Search Results (2,330)

A suicide epidemic exists among young U.S. veterans, with risk especially elevated in the first year of transition for the 200,000 servicemembers exiting the military annually. The VA Veteran Sponsorship Initiative (VSI) is a public–private-partnership between federal and community partners that aims to decrease suicides by providing a VA-certified volunteer peer sponsor and connection to community services. Onward Ops is a key community-based national program that enrolls, matches and manages the relationship between servicemembers and sponsors. A prior randomized controlled trial showed that the effectiveness of community interventions can be enhanced when augmented by an Onward Ops sponsor. In preparation for national implementation, we conducted a quasi-experimental, matched-cohort pilot to evaluate the feasibility of an adapted VSI protocol and then assessed effectiveness. The adaptations were executed using the Framework for Reporting Adaptations and Modifications-Enhanced between April 2021 and April 2023. The formative results supported the feasibility of the adaptations to enable proactive enrollment on military installations and expand data infrastructure, partnerships, peer sponsors, and VA clinical services. We then assessed the effectiveness for outcomes not studied in the original VSI trial for active-duty soldiers who enrolled between April and December 2023. After nearest-neighbor matching, the sample included 551 VSI participants and 551 soldiers transitioning as usual. The point-probability contrast or risk differences from the conditional logistic regression model indicated that the VSI caused a statistically significant increase in VA primary care utilization of 0.198 and a statistically significant decrease in suicide attempts of −0.019, both assessed 10 months post-military discharge. The study demonstrated the utility of public–private-partnerships, peer-sponsorship programs and enhanced VA services to support servicemembers during transition. Full article

Solving constrained multiobjective optimization problems (CMOPs) is highly challenging due to the presence of complicated feasible regions, intense conflicts among objectives, and unevenly distributed constraints. As a result, conventional methods relying on a single constraint-handling mechanism frequently fail to maintain a stable equilibrium among solution feasibility, diversity, and convergence. To overcome these bottlenecks, this article introduces AFFCMO, a novel adaptive feasibility-guided framework tailored for constrained multiobjective optimization. At its core, the proposed approach utilizes a coevolutionary dual-population architecture that divides the search process into two distinct tasks. Specifically, an auxiliary population is tasked with global exploration, while a primary population focuses on the intensive exploitation of discovered feasible areas. To achieve this, the primary population leverages a DE/current-to-pbest/1 differential evolution strategy to closely approximate the constrained Pareto front. Simultaneously, the auxiliary population expands the search space using a mutation operator that adapts to the current evolutionary stage. Furthermore, exploration is bolstered by a multicriterion environmental selection scheme designed for the auxiliary group. By combining Euclidean geometric distributions, constraint relaxation, and value modeling inspired by epidemic dynamics, this strategy successfully preserves valuable infeasible solutions that can guide the search. Additionally, a dynamic resource allocation strategy based on historical search feedback and Thompson sampling is incorporated. This mechanism continuously evaluates the recent search contributions of both populations and adaptively adjusts their offspring sizes, thereby reducing the bias introduced by static allocation schemes. This mechanism continuously assesses the actual search contributions of both populations, allowing for the adaptive resizing of offspring generations and thereby eliminating the inherent biases of static allocation methods. Comprehensive empirical evaluations are conducted on 47 benchmark problems from four distinct test suites. The results indicate that AFFCMO significantly outperforms seven contemporary multiobjective evolutionary algorithms in terms of exploring complex feasible regions, preserving solution diversity, and achieving high convergence accuracy. Full article

Coronavirus infections pose a significant threat to both human and animal health, causing widespread morbidity, mortality, and substantial economic losses. While vaccines are crucial for prevention, their efficacy is often limited by the high mutation rate of these viruses. This underscores the urgent need for anti-coronavirus drugs, particularly broad-spectrum antiviral agents. In this study, we demonstrated for the first time that Biochanin A (BCA), a bioactive isoflavonoid found in legumes, exhibits broad-spectrum antiviral activity against coronaviruses. BCA potently inhibits porcine epidemic diarrhea virus (PEDV), as well as human coronaviruses HCoV-OC43 and HCoV-229E in vitro, with EC₅₀ values of 6.90, 2.80 and 15.4 μM, respectively. In a lethal mouse model of HCoV-OC43-induced encephalitis, oral administration of BCA (40–60 mg/kg) significantly improved animal survival and reduced cerebral viral loads. Mechanistic studies revealed that BCA upregulates the AMPK/Nrf2 signaling pathway, thereby increasing expression of the glutamate-cysteine ligase catalytic subunit (GCLC) and enhancing glutathione (GSH) biosynthesis. Our findings identify BCA as a promising host-directed antiviral agent and highlight its therapeutic potential against coronavirus infections. Full article

In recent years, fractional HIV models have received increasing attention. This study derives a fractional HIV model using the continuous-time random walk (CTRW) method, endowing the mathematical model with physical significance. Based on the transmission characteristics of HIV, the proposed model considers extrinsic infectivity, intrinsic infectivity, and drug control, specifically as follows: the extrinsic infectivity is a constant independent of the infection time; the intrinsic infectivity is a power-law function that depends on drug efficacy and infection time; the drug efficacy rate follows a Mittag–Leffler distribution with a long-term effect. Based on these considerations, a fractional HIV model with drug control is established in this paper. In addition, the global asymptotic stability of the equilibrium and the sensitivity analysis of the basic reproduction number $R_0$ are studied, and the theoretical results are verified by numerical simulations. The results show that reducing extrinsic infectivity, controlling intrinsic infectivity, and the drug efficacy rate are crucial in controlling the spread of HIV. Full article

The high number of deaths and serious injuries in traffic crashes can be considered a silent global epidemic, as it is still understood by part of society as an inherent consequence of road traffic. There are several risk factors that can increase the occurrence or severity of crashes on roads, acting alone or in combination. Road safety diagnoses based on facts and evidence are essential for improving public policies to reduce victims. With the aim of assisting in these diagnoses and since the official database on these victims is not made available in detail to the public, this work investigates the relationship between seven indicators, collected in field research and in public databases, and the occurrence and fatality of traffic victims in the City of Rio de Janeiro. Linear regression models are developed for each approach and the one with the best statistical parameters is chosen. The model with greater robustness demonstrated that helmet non-use, the density of traffic enforcement cameras, and illiteracy together explain a significant portion of the variation in the fatality rate. The results are considered satisfactory, since a limited number of existing risk factors for road safety were used. Full article

Measles remains a significant public health threat despite widespread vaccination, with recent resurgences driven by vaccine hesitancy and coverage gaps. Existing mathematical models often fail to capture the substantial temporal heterogeneity in incubation periods, vaccine-induced protection, and recovery processes that characterize measles transmission. We develop and analyze an SVEIR epidemic model incorporating four independent distributed time delays with exponential survival factors, capturing the realistic variability in these epidemiological processes. The model features compartment-specific mortality rates, disease-induced mortality, and imperfect vaccination with failure probability $\theta$. Using next-generation matrix methods adapted for delay kernels, we derive the delay-dependent reproduction number ${\mathcal{R}}_0^d$ and prove, via systematic construction of Volterra-type Lyapunov functionals, that it constitutes a sharp threshold: the disease-free equilibrium is globally asymptotically stable when ${\mathcal{R}}_0^d\le 1$, while a unique endemic equilibrium emerges and is globally stable when ${\mathcal{R}}_0^d>1$. Normalized forward sensitivity analysis reveals that the transmission rate $\beta$ and recruitment rate $\Lambda$ exhibit maximal positive elasticity, while the vaccination rate p, vaccine failure probability $\theta$, and incubation delay ${\tau }_3$ possess the largest negative elasticities. Critically, ${\tau }_3$ exerts exponential influence via $e^{-n_3{\tau }_3}$, making interventions that delay infectiousness—such as post-exposure prophylaxis—unusually potent. We derive an explicit expression for the critical delay ${\tau }_3^{\mathrm{cr}}$ at which ${\mathcal{R}}_0^d=1$, demonstrating that prolonging the effective incubation period sufficiently can shift the system from endemic persistence to extinction. Numerical simulations using Dirac delta kernels confirm all theoretical predictions. These findings provide three actionable insights for public health: (1) maintaining high vaccination coverage among new birth cohorts remains paramount; (2) improving vaccine quality (reducing $\theta$) yields substantial returns; and (3) the incubation delay represents a quantifiable, measurable target for evaluating the population-level impact of time-sensitive interventions. The framework is broadly applicable to infectious diseases characterized by significant temporal heterogeneity. 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

Dengue fever (DF) is an acute mosquito-borne infectious disease caused by dengue virus (DENV), primarily transmitted by Aedes aegypti and Aedes albopictus. Nearly 4 billion people worldwide are at risk of infection, and the 2024 epidemic reached an unprecedented scale. Severe cases can lead to hemorrhage, shock, and even death, prompting the WHO to classify it as a potential pandemic pathogen. Current prevention and control measures face prominent bottlenecks, including limited applicable populations for vaccines, lack of specific antiviral drugs, and increasing insecticide resistance in mosquito vectors. Notably, susceptible animal models serve as core tools for elucidating the pathogenic mechanisms of dengue virus, screening antiviral drugs, and evaluating vaccine protective efficacy, holding irreplaceable significance. This review systematically summarizes the characteristics, application scenarios, and research progress of mainstream and potential susceptible animal models, including non-human primates, mice, pigs, tree shrews, and bats. It covers model systems with different immune statuses, genetically modified types, and species-specific traits. Among these, mouse models are the most widely used due to their high flexibility and controllable cost, while non-human primate models have become key carriers for preclinical vaccine evaluation by virtue of their high homology with human immune responses. However, current models generally suffer from core bottlenecks, such as incomplete simulation of core severe phenotypes, insufficient restoration of immune mechanisms, unclear viral receptor mechanisms, and lack of unified standards for inoculation doses and evaluation indicators. These limitations make it difficult to accurately replicate key severe disease mechanisms, including antibody-dependent enhancement (ADE) and cytokine storms. Future model development should focus on core requirements—including intact immunity, broad-spectrum susceptibility, and accurate simulation of clinical pathological features—prioritize solving the simulation challenges of ADE and cytokine storms, and establish standardized experimental systems and evaluation criteria. By comprehensively summarizing the advantages and limitations of the existing models, this review provides a systematic reference for the optimization and upgrading of dengue virus-susceptible animal models. It also holds important guiding significance for promoting the in-depth development of basic dengue research, innovation in prevention and control technologies, and clinical transformation and application. Full article

Health emergencies linked to epidemic outbreaks in vulnerable contexts require rapid and effective architectural responses. Natural ventilation represents a key strategy for infection control and indoor comfort, yet traditional airflow calculation methods require climatic and construction data, which are often unavailable or incomplete. In emergency situations, this results in the inapplicability of such methods and creates a critical information gap. This study proposes a simplified equation to estimate airflow rate (Q) in single-sided and cross-ventilation configurations, based on openable surface area and a reference Effective Window Air Speed (EWAS). Two infectious disease treatment centers were modeled and simulated using EnergyPlus (E+) under five climatic scenarios—two real and three hypothetical—characterized by low, medium, and high wind exposure. Simulation results were compared with existing formulas and with the proposed simplified equation. Although the simplified model introduces a margin of error compared with dynamic simulations, it provides meaningful estimates, with mean deviations typically in the 20–35% range, lower in single-sided conditions and higher for cross-ventilation under medium-to-high wind exposure. The study demonstrates that an ultra-simplified approach can serve as a support tool for the design of temporary healthcare facilities in resource-limited contexts, where rapidity and data accessibility are essential. Full article

Modeling the complex spatio-temporal dynamics of infectious diseases presents a significant computational challenge due to heterogeneous regional interactions, high-dimensional multimodal data streams, and the critical need for algorithmic fairness. This paper proposes a novel computational framework that unifies graph-based spatio-temporal forecasting, anomaly detection, and retrieval-augmented generation (RAG) into a single mathematical architecture. The predictive backbone employs a graph-augmented Temporal Fusion Transformer to capture non-linear temporal dependencies and spatial disease propagation. By formalizing regional topology and mobility flows as a weighted mathematical graph, the model systematically integrates structured epidemiological counts, continuous environmental covariates, and digital trace signals. To address algorithmic bias, we formulate a fairness-aware optimization problem by embedding a specific regularization term into the training objective, which mathematically penalizes disparities in true-positive rates across diverse socio-demographic strata. Furthermore, the numerical outputs and anomaly scores are processed by a large language model equipped with hybrid dense and sparse retrieval to generate interpretable, computationally grounded decision support. Extensive experiments on a longitudinal dataset comprising 62 administrative regions over 260 weeks validate the mathematical robustness of the proposed framework. The graph-augmented architecture improved forecasting accuracy by up to 24% and anomaly detection F1 scores by over 6% compared to state-of-the-art deep learning baselines, while the fairness-regularized loss function reduced the maximum subgroup recall gap by more than 50%. These findings demonstrate that predictive accuracy and algorithmic fairness can be jointly optimized, providing a rigorous computational methodology for spatio-temporal epidemic modeling and AI-driven surveillance. Full article

On 9–10 December 2025, the Coalition for Epidemic Preparedness Innovations (CEPI) and the International Vaccine Institute (IVI) convened a workshop in Seoul under CEPI’s Disease X Program. The primary objective was to identify existing gaps needing to be filled and streamline vaccine development and preparedness for Severe Fever with Thrombocytopenia Syndrome (SFTS). CEPI’s partners and experts discussed a multifaceted agenda, ranging from understanding the evolving epidemiology to the refinement of animal models and immunological assay harmonization. Key outcomes included the refinement of Target Product Profiles (TPPs) specifying use cases for both peacetime and outbreak contexts, alongside a recommendation for a core immunoassay panel aimed at harmonizing evaluation frameworks and mitigating the challenges posed by low SFTS prevalence. Integration of the One Health approach emerged as a critical strategy for SFTS prevention, complemented by proactive regulatory engagement to compress vaccine development timelines. This report summarizes these key insights from the workshop, delineating a strategic framework for delivering safe, effective, and accessible vaccines for SFTS and broader Disease X threats. Full article

Viral and fungal diseases of greenhouse strawberries lead to significant crop losses, while traditional uniform spraying schemes do not account for the actual distribution of infection foci or changes in the microclimate. This paper proposes an integrated system for greenhouse farms that combines a stochastic SIRS model of the epidemic process with a microclimate-dependent infection coefficient β_eff(t), a computer vision module based on a lightweight YOLOv10n detector, and a mobile sprayer robot. For three sets of parameters corresponding to moderate infection, outbreak, and suppression scenarios, ensemble simulations are performed (100 realizations per scenario). The results show that the maximum number of infected plants reaches approximately 690 out of 1000 in the outbreak scenario and only about 28 out of 1000 in the suppression scenario, reflecting the effect of timely microclimate correction and local spraying. The YOLOv10n detector is used as a sensor to determine the proportion of affected plants I(0)/N and provides automatic formation of the initial conditions of the population model. The resulting forecasts then serve as the basis for selecting one of three operating modes for the spraying robot (observation, microclimate correction, local treatment). Unlike existing works that consider disease detection, epidemiological models, or robotic spraying separately, this paper proposes a unified closed-loop scheme of “computer vision—stochastic model—mobile robot,” linking detection quality with epidemic process forecasting and treatment strategy. In this study, the feasibility of the proposed system was examined through numerical simulations, detector-level performance evaluation, and offline image-based integrated validation of the detector-to-decision workflow. Full closed-loop experiments in a real greenhouse environment are planned for future work. Full article

The porcine epidemic diarrhea virus (PEDV) causes a gastrointestinal disease generating mortality rates approaching 100% in piglets worldwide. The S glycoprotein of PEDV is the main target for the development of vaccines. Two vaccines approved by the Ministry of Agriculture and Rural Development are used in Mexico: the first vaccine is based on an inactivated virus isolated more than a decade ago, whereas the second vaccine is based on mRNA technology. The most important tool for controlling PEDV outbreaks is vaccination; however, coronaviruses are characterized by the accumulation of multiple mutations, which compromise the immune response elicited by outdated vaccines. In this work, we classified the Mexican strains of PEDV reported so far in GenBank, according to their genotypes. Subsequently, we searched for B and T cell epitopes conserved in Mexican PEDV strains using bioinformatic tools. In addition, we explored whether these epitopes can induce allergies, autoimmunity, and/or toxic effects. Next, we determined the localization of B cell epitopes in the S glycoprotein using the protein crystal and protein modeling of several S glycoproteins. Finally, we carried out molecular docking analysis to assess whether these T cell epitopes could interact with the peptide-binding groove of the Swine Leukocyte Antigens (SLAs). Five conserved B cell epitopes were found to be exposed on the surface of the S glycoprotein, whereas several promiscuous CTL and HTL epitopes were bound, with low free energy, to the peptide-binding grooves of SLA-I and SLA-II, respectively. The best epitopes were used to generate a plasmid carrying the sequence to produce a recombinant protein. This plasmid was used for transfection experiments in PK-15 cell culture. The B cell epitopes reported here were recognized by the sera from pigs infected with PEDV but not by the sera from uninfected animals. These results justify future evaluations of the ability of these epitopes to stimulate cytokine production by T cells, antibody generation, and their neutralizing activity. Full article

Branching processes are stochastic models describing the evolution of populations in which individuals reproduce and die independently over time. In the classical setting, an individual’s reproductive capacity is fixed throughout its lifetime. However, in real-world situations, fertility typically rises during a juvenile phase, peaks at maturity, and subsequently declines. In order to capture this feature, we introduce a branching random walk with ageing, as an extension of the classical branching random walk, by assigning each individual an age-dependent reproductive rate. Our model differs from classical age-dependent processes such as the Bellman–Harris model, where the remaining lifespan depends on age, while the rate of reproduction is fixed within that lifetime. As in the classical case, branching random walks with ageing are parametrised by $\lambda >0$, which tunes the reproductive speed and may be seen as a characteristic of the population. The thresholds of $\lambda$ separating extinction and survival are the global and local critical parameters. We characterise the value of the local critical parameter and provide a lower bound for the global critical parameter. We identify a class of ageing branching random walks for which this lower bound coincides with the global critical parameter. We study how local modifications to the reproduction and ageing rates may change the critical parameters. This is of practical interest: in species preservation, one may want to lower the critical parameters, so that $\lambda$ exceeds them, and there is a positive probability of survival. On the other hand, in epidemic control, the goal is to increase the critical parameters, since if $\lambda$ is below them, then the epidemic is eventually going to disappear. We compute the expected number of individuals alive in a branching process with ageing and show that, contrary to the behaviour of classical branching processes, it may exhibit an initial growth even when the population is ultimately destined for extinction. Full article

Rift Valley fever virus (RVFV) and Crimean-Congo hemorrhagic fever virus (CCHFV) are designated by the World Health Organization as priority pathogens due to their epidemic potential, zoonotic transmission, and the absence of licensed therapeutics or vaccines. The development of effective antivirals critically relies on robust in vitro and in vivo models; however, progress is limited by the requirement for high-containment facilities. In this review, we provide a comprehensive overview of the experimental models currently available for RVFV and CCHFV, ranging from cell-based assays to animal models, and discuss their respective advantages, limitations, and translational relevance. We further highlight strategies allowing for BSL-2 experimentations, thereby expanding research accessibility, and accelerating the development of countermeasures against these high-priority pathogens. Full article