Search Results (1,160)

Apolygus lucorum is a major piercing–sucking pest in viticulture, yet its seasonal dynamics and outbreak risk in semi-arid wine-grape regions remain insufficiently understood. This study was conducted in a semi-arid wine-grape region of northwestern China during the 2024–2025 growing seasons. Adult density was monitored at 150 fixed sampling points across five landscape units. LOWESS-based phenological staging, stage-specific spatial interpolation, and an XGBoost-SHAP framework integrating meteorological, topographic, and grape phenological predictors were used to characterize spatiotemporal patterns and key predictors. A. lucorum density remained low in May, increased from June to July, peaked during August-September, and remained relatively high in October, with higher overall abundance in 2025 than in 2024. Spatial analyses revealed marked heterogeneity among landscape units, with high-density patches shifting across years and phenological phases. The XGBoost model showed good predictive performance, with an R² of 0.878 on the independent test set and a mean GroupKFold cross-validation R² of 0.869 ± 0.014. SHAP analysis identified grape phenology, elevation, relative humidity, sunshine duration, and temperature as the leading predictors of model-predicted density. PDP and ICE analyses showed higher predicted counts during later phenological periods, at lower elevations, and under higher relative humidity, particularly around 55%. Two-dimensional PDPs further indicated that high predicted densities mainly occurred under combinations of higher relative humidity, later phenological timing, moderate-to-high temperature, longer sunshine duration, and lower elevation. These findings provide a scientific basis for implementing precision-integrated pest management strategies in semi-arid viticultural regions, where monitoring relative humidity during critical phenological windows can serve as an early warning indicator for impending outbreaks. Full article

Background and Objectives: Dengue is emerging as a multifaceted public health challenge that extends beyond traditional vector-borne disease frameworks. Climate change, rapid urbanisation, environmental transformation, global mobility, and digital ecosystems are progressively reshaping transmission dynamics, outbreak patterns, and preparedness needs worldwide. This narrative review aimed to examine dengue from an integrated public health perspective, focusing on climate-sensitive transmission, urban health, surveillance and preparedness, digital epidemiology, artificial intelligence (AI), and health communication. Materials and Methods: A structured narrative review was conducted through targeted literature searches in PubMed, Scopus, and Web of Science between April and May 2026. To this end, a series of separate thematic search strategies were developed to explore the principal conceptual domains addressed in the review. The synthesis was organised around five interconnected preparedness domains: climate change and environmental transformation; urbanisation and urban health; surveillance, vaccination, and integrated preparedness; digital health, artificial intelligence, and mathematical modelling; and health communication and community engagement. The retrieved literature was analysed using a thematic narrative synthesis approach. Results: The retrieved evidence indicated the progressive expansion and redefinition of dengue risk across both endemic and historically non-endemic regions. Climate variability, environmental transformation, rapid urbanisation, and increasing human mobility have emerged as interconnected drivers capable of influencing vector ecology, transmission dynamics, outbreak frequency, and healthcare system vulnerability. Urbanisation has been frequently associated with infrastructural inequalities, environmental degradation, inadequate water and waste management, and territorial conditions favourable to vector proliferation. The extant literature has also placed significant emphasis on the growing importance of integrated surveillance systems and early warning approaches combining epidemiological, environmental, climatic, entomological, and mobility-related data. Digital epidemiology, AI-based predictive models, and digital surveillance tools may contribute to strengthening outbreak forecasting and preparedness capacity, although important limitations related to data quality, interoperability, interpretability, and implementation remain. In parallel, misinformation, risk communication challenges, and digital communication ecosystems emerged as relevant factors influencing public perception, preventive behaviours, institutional trust, and adherence to public health interventions. Conclusions: Dengue is a systems-level public health challenge shaped by climate change, urbanisation, environmental disruption, human mobility, health-system preparedness, and digital ecosystems. Conventional vector-control strategies alone are unlikely to adequately address this growing complexity. Strengthening dengue preparedness should therefore be considered a broader indicator of public health resilience and long-term health-system adaptation. Full article

The outbreak of coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has emerged as one of the most significant global health crises in recent history. Coronaviruses are a diverse group of RNA viruses classified into alpha, beta, gamma, and delta genera, with SARS-CoV-2 belonging to the beta-coronavirus family. The virus exhibits high transmissibility and causes a wide spectrum of clinical manifestations ranging from mild respiratory symptoms to severe complications such as acute respiratory distress syndrome, multi-organ failure, and death, particularly among elderly and immunocompromised individuals. Structurally, SARS-CoV-2 possesses a large single-stranded RNA genome encoding major structural proteins, including spike (S), envelope (E), membrane (M), and nucleocapsid (N) proteins, which play critical roles in host-cell recognition and viral infection. Understanding the molecular mechanisms of virus–host interactions, especially protein–protein interactions (PPIs), is essential for uncovering viral pathogenesis and identifying potential therapeutic targets. Traditional experimental techniques for PPI detection, such as yeast two-hybrid and affinity purification methods, are often expensive, labor-intensive, and prone to inaccuracies. Consequently, computational approaches based on machine learning (ML) and deep learning (DL) have gained significant attention for efficient and scalable PPI prediction. These methods use diverse biological information, including protein sequences, structural features, genomic data, Gene Ontology annotations, and interaction networks, to model complex biological relationships. This survey reviews computational approaches to PPI prediction, highlighting ML- and DL-based techniques, methodological advances, performance evaluation practices, and limitations that affect benchmark comparability. It also discusses biological databases and data sources commonly used in PPI studies and explicitly considers how models trained in coronavirus-centered settings may generalize to other viral families with different mechanisms of host interaction. Full article

Nontuberculous mycobacteria (NTM) are environmental opportunistic pathogens with increasing clinical relevance worldwide. Among them, the Mycobacterium avium complex (MAC), comprising species such as M. avium, M. intracellulare, and M. chimaera, is responsible for the majority of human NTM diseases. MAC causes chronic pulmonary disease and disseminated infections, particularly in immunocompromised individuals, although infections in immunocompetent hosts are increasingly reported. Despite advances in molecular diagnostics, accurate species- and subspecies-level identification remains challenging due to high genetic diversity and biased genomic databases. This limitation hampers the understanding of transmission dynamics, antimicrobial resistance patterns, and epidemiological trends. In recent years, whole-genome sequencing (WGS) has emerged as a key tool for high-resolution typing, enabling improved phylogenetic analysis, outbreak investigation, and resistance prediction. This review summarizes current knowledge on MAC taxonomy, clinical manifestations, antimicrobial resistance mechanisms, and ecological distribution, with a particular focus on the role of genomic surveillance. We highlight the need for integrated genomic frameworks to support early detection, accurate classification, and effective public health surveillance of MAC infections globally in a One Health perspective. Full article

Avian orthoreovirus (ARV) has re-emerged as one of the most important viral pathogens affecting modern poultry production worldwide. In China, the epidemiological landscape of ARV has undergone a substantial transformation over the past decade, characterized by increasing genotypic diversity, frequent genome reassortment, an expanding host range, and recurrent vaccine-breakthrough outbreaks. Growing evidence indicates that contemporary ARV populations evolve within a dynamic multispecies transmission network shaped by intensive poultry production, host adaptation, and vaccine-associated selective pressures. Recent molecular studies have revealed extensive genetic heterogeneity among circulating strains and highlighted the limitations of conventional σC-based classification systems for accurately describing viral evolution, pathogenicity, and antigenic diversity. Whole-genome analyses further demonstrate that reassortment among chicken-origin, duck-origin, and goose-origin orthoreoviruses plays a pivotal role in generating novel viral variants with altered biological properties. In parallel, accumulating evidence suggests that ARV exerts broad immunomodulatory effects through the disruption of innate antiviral signaling, impairment of lymphoid organ function, interference with vaccine responsiveness, and the enhancement of susceptibility to secondary infections. These findings indicate that ARV should be regarded not only as an arthrotropic pathogen but also as an important immunopathological agent influencing flock health and productivity. This review summarizes current knowledge of ARV in China, with an emphasis on molecular epidemiology, genomic evolution, reassortment mechanisms, transmission ecology, immune interference, vaccine escape, and integrated prevention strategies. Particular attention is given to the increasing importance of whole-genome surveillance, phylodynamic analysis, and multispecies epidemiological monitoring for understanding contemporary ARV evolution. Future perspectives involving structural vaccinology, precision immunization, metagenomics-assisted surveillance, and predictive evolutionary modeling are also discussed. Collectively, sustainable ARV control will likely require genome-informed and adaptive prevention frameworks integrating virology, immunology, epidemiology, and precision poultry management. Full article

Influenza virus outbreaks remain a persistent public health concern, yet traditional metabolomics methods are inadequate for addressing key analytical challenges of “dark matter” in influenza research. By integrating quantitative MS¹ data, MS²-derived fragmentation trees and molecular fingerprints, structure-based comparative metabolomics enhances predictive capability for chemical structures, and enables the discovery of candidate metabolic markers without the need for database spectra. In this study, we established a C57BL/6J mouse model of H1N1 infection (with PBS as control) and performed structure-based comparative metabolomics on fecal samples using liquid chromatography–mass spectrometry (LC-MS). Quantitative analysis of MS¹ data identified 40 differential metabolites, while qualitative analysis of MS² data enabled their structural annotation. A candidate metabolite marker, LysoPE 15:0, along with other potential metabolic markers, was annotated and validated using Mirror plot, CFM-ID, and sim-Rank-Network. Our findings demonstrate that structure-based comparative metabolomics enables library spectra-free annotation of metabolomic “dark matter” and provides a methodological workflow for discovering candidate metabolite markers in other diseases. Full article

Background/Objectives: Strangles, caused by Streptococcus equi subspecies equi (S. equi), remains a common and severe equine infectious disease. Strangvac^®, a recombinant fusion protein vaccine licenced in Europe, contains the antigens (Ag) CCE, Eq85, IdeE and a saponin adjuvant. Although its efficacy is high (94% in clinical trials and 100% in some natural outbreaks), immune correlates of protection have not been defined. This study determined the antibody (Ab) thresholds predictive of protection against clinical disease following high-dose experimental S. equi infection and the expected levels of protection at 6 and 12 months after V2. Methods: This study was a retrospective analysis of six independent double-blinded placebo-controlled experimental infection studies involving 129 ponies (80 vaccinated controls and 49 placebo controls) and a serology study (12 vaccinated ponies). Ponies received two to five vaccine doses before being experimentally challenged with S. equi strain Se4047. Ponies in the serology study were not experimentally infected. The onset of pyrexia (≥39 °C for at least 2 of 3 consecutive days, OOT) was used as a disease marker. Serology to IdeE, Eq85 and CCE was analysed with standardised clinical outcomes to define protective thresholds through correlation and Receiver Operating Characteristic (ROC) analyses. The predicted level of protection up to one year after V2 was then calculated (duration of immunity: DOI). Results: A protection threshold of ≥10 days to OOT, derived from the control distribution, was used for ROC modelling. Predictive performance (e.g., accuracy, precision, specificity) was calculated for individual and combined Ab thresholds. All controls developed pyrexia (median 6 days, IQR 5–7), with 46 out of 49 (93.9%) within 9 days of the challenge. Vaccinated ponies showed significantly delayed or absent OOT compared with controls (p < 0.0001), with 37 vaccinated ponies (46.25%) reaching the end of the studies without developing pyrexia. The Ab titre to all antigens was significantly associated with the level of protection (p < 0.0001). ROC analyses demonstrated high discriminative power (AUC 0.86–0.88). Optimal Ab titre boundaries yielded high precision (≥80%) for all Ags (IdeE: 3.5–4.3; Eq85: 2.65–3.7 and CCE: 2.66–3.2). Both precision and accuracy remained above 80% for levels of IdeE and Eq85 Ab titres superior or equal to those measured up to one year after V2, with an estimated level of protection of 78.9% to 81.2% in vaccinated animals. Conclusions: Ab titres to all three Ags represent robust correlates of protection against pyrexia following high-dose experimental S. equi challenge in Strangvac^®-vaccinated ponies. Ab titres measured up to one year after V2 were estimated to continue to provide significant protection in vaccinated animals. These findings support the observed levels of protection conferred by Strangvac^® against natural infection with S. equi. Full article

Background/Objectives: Antimicrobial resistance (AMR), driven by high-risk bacterial pathogens, is a major healthcare threat. This scoping review mapped artificial intelligence/machine learning (AI/ML) and computational approaches integrated with whole-genome sequencing (WGS), genomic surveillance, rapid typing, epidemiological data, or clinical metadata for early warning of AMR outbreak clones. Methods: Following PRISMA-ScR guidance and the Population–Concept–Context (PCC) framework, PubMed/MEDLINE, Scopus, and Web of Science were searched for English-language studies published between 2010 and 2026. Eligible studies addressed AI/ML or computational approaches for AMR outbreak detection, clone surveillance, transmission analysis, or infection prevention and control (IPC). Results: Thirty-eight studies were grouped into five domains: genomic surveillance; rapid typing; resistance, risk-factor, and lineage prediction; transmission reconstruction; and IPC-oriented genomic epidemiology. AI/ML supported automation, isolate prioritization, typing triage, prediction, transmission modelling, and electronic health record (EHR)-linked route identification. Conclusions: AI/ML may enhance WGS-based AMR surveillance, but validation, dataset dependence, heterogeneity, and limited IPC outcome reporting remain key gaps. Full article

Background: Dengue fever has emerged as a major public health concern in Bangladesh, with increasing incidence and geographic spread of outbreaks in recent years. This study aimed to investigate the lagged and non-linear associations between climatic factors and dengue incidence across all eight administrative divisions of Bangladesh from 2014 to 2025. Materials and Methods: An ecological time-series design was employed using monthly dengue case data (n = 741,338) and meteorological variables. A generalized additive model (GAM) with a negative binomial distribution was applied to account for overdispersion and capture complex relationships. Descriptive analysis was conducted to assess spatial heterogeneity, and choropleth maps were constructed to visualize the spatial distribution and regional variation in dengue burden across the country. Cross-correlation analysis was performed to identify significant lagged associations between climatic variables and dengue incidence. Results: Descriptive analysis showed substantial spatial heterogeneity, with the highest incidence observed in Dhaka (6.53 per 100,000) and the lowest in Sylhet (0.21 per 100,000). Choropleth maps illustrated distinct spatial distribution and regional variation in dengue burden across the country. Cross-correlation analysis identified significant lagged associations for temperature and rainfall (lag 1–3 months), humidity (lag 1–2 months), and wind speed (lag 2–3 months). The final GAM explained 88.6% of the deviance in dengue incidence (AIC = 7404.15; dispersion = 0.767). The approximate significance of smooth terms revealed that temperature at a lag of 1 month (p < 0.001, edf = 12.28), rainfall at a lag of 3 months (p < 0.001, edf = 2.85), and wind speed at a lag of 2 months (p < 0.001, edf = 2.25) were highly significant non-linear predictors of dengue transmission. Relative humidity was not significantly associated with dengue incidence. Non-linear effects revealed peak dengue risk at temperatures between 25 and 30 °C and moderate rainfall (~10 mm), particularly during monsoon months (June–October). A strong autoregressive effect indicated that prior dengue incidence significantly influenced current transmission. Conclusions: Overall, dengue transmission in Bangladesh is driven by complex, lagged, and non-linear interactions between climatic variables, seasonality, and regional factors. These findings provide critical evidence for climate-based early warning systems, enhance outbreak prediction, and inform evidence-based vector control strategies. Full article

East Coast Fever (ECF) causes approximately one million livestock deaths annually in sub-Saharan Africa, posing a significant threat to livestock. The wildlife–livestock interface complicates disease management, as wildlife serve as reservoirs. This study developed a Continuous Time Markov Chain (CTMC) model incorporating the wildlife–livestock interface to analyze ECF dynamics. Using the Galton–Watson approximation, we assessed the probability of disease extinction following the introduction of infected hosts or vectors. The probability of disease extinction calculated from the branching process is shown to be in good agreement with the probability approximated from numerical simulations. The disease dynamics of the deterministic model and the CTMC model are compared to ascertain the effect of demographic stochasticity on ECF dynamics. Differences in model predictions and asymptotic dynamics between stochastic and deterministic models were evident. The deterministic and stochastic formulations should therefore be viewed as complementary modeling frameworks, with the deterministic model characterizing average epidemic dynamics and the CTMC model capturing the probabilistic variability and extinction behavior inherent in real transmission processes. These differences are crucial for intervention strategies earmarked to prevent outbreaks. Our analysis revealed a high probability of ECF extinction if the disease emerges from recovered carrier cattle. Finite time to ECF disease extinction is estimated using 10,000 sample paths, and it is shown that the epidemic duration is shortest if the disease is introduced by infectious cattle. The epidemic duration is longest when the disease is introduced by infectious ticks. Additionally, we observed that host interactions at the wildlife–livestock interface play a critical role in shaping ECF transmission and informing control strategies. Full article

Timely fish disease monitoring is an important requirement for sustainable aquaculture because disease outbreaks can reduce survival, increase treatment inputs, and destabilise production. In aquaponic systems, fish health is also linked to nutrient cycling and the stability of integrated fish–vegetable production, making automated fish-health perception a potentially useful component of resource-efficient farming. Existing classification and detection methods can identify disease categories or approximate lesion locations, but they provide limited information about lesion area, boundary shape, and severity-related spatial extent. This study presents a deep learning framework for pixel-level fish lesion segmentation to support sustainable aquaculture health monitoring, with aquaponic systems considered as a potential application context. The framework combines lesion-preserving frequency augmentation (LPFA), confidence-guided large-kernel encoding (CGLE), and confidence-filtered decoder refinement (CFDR). LPFA expands lesion appearance variation during training while retaining the main lesion layout. CGLE uses coarse prediction confidence to allocate broader contextual modelling to uncertain encoder regions, and CFDR applies selective decoder correction to low-confidence regions. A public freshwater fish disease dataset is reformulated into a dense prediction task with 1750 raw images from seven image-level categories, including six disease categories and one normal healthy category. The images are divided into training, validation, and test subsets at an 8:1:1 ratio, and controlled augmentation strategies are applied online rather than being used to create a larger static dataset. Across five random-seed runs, the proposed method achieves $82.6\pm 0.3$% mIoU, $90.9\pm 0.2$% mDice, and $73.5\pm 0.4$% Boundary IoU. Relative to TransUNet, the mean mIoU rises from $78.4\pm 0.4$% to $82.6\pm 0.3$%, and Boundary IoU rises from $68.8\pm 0.5$% to $73.5\pm 0.4$%, with paired bootstrap testing supporting the stability of the improvement. These results indicate its potential as a lesion-quantification decision-support component for smart and sustainable fish-production systems. Full article

The southern pine beetle (SPB) is a serious pest of pine forests from Central America to the eastern United States, with a recent range expansion into the northeastern United States. Efforts to detect and monitor SPB activity began in 1960 as part of an overall integrated pest management system to limit its impact to southern pine forests. The ubiquity of SPB’s pine hosts in the southern United States, in the form of plantations and natural mixed stands, along with the regular occurrence of SPB outbreaks over a vast region, makes SPB a leading driver of overall forest health across this region. We review the past and current methodology for collecting SPB-related pine mortality and outbreak data using aerial and ground survey techniques and remote sensing via satellite imagery. We show how historical and ongoing measurements of SPB abundance, from pheromone-baited traps and aerial surveys, are used to forecast near-term probabilities of outbreaks with a statistical model (actualized through a public URL) that captures the natural tendency of SPB populations to be very high or very low. Insect forecasts can also be combined with maps of the host distributions to generate predictions of short-term regional risks and longer-term tree mortality forecasts via the US Forest Service’ National Insect and Disease Risk Map (NIDRM). Because the measurements of insect abundance and impact outcomes have become part of continuing forest management operations, statistical models can continue to be improved and there is self-reinforcing feedback between models and management. Improved understanding and monitoring of prominent insect pests that impact abundant tree species is a pathway to managing forest health more broadly. Full article

In January 2025, a measles outbreak was reported in Gaines County, Texas, and subsequently spread to other counties and states. However, investigations into the geographic spread of this outbreak remain limited. We developed a measles transmission model parameterized with 2020–2024 measles–mumps–rubella (MMR) vaccination coverage and human mobility data in Texas. We conducted sensitivity analyses to evaluate how variation in model parameters affects outcomes. We compared model predictions to data from the 2025 measles outbreak and simulated scenarios for outbreak originating in different low vaccination counties. We found that an outbreak originating in Gaines County would have at least 80% probability of directly generating local outbreaks in eight neighboring counties in West Texas. The spatial spread was highly sensitive to the basic reproduction number ($R_0$) and population-level MMR vaccination coverage. Outbreaks originating in counties with low MMR vaccination rates, such as Polk, Montague, or Limestone County, are likely to spread to large metropolitan areas such as Houston and Dallas. Measles has the potential to cause statewide outbreaks in Texas. Our modeling framework can inform county-level risk assessment and guide preemptive vaccination strategies. Full article

This study developed an integrated stochastic framework to forecast cotton pest outbreaks across six tropical Indian agroecosystems. Methodologically, the approach fused entropy and Gini inequality indices, Random Forest machine learning, SHAP-based feature interpretation, fuzzy logic risk assessment, and climate scenario simulations (+2 °C, +20% rainfall) to quantify outbreak variability, driver importance, and system resilience. Findings revealed extreme pest stochasticity (mean = 15.7, variance > 4200), with low entropy (0.06) and a high Gini coefficient (0.82) confirming highly concentrated spatial and temporal outbreaks. While Random Forest demonstrated limited predictive skill (RMSE = 68.9, R² = 0.07), SHAP analysis transparently identified evaporation, wind speed, and humidity as dominant drivers. Fuzzy logic yielded an average risk score of 1.0, reflecting frequent exceedance of biological thresholds. Scenario simulations demonstrated pronounced climate sensitivity: a +2 °C temperature increase raised mean incidence to 18.7, and +20% rainfall increased it to 18.6, resulting in a resilience index of 1.51 that indicates disproportionate vulnerability. In conclusion, combining stochastic variability metrics, explainable machine learning, and threshold-based risk modeling significantly advances tropical pest forecasting under uncertainty. Importantly, this framework contributes to sustainability by enabling climate-resilient cotton production, reducing reliance on chemical pesticides, and supporting adaptive advisory systems that strengthen long-term agroecosystem resilience. These results emphasize the critical need for adaptive, location-specific management strategies to mitigate climate-driven pest intensification and enhance resilience in cotton production systems. Full article

Hantaviruses are emerging zoonotic pathogens responsible for two severe clinical syndromes: (i) haemorrhagic fever with renal syndrome (HFRS) and (ii) hantavirus cardiopulmonary syndrome (HCPS), collectively causing more than 200,000 human cases annually worldwide. Despite their public-health importance, the molecular mechanisms governing the host response and the population-level dynamics of rodent-to-human spillover remain incompletely characterised. The timeliness of this framework is underscored by the April–May 2026 outbreak of Andes orthohantavirus aboard the MV Hondius cruise ship, the first such cluster in a maritime setting, with three deaths reported across multiple countries. This event revealed critical gaps in existing models that treat humans solely as dead-end spillover hosts. Our coupled Susceptible-Exposed-Infectious-Recovered-Dead (SEIRD) model assumes no human-to-human transmission and is therefore designed for hantavirus strains where spillover does not lead to secondary human cases, specifically Hantaan virus (HTNV), Puumala virus (PUUV), Sin Nombre virus (SNV), and Dobrava-Belgrade virus (DOBV). The Andes virus (ANDV) outbreak aboard the MV Hondius is used as a real-world case study to assess the boundaries of our model and to motivate future extensions, not as a direct validation target for its quantitative predictions. Here, we present an integrated computational study combining three complementary analyses. First, we performed a preliminary phylogenetic analysis of the viral sequence, identifying Orthohantavirus andesense as the likely etiological agent responsible for the vessel-associated outbreak. Second, we carried out a downstream transcriptomic analysis of Hantaan virus (HTNV)-infected human umbilical vein endothelial cells (HUVECs), using publicly available RNA-seq data (GEO accession GSE133751, $n=3$ per group). This analysis identified 184 upregulated and 19 downregulated genes, highlighting a transcriptional response dominated by interferon-stimulated genes (ISGs), including CXCL10, CXCL11, MX2, DDX58, IRF7, STAT1, OASL, and CMPK2. We then constructed a protein–protein interaction (PPI) network using STRING, comprising 176 nodes and 3210 edges, and applied a composite network centrality score to rank putative regulatory hubs. This analysis identified ISG15, IRF1, CXCL10, STAT1, and DDX58 as the most central nodes. Pathway enrichment analysis confirmed a strong activation of interferon signalling (Reactome, $p=1.3\times {10}^{-63}$), antiviral defence mechanisms (Gene Ontology, $p=3.8\times {10}^{-58}$), and NF-$\kappa$B-related pathways, together with a concurrent suppression of ribosomal translation. Finally, we developed a coupled SEIRD epidemiological model that explicitly represents rodent-to-rodent and rodent-to-human transmission with logistic rodent population growth. Preliminary simulation analysis demonstrates that reducing human exposure to rodent excreta is substantially more effective than rodent population control alone for reducing human disease burden, and that rodent control in isolation can paradoxically increase human cases through a dilution-like effect. The integrated framework provides molecular and epidemiological insights relevant to hantavirus surveillance, therapeutic target identification, and public-health intervention design. Full article