infection data by estimating the time-varying transmission rates using a Long Short-Term Memory (LSTM) network and calibrating vaccination and treatment rates via Latin Hypercube Sampling (LHS). Sensitivity analysis identifies the key parameters for optimal control, and results indicate that simultaneously enhancing the vaccination rate most effectively mitigates the epidemic, as supported by cost-effectiveness analysis. Full article

Currently, epidemiological models can not only be found in epidemiology but also in other research disciplines. However, an interdisciplinary perspective that highlights the commonalities of epidemiological models across disciplines is missing. The goal of the current study is to foster such a perspective. To this end, a methodology is used that sets the current study apart from traditional review studies. Two benchmark epidemiological models formulated in terms of coupled ordinary differential equations, the susceptible–infected–recovered model and the susceptible–exposed–infected–recovered model, are followed through eight disciplines: epidemiology, virus dynamics within humans, computer viruses, drug addiction, voter dynamics, rumor spreading, sales dynamics, and viral marketing. Structural similarities and structural differences across these disciplines within the context of these two models are worked out. It is shown how the exact same mathematical structure can be applied for quite different interpretations across the selected disciplines. It is also shown that more complex model variants exhibit structural differences across research disciplines. In this way, this study helps researchers compare their own works on a structural level with related works in other disciplines. The particular importance of the current study is that it can boost progress in epidemiological modeling by making researchers aware of an interdisciplinary perspective. Full article