Search Results (224)

Consider an evolving epidemic in which each person is either (S) susceptible and healthy; (E) exposed, contagious but asymptomatic; (I) infected, symptomatic, and quarantined; or (R) recovered, healthy, and susceptible. The inference problem, given (i) who is showing symptoms (I) and who is not (S, E, R) and (ii) the distribution of meetings among people each day, is to predict the number of infected people (state I) in future days (e.g., 1 through 20 days out into the future) for the purpose of planning resources (e.g., needles, medicine, staffing) and policy responses (e.g., masking). Each prediction horizon has different uses. For example, staffing may require forecasts of only a few days, while logistics (i.e., which supplies to order) may require a two- or three-week horizon. Our algorithm and system EpiInfer is a non-Markovian approach to forecasting infection rates. It is non-Markovian because it looks at infection rates over the past several days in order to make predictions about the future. In addition, it makes use of the following information: (i) the distribution of the number of meetings per person and (ii) the transition probabilities between states and uses those estimates to forecast future infection rates. In both simulated and real data, EpiInfer performs better than the standard (in epidemiology) differential equation approaches as well as general-purpose neural network approaches. Compared to ARIMA, EpiInfer is better starting with 6-day forecasts, while ARIMA is better for shorter forecast horizons. In fact, our operational recommendation would be to use ARIMA (1,1,1) for short predictions (5 days or less) and then EpiInfer thereafter. Doing so would reduce relative Root Mean Squared Error (RMSE) over any state of the art method by up to a factor of 4. Predictions of this accuracy could be useful for people, supply, and policy planning. Full article

This paper investigates a stochastic semi-parametric SEIR model characterized by infectivity during the incubation period and influenced by white noise perturbations. First, based on the theory of stochastic persistence, we derive the conditions required for the disease to persist within the model. Under these conditions, we apply Khasminskii’s ergodic theorem and Lyapunov functions to establish that the model possesses a unique ergodic stationary distribution. Finally, we utilize Khasminskii’s periodic theorem to examine the corresponding stochastic periodic SEIR model derived from the stochastic semi-parametric SEIR model, identifying sufficient conditions for the existence of non-trivial periodic solutions. Our theoretical results are further validated through numerical simulations. Full article

The management of public health projects in a BANI (brittle, anxious, non-linear, incomprehensible) environment, exemplified by the ongoing war in Ukraine, presents unprecedented challenges due to fragile systems, heightened uncertainty, and complex socio-political dynamics. This study proposes an AI-driven framework to enhance the resilience and effectiveness of public health interventions under such conditions. By integrating a coupled SEIR–Infodemic–Panicdemic Model with war-specific factors, we simulate the interplay of infectious disease spread, misinformation dissemination, and panic dynamics over 1500 days in a Ukrainian city (Kharkiv). The model incorporates time-varying parameters to account for population displacement, healthcare disruptions, and periodic war events, reflecting the evolving conflict context. Sensitivity and risk–opportunity analyses reveal that disease transmission, misinformation, and infrastructure damage significantly exacerbate epidemic peaks, while AI-enabled interventions, such as fact-checking, mental health support, and infrastructure recovery, offer substantial mitigation potential. Qualitative assessments identify technical, organisational, ethical, regulatory, and military risks, alongside opportunities for predictive analytics, automation, and equitable healthcare access. Quantitative simulations demonstrate that risks, like increased displacement, can amplify infectious peaks by up to 28.3%, whereas opportunities, like enhanced fact-checking, can reduce misinformation by 18.2%. These findings provide a roadmap for leveraging AI to navigate BANI environments, offering actionable insights for public health practitioners in Ukraine and other crisis settings. The study underscores AI’s transformative role in fostering adaptive, data-driven strategies to achieve sustainable health outcomes amidst volatility and uncertainty. Full article

Despite initial changes in respiratory illness epidemiology due to SARS-CoV-2, influenza activity has returned to pre-pandemic levels, highlighting its ongoing challenges. This paper investigates an influenza epidemic model using a Susceptible-Exposed-Infected-Recovered (SEIR) framework, extended with fuzzy Atangana–Baleanu–Caputo (ABC) fractional derivatives to incorporate uncertainty (via fuzzy numbers for state variables) and memory effects (via the ABC fractional derivative for non-local dynamics). We establish the theoretical foundation by defining the fuzzy ABC derivatives and integrals based on the generalized Hukuhara difference. The existence and uniqueness of the solutions for the fuzzy fractional SEIR model are rigorously proven using fixed-point theorems. Furthermore, we analyze the system’s disease-free and endemic equilibrium points under the fractional framework. A numerical scheme based on the fractional Adams–Bashforth method is used to approximate the fuzzy solutions, providing interval-valued results for different uncertainty levels. The study demonstrates the utility of fuzzy fractional calculus in providing a more flexible and potentially realistic approach to modeling epidemic dynamics under uncertainty. Full article

Over the past decade, the rapid growth of supply chain finance (SCF) in developing countries has made it a key profit driver for commercial banks and financial firms. In parallel, financial risk control in SCF has attracted more and more attention from financial service providers and has gained research momentum in recent years. This study analyzes the contagion mechanism of SCF-related risks faced by commercial banks through examining SCF network topology. First, this study uses complex network theory to integrate an SEIR epidemic model (Susceptible–Exposed–Infectious–Recovered) into financial risk management. The model simulates how financial risks spread in supply chain finance (SCF) under banks’ strategic, tactical, or operational interventions. Then, some key points for financial risk control from the perspective of commercial banks are obtained by investigating the risk stability threshold of the financial network of SCF and its stability. Numerical simulations show that effective interventions—such as strengthening loan guarantees to reduce the number of exposed firms—significantly curb risk transmission by restricting its scope and shortening its duration. This research provides commercial banks with a quantitative framework to analyze risk propagation and actionable strategies to optimize SCF risk control, enhancing financial system stability and offering practical guidance for preventing systemic risks. Full article

Epidemics spread through shipping networks and have dual characteristics as both biological sources of infection and triggers of cascading failures. However, existing resilience models fail to capture this dual and coupled dynamics. To minimize the cascading impacts of epidemics on global shipping networks, this paper proposes an innovative resilience assessment framework that considers the interaction between epidemic transmission and the shipping network cascading failure. First, a weighted shipping network topology is constructed based on route flow characteristics to quantify route frequency, stopping time, and the number of infected people, and the epidemic transmission across ports is modeled with an improved SEIR model, which contains a heterogeneous infectivity function and a dynamic transmission matrix, revealing a dual transmission mechanism inside and outside the ports. Second, a two-stage cascading failure model is developed: a direct failure triggered by infected people exceeding the threshold and an indirect failure triggered by the dynamic redistribution of loads. The load redistribution strategy is optimized to reconcile the residual port capacity and the risk of infection. Finally, a multidimensional resilience assessment framework covering structural destruction resistance, network efficiency, path redundancy, and a cascading failure propagation rate is constructed. Example validation shows that the improved load redistribution strategy reduces the maximum connected subgraph decay rate by 68.2%, reduces the cascading failure rate by 88%, and improves the peak network efficiency by 128.2%. In case of multi-source epidemics, the state of the network collapse can be shortened by 12 days if the following recovery strategy is adopted: initially repair high connectivity hubs (e.g., Port of Shanghai), and then repair high centrality nodes (e.g., Antwerp Port) to achieve a balance between recovery efficiency and network functionality. The research results reduce the risk of systemic disruptions in maritime networks and provide decision-making tools for dynamic port scheduling during pandemics. Full article

Faced with mounting pressure to achieve high-quality green transformation, manufacturing enterprises are increasingly scrutinized for greenwashing behaviors. This study develops a novel hybrid modeling framework that combines evolutionary game theory with the SEIR epidemic model to investigate the dynamic interactions between environmental regulation, media pressure, and green innovation behavior. The model captures how strategic decisions among boundedly rational actors evolve over time under dual external pressures. Simulation results show that stronger environmental regulatory intensity accelerates the adoption of substantive green innovation and concurrently reduces the media pressure associated with greenwashing. Moreover, while social media disclosure has a limited impact during the early stages of greenwashing information diffusion, its influence becomes significantly amplified once a critical dissemination threshold is surpassed, rapidly transforming latent information into widespread public concern. This amplification triggers significant public opinion pressure, which, in turn, incentivizes local governments to enforce stricter environmental policies. The findings reveal a synergistic governance mechanism where environmental regulation and media scrutiny jointly curb greenwashing and foster genuine corporate sustainability. Full article

This study proposes and examines the dynamics of a susceptible–exposed–infectious–recovered (SEIR) model for the spread of seasonal influenza. The population is categorized into four distinct groups: susceptible (S), exposed (E), infectious (I), and recovered (R) individuals. The symmetric model integrates a bilinear incidence rate alongside a nonlinear recovery rate that depends on the quality of healthcare services. Additionally, it accounts for the impact of fear related to the disease and includes a constant vaccination rate as well as a nonlinear treatment function. The model advances current epidemiological frameworks by simultaneously accounting for these interrelated mechanisms, which are typically studied in isolation. We derive the expression for the basic reproduction number and analyze the essential stability properties of the model. Key analytical results demonstrate that the system exhibits rich dynamic behavior, including backward bifurcation (where stable endemic equilibria persist even when the basic reproduction number is less than one) and Hopf bifurcation. These phenomena emerge from the interplay between fear-induced suppression of transmission, treatment saturation, and healthcare quality. Numerical simulations using Saudi Arabian demographic and epidemiological data quantify how increased fear perception shrinks the bistability region, facilitating eradication. Healthcare capacity improvements, on the other hand, reduce the critical reproduction number threshold while treatment accessibility suppresses infection loads. The model’s practical significance lies in its ability to identify intervention points where small parameter changes yield disproportionate control benefits and evaluate trade-offs between pharmaceutical (vaccination/treatment) and non-pharmaceutical (fear-driven distancing) strategies. This work establishes a versatile framework for public health decision making and the integrated approach offers policymakers a tool to simulate combined intervention scenarios and anticipate nonlinear system responses that simpler models cannot capture. Full article

This study simulates the spread of epidemics on university campuses using a multilayer temporal network model combined with the SEIR (Susceptible–Exposed–Infectious–Recovered) transmission model. The proposed approach explicitly captures the time-varying contact patterns across four distinct layers (Rest, Dining, Activity, and Academic) to reflect realistic student mobility driven by class schedules and spatial constraints. It evaluates the impact of various intervention measures on epidemic spreading, including subnetwork closure and zoned management. Our analysis reveals that the Academic and Activity layers emerge as high-risk transmission hubs due to their dynamic, high-density contact structures. Intervention measures exhibit layer-dependent efficacy: zoned management is highly effective in high-contact subnetworks, its impact on low-contact subnetworks remains limited. Consequently, intervention measures must be dynamically adjusted based on the characteristics of each subnetwork and the epidemic situations, with higher participation rates enhancing the effectiveness of these measures. This work advances methodological innovation in temporal network epidemiology by bridging structural dynamics with SEIR processes, offering actionable insights for campus-level pandemic preparedness. The findings underscore the necessity of layer-aware policies to optimize resource allocation in complex, time-dependent contact systems. Full article

The rapid expansion of economic globalization and trade has led to a sharp increase in the shipping investment risks currently faced by cities around the world. This study aims to explore the risk warning mechanism of shipping input under public health events to establish an effective risk warning method. This would enable the rapid identification of potential risk inputs and the implementation of targeted prevention and control measures to ensure public health and safety. This study investigates the mechanisms of both intra-regional and cross-border risk transmission within shipping networks. It establishes a transmission dynamics model (termed the SEIR-SEI model) incorporating climatic, economic, and health factors to analyze the potential inherent risks of regional shipping nodes. It uses the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) model, modified by the entropy weight method, to calculate the importance of nodes in the shipping network. This comprehensive approach considers the network’s clustering coefficient, betweenness centrality, closeness centrality, degree centrality, and eigenvector centrality. To validate the practicality of the model, this study selects shipping data with Shanghai, China, as the destination node to conduct simulation computations of different risk propagation chains. The findings demonstrate that overall risk transmission is determined by the joint influence of a node’s inherent risks and propagation probabilities. This study not only clarifies the process of cross-border transmission of public health events through the shipping network between cities of different countries, but also provides insights for the application of shipping input risk assessment systems, enriching the academic research on logistics network propagation. Full article

In this work, we employ two modeling approaches—a mean-field model and a network model—for the purpose of modeling respiratory infection outbreaks in Russia. The presented approaches and their software implementation combine heterogeneity and structural simplicity and, in this sense, they close the gap between the compartmental SEIR models and complex detailed solutions based on agent-based approaches—the two most common modeling techniques for influenza and COVID-19 dynamics. The mathematical description of the approaches is presented, with SEIR compartmental model serving as a baseline for comparison. The experiments demonstrate the similarity of the modeling output of the presented approaches, which allows their interchangeable usage in replicating real outbreak dynamics in Russian cities. The ability of the discussed approaches to mimic data from Russian epidemic surveillance is shown by fitting a mean-field model to data from an influenza outbreak in Saint Petersburg in 2014–2015. The comparison of model complexity and their performance is made using synthetic scenarios. Following the results of numerical experiments, the comparative advantages and drawbacks of the approaches in the application to respiratory infection outbreaks are discussed. The presented modeling techniques, in addition to classical SEIR models and agent-based models as a part of epidemic surveillance, allow one to select the best modeling option for any particular task in outbreak surveillance and control, based on the computational resources at hand, data availability, and data quality. Full article

In the post-COVID-19 era, the dynamic spread of COVID-19 poses new challenges to epidemiological modelling, particularly due to the absence of large-scale screening and the growing complexity introduced by immune failure and reinfections. This paper proposes an AEIHD (antibody-acquired, exposed, infected, hospitalised, and deceased) model to analyse and predict COVID-19 transmission dynamics in the post-COVID-19 era. This model removes the susceptible compartment and combines the recovered and vaccinated compartments into an “antibody-acquired” compartment. It also introduces a new hospitalised compartment to monitor severe cases. The model incorporates an antibody-acquired infection rate to account for immune failure. The Extended Kalman Filter based on the AEIHD model is proposed for real-time state and parameter estimation, overcoming the limitations of fixed-parameter approaches and enhancing adaptability to nonlinear dynamics. Simulation studies based on reported data from Australia validate the AEIHD model, demonstrating its capability to accurately capture COVID-19 transmission dynamics with limited statistical information. The proposed approach addresses the key limitations of traditional SIR and SEIR models by integrating hospitalisation data and time-varying parameters, offering a robust framework for monitoring and predicting epidemic behaviours in the post-COVID-19 era. It also provides a valuable tool for public health decision-making and resource allocation to handle rapidly evolving epidemiology. Full article

In this article, the validity of the comparison principle (CP) for weakly coupled quasi-linear cooperative systems with delays is proven. This is a powerful tool for studying the qualitative properties of the solutions. The CP is crucial in the proofs of the existence and uniqueness of weak solutions to cooperative reaction–diffusion systems presented here. Other direct consequences of the CP are the stability of the solution, the attenuation of long time periods, etc. An example model is given by spatial SEIR models with delays. They are suitable for modeling disease spread in space and time and can be described using a weakly coupled cooperative reaction–diffusion system. In this paper, spatial SEIR models with delays are considered in a continuous space. The emphasis is on the qualitative properties of the solutions, which are important for providing a mathematical basis for the model. Full article

Phenomenological models are highly effective tools for forecasting disease dynamics using real-world data, particularly in scenarios where detailed knowledge of disease mechanisms is limited. However, their reliability depends on the model parameters’ structural and practical identifiability. In this study, we systematically analyze the identifiability of six commonly used growth models in epidemiology: the generalized growth model (GGM), the generalized logistic model (GLM), the Richards model, the generalized Richards model (GRM), the Gompertz model, and a modified SEIR model with inhomogeneous mixing. To address challenges posed by non-integer power exponents in these models, we reformulate them by introducing additional state variables. This enables rigorous structural identifiability analysis using the StructuralIdentifiability.jl package in JULIA. We validated the structural identifiability results by performing parameter estimation and forecasting using the GrowthPredict MATLAB Toolbox. This toolbox is designed to fit and forecast time series trajectories based on phenomenological growth models. We applied it to three epidemiological datasets: weekly incidence data for monkeypox, COVID-19, and Ebola. Additionally, we assessed practical identifiability through Monte Carlo simulations to evaluate parameter estimation robustness under varying levels of observational noise. Our results confirm that all six models are structurally identifiable under the proposed reformulation. Furthermore, practical identifiability analyses demonstrate that parameter estimates remain robust across different noise levels, though sensitivity varies by model and dataset. These findings provide critical insights into the strengths and limitations of phenomenological models to characterize epidemic trajectories, emphasizing their adaptability to real-world challenges and their role in informing public health interventions. Full article

Port congestion leads to frequent delays in multilevel handlings at automated terminals (ATMH). These delays propagate throughout the terminal, intensified by the interdependencies among equipment, which severely undermines the overall efficiency of the port. To elucidate the characteristics of ATMH and to investigate the dynamics of delay propagation, this study employs causal analysis methods applied to a U-shaped automated terminal multilevel handling system. By integrating the Minimum Redundancy Maximum Relevance (mRMR) algorithm with multivariate transfer entropy, we propose a novel approach to develop an interactive influence network for a U-shaped automated container terminal. Furthermore, this research develops a delay propagation model that accounts for equipment withdrawal mechanisms. The simulation results indicate that the multilevel handling system exhibits a certain degree of randomness, with close interaction between Automated Guided Vehicles and yard cranes. Measures that involve the withdrawal of propagating equipment and the implementation of immunity control on critical equipment can significantly mitigate the spread of delays. This study broadens the methodological framework for existing research on multilevel handling systems at automated terminals, exploring the operational characteristics and propagation patterns of delays. Such insights will assist terminals in implementing effective governance strategies when confronted with delays induced by uncertain factors, thereby reducing the risk of delay propagation and enhancing overall operational efficiency. Full article