Computer Science and Mathematics

Abstract: Non-coding RNAs (ncRNAs) play important roles in various biological processes primarily via regulating gene expression at multiple levels. Nevertheless, compared to protein-coding genes, ncRNAs are relatively understudied, characterized by low expression, large variation, and more dynamic expression across different tissues and cell types, which creates unique challenges in differential expression (DE) analysis. We propose a new DE method specifically for ncRNAs based on a composite quantile regression model for count data, in which we combine multiple quantiles and select the best subset of quantiles that most differentiate the expression levels between conditions. The proposed method is flexible, robust and capable of capturing the typically low-count, multi-modal and wide-spread distribution of ncRNA count data. We showed in simulations that our method improved the detection power of ncRNAs while controlling the false positive rates as compared to existing DE tools especially when counts are low. Critical ncRNAs and biologically interpretable targeted pathways were identified when we further applied our method to a Smart-seq-total single-cell RNA-seq dataset and a human organ developmental RNA-seq dataset. An R package to implement CQRM and the codes for this study are publicly available: https://github.com/iamverywell/CQRM.

Abstract: Nowadays, the complexity of electronic health records (EHRs) requires tools capable of efficiently and accurately extracting and interpreting clinically relevant information to support clinicians. This study explores the use of the Cheshire Cat AI framework, configured with Ollama and using LLaMA3 as a language model, with the main purpose of performing automatic analysis of synthetic EHRs from Kaggle. Through specific structured queries, the model was able to successfully reconstruct patients’ clinical histories and extracted useful data such as diagnoses, treatments, visits, comorbidities and demographic data. A validation process through repeated queries was then performed, which confirmed a high level of accuracy. To preserve data privacy, only synthetic datasets were used in this work. Beyond the simple retrieval of information by means of queries, the study highlights the great potential of language models in clinical decision support. Their ability to interpret large and heterogeneous datasets certainly offers new opportunities to improve diagnostic accuracy, simplify workflows and personalise treatments. Specifically, natural language queries by tools such as Cheshire Cat AI can be used for intelligent support systems that can, for instance, integrate multimodal and real-time data to provide medical recommendations. These results represent a first step towards the exploitation of large language models not only for EHR analysis, but also to assist in clinical decision-making processes in different medical fields and, above all, for the study of specific complex diseases such as rare diseases.

Abstract: The demand for user-friendly applications to support biologists in analyzing high-throughput proteomics data remains a pressing challenge. Given the complexity and the multiple intermediate steps involved, this process is time-consuming and often requires specialized computational skills. To simplify and accelerate the exploration of proteomics data, we present PiProteline, an R package designed to operate on high-dimensional data matrices assembled from the output of any search engine commonly used in bottom-up proteomics experiments. In addition to data preprocessing and descriptive statistics, PiProteline enables label-free quantitation, functional enrichment, and systems biology analyses. Notably, it supports both unweighted and weighted protein-protein interaction (PPI) network topological analyses for the identification of critical nodes, such as hubs and bottlenecks. By integrating multiple analytical approaches, PiProteline accelerates the selection of potentially relevant protein signatures, providing insights into the molecular mechanisms characterizing the investigated systems. This information facilitates the formulation of new hypotheses and the design of targeted experiments, ultimately reducing costs and advancing translational medicine. PiProteline is available as an open-source R package via GitHub (https://github.com/lomi95/PiProteline) and as a Shiny application (https://github.com/salehnhd/PiProteline-Shiny).

Abstract: Eye diseases such as trachoma, allergic conjunctivitis, and dry eye syndrome have shown increasing prevalence in regions experiencing adverse environmental and climatic changes. Factors such as air pollution, dust exposure, humidity variations, and ultraviolet (UV) radiation directly impact ocular health, especially among vulnerable populations. In this study, we develop a deterministic compartmental model to explore the dynamics of environmentally-driven eye disease transmission and progression. The model integrates climate-sensitive variables, such as dust concentration and humidity, into the transmission and recovery rates of the disease. We analyse the model's equilibria, investigate the basic reproduction number R₀, and assess the influence of environmental mitigation Strategies on disease control. Numerical Simulations are provided to illustrate how seasonal and anthropogenic changes in environmental conditions affect disease prevalence over time.

Abstract: Whole-slide image (WSI) analysis is limited by a familiar mismatch: each slide contains tens of thousands of candidate tissue patches, while supervision is usually available only at slide level. Existing bag-construction strategies tend to address only one side of this problem. Uniform extraction and handcrafted heuristics do not control redundancy, attention-based multiple-instance models couple patch importance to a particular downstream classifier, and coreset methods optimise embedding-space coverage without modelling task-relevant patch quality. We introduce InfoDPP-PAC, a principled patch-selection framework that combines teacher-seeded Gaussian process relevance modelling, determinantal log-determinant diversity, submodular greedy optimisation, and a concentration-based adaptive stopping rule. The main theoretical result shows that the log-determinant diversity term used in DPP-style selection is the Gaussian process mutual information between a selected subset and the latent relevance function. This yields a monotone submodular objective with standard greedy approximation guarantees at fixed budgets. We further derive a PAC-style certificate for residual information gain, allowing the number of retained patches to vary by slide rather than being fixed a priori. The empirical study is deliberately scoped to selection-quality validation: it evaluates whether the selected subset is diverse, spatially and morphologically covering, non-redundant, and enriched for the teacher-derived relevance signal. It does not claim end- to-end diagnostic improvement after retraining a downstream MIL model. On 202 HISTAI gastrointestinal whole-slide images, the adaptive rule uses 83.7% fewer patches on average than a fixed full budget while retaining 97.9% of full-budget composite selection quality. At a matched budget, InfoDPP-PAC achieves the highest mean teacher-derived relevance score among fourteen baselines, with diversity and composite scores close to the strongest coreset methods. The results support InfoDPP-PAC as a controlled quality-diversity-cardinality selection framework, rather than as a downstream clinical predictor.

Abstract: This study presents a mathematical modeling framework to analyze the impact of integrating sterilizing treatment into tuberculosis (TB) control strategies, particularly in resource-limited settings. Our findings highlight that while sterilizing treatment alone is highly effective in reducing TB incidence and mortality, its widespread implementation requires significant financial investment. The optimal control approach demonstrates that a mixed strategy, combining sterilizing and non-sterilizing treatments, can achieve comparable public health benefits at a lower cost, especially in the medium-term planning. From a long-term perspective, however, our results suggest that exclusively sterilizing treatment ultimately leads to greater reductions in TB incidence, prevalence, and mortality, justifying its higher initial cost. This is primarily due to the ability of sterilizing drugs to eliminate latent TB infections, prevent future active infections and disease-induced deaths, and avoid a considerable number of treatments. Additionally, under scenarios where the cost of sterilizing drugs decreases over time, a swift transition to a solely sterilizing treatment could result in both epidemiological and economic advantages for healthcare systems.

Abstract: Genome sequence information is the primordial need for studying species genetics, evolutionary history, disease mechanisms, risk prediction, adaptation and many more. Eventually large-scale initiatives are underway to sequence unknown genomes from various species including humans with various phenotypic states to gain insights into gene function and genetic diversity. However, assembling large genomes remains a computational challenge due to several factors including sequence complexity, continuous growth in sequencing throughput, lack of suitable benchmarking for the selection of optimal combination of tools etc. Additionally, its quality evaluation is another crucial step to perform but varying methods often lead to arbitrary comparisons. Moreover, high sequencing error rates necessitate the error correction and consensus sequence generation steps in genome assembly. To rectify these sequencing errors several polishing tools are already in use but their in-depth survey is still lacking. Appropriate selection of tools can enhance consensus quality and can generate precise assembly. Hence, a comprehensive survey is always beneficial to build a pipeline incorporating multiple evaluation indicators, including contiguity, accuracy, completeness, and contamination along with a proper guidance to select optimal tool and follow the right steps to achieve more accurate and complete genome assembly.

Abstract: Nonlocal circumstances in genetic engineering are crucial as they pertain to the understanding of genetic material. When these conditions are associated with differential-integral equations, particularly concerning the time variable, they yield comprehensive insights into the material's temporal memory, which can be advantageous for understanding all material properties (including chronic conditions or behavioral characteristics), thereby assisting specialists in managing its future evolution. This study investigates fractional nonlinear mixed integro-differential equations (FrNMIo-DE) with nonlocal circumstances, a category of mathematical problems prevalent in many domains including physics, engineering, and biological systems. Fractional calculus, which generalizes classical differentiation and integration to non-integer orders, offers a robust foundation for modelling memory and hereditary characteristics in complex systems. We examine the existence and uniqueness of solutions to FrNMIo-DE under nonlocal restrictions, using a discontinuous kernel dependent on location and time-space L2[−1,1] × C[0,T], where T < 1, via analytical methods. According to the features of fractional integrals, the FrNMIo-DE adheres to the second-kind Volterra-Hammerstein integral equation (V-HIE), characterized by a discontinuous kernel in position for the Hammerstein integral term and a continuous kernel in time for the Volterra integral (VI) term. Subsequently, we use a separation approach technique to produce HIE with time-dependent physical coefficients. Following an analysis of the system's convergence, a nonlinear algebraic system (NAS) is constructed using the Toeplitz matrix technique (TMT) and related methodologies. The numerical data and associated errors are shown via the Maple 2022 software.

Abstract: The Susceptible-Infected-Recovered (SIR) model is a crucial framework for researching the dy-namics of infectious diseases within humans. Here, we provide an overview of both analytical and numerical methodologies for solving the SIR model. This model contains a set of ordinary differential equations that govern the dynamics of infectious disease which represent the rates of change for susceptible, infected, and recovered humans across time. In order to comprehend the transmission and management of infectious diseases, analytical and numerical solutions are complimentary. Analytical solutions provide theoretical predictions and insight into the model's underlying dynamics, while numerical solutions give the simulation and investigation of the model's behavior under many situations. We can understand the dynamics of infectious diseases and public health initiatives to lessen their effects by integrating these methods. To demonstrate their accuracy in forecasting the spread under varied conditions, this study offers trustworthy so-lutions to the differential equations regulating dengue transmission.

Abstract: Machine learning (ML) and deep learning (DL) have made great improvements in classifying cancer, but there are problems that go past the basic difference between tumor and normal cells. Models trained on extensive datasets such as Clinical Proteomic Tumor Analysis Consortium (CPTAC) and The Cancer Genome Atlas (TCGA) demonstrate high accuracy in tumor detection. However, performance diminishes in clinically significant tasks such as molecular subtyping, stage and grade prediction, prognosis estimation, and tissue-of-origin identification. Evidence from various omics and imaging modalities establishes the data foundations of computational oncology and conceptualizes cancer classification as a hierarchical challenge characterized by diminishing predictive performance as biological complexity escalates. Biological signal strength has a bigger effect on performance than model architecture. Preprocessing steps, such as normalization, scaling, imputation, and batch correction, are recognized as significant factors influencing model outcomes, especially in heterogeneous datasets. Multimodal fusion strategies enhance robustness but yield minimal improvements in sensitivity owing to inadequate spatial-molecular alignment.Current methods have a basic sensitivity limit. Progress in clinically relevant prediction will depend on better data resolution, careful preprocessing, and strict validation, rather than more complex models. The following review examines each of these arguments in depth, from data foundations to hierarchical classification tasks that expose their limitations.

Abstract: Sensor-network, Internet of Things, industrial-monitoring, and cyber-physical security graphs are increasingly outsourced to clouds, where similarity search should be supported without exposing graph content, query graphs, update contents, or database evolution. Existing privacy-preserving graph similarity schemes mainly target static encrypted databases and therefore handle insertions, deletions, label updates, and long-running index maintenance poorly. This paper proposes DFB-PPGSQ, a dynamic forward/backward-private graph similarity matching scheme that moves branch-based lower-bound filtering into a structured-encryption framework. DFB-PPGSQ uses epoch-local feature tokens, per-record occurrence handles, one-time update labels, update buffers, deletion tombstones, and shuffle-based branch-tree re-randomization to preserve pruning efficiency while bounding temporal leakage. We formalize the system model, leakage functions, algorithms, and security interpretation, and implement a reproducible Python prototype with HMAC-SHA256 token generation and multi-profile dynamic sensor-topology workloads. Across five random seeds, DFB-PPGSQ keeps query latency close to the static branch-tree baseline (46.60 ms versus 44.72 ms at 4000 graphs), avoids immediate full-rebuild updates (0.091 ms insertion and 0.092 ms label update), and keeps metadata-assisted cross-epoch token linkage below 5.6% attack success after refresh in additional industrial and campus IoT stress workloads.

Abstract: Plant physiology and development, ranging from leaf growth and flowering time in Arabidopsis thaliana to tuberization in potato, are profoundly influenced by ambient light con-ditions. It has been acknowledged that light significantly inhibits hypocotyl elongation. However, how light patterns affect early seedling development remains unclear. To ad-dress this, we developed a logistic model for Arabidopsis thaliana hypocotyl elongation. The model incorporated the circadian clock and the PHYTOCHROME-INTERACTING FAC-TOR (PIF)-mediated signaling pathways, capturing the biological characteristics of limited hypocotyl growth. Through numerical simulations, we identified several artificial photoperiods that can accelerate hypocotyl elongation, and thereby promote rapid growth. Compared with the conventional 12L:12D (12 h light/12 h dark) photoperiod, inserting a 4-h light pulse into the dark phase of an 8L:16D cycle advanced the elongation-rate peak by 5.5 h and shortened the growth cycle by 32%. Furthermore, under a 9L:9D photoperi-odic stress, the growth peak occurs 6 h earlier, and the growth cycle is shortened by 25%. These findings highlight the potential role of artificial photoperiods in promoting hypo-cotyl elongation, providing a potential strategy for light control of plant photomorpho-genesis and development.

Abstract: Hepatitis B remains a major public health concern worldwide, particularly in developing nations where poor vaccination coverage, lack of screening, unsafe sexual practices, and delayed treatment fuel its spread. We developed a fractional-order model incorporating vaccination, screening, post-exposure prophylaxis (PEP), acute and chronic infection management, and behavioural measures. Theoretical analysis confirmed the model's existence, uniqueness, positivity, and boundedness. Equilibrium states were identified, and the basic reproduction number (R₀) was derived via the next-generation matrix. The disease-free equilibrium is locally and globally asymptotically stable when R₀< 1, while the endemic equilibrium exists and is globally asymptotically stable when R₀>1. Model fitting and parameter estimation used acute Hepatitis B data from Ireland. Sensitivity analysis identified vaccination, screening, and safe practices as the most influential control factors. Numerical simulations showed that conventional strategies alone are insufficient; higher vaccination coverage, efficient screening, improved safe practices, and effective PEP are essential to reduce transmission. Collectively, these interventions minimize progression to chronic disease, reduce long-term burden, lower complications and mortality, particularly when acute infection management is included.

Abstract: The human microbiome is a complex, dynamic and highly structured ecosystem whose analysis requires computational methods able to capture relationships among microbial taxa, genes, metabolic pathways, host factors, environmental exposures and disease phenotypes. Conventional machine-learning pipelines often represent microbiome samples as independent high-dimensional abundance vectors, thereby neglecting ecological, phylogenetic and functional dependencies among microbial entities. Graph-based learning provides a natural framework for modelling such dependencies, whereas graph contrastive learning (GCL) offers a self- supervised paradigm for learning robust representations from graph-structured data under limited label availability. This survey reviews the emerging intersection between GCL and microbiome data analysis. We first discuss the biological and computational characteristics of microbiome data, including sparsity, zero inflation, compositionality, batch effects, cohort heterogeneity and weak supervision. We then organize microbiome graph representations into taxa–taxa association networks, phylogenetic graphs, sample similarity graphs, microbe– disease association networks, host–microbe graphs, metabolic graphs and heterogeneous multi-omics graphs. Next, we summarize the foundations of GCL, including view generation, positive and negative pair construction, contrastive objectives, negative-free learning and multi-view representation learning.

Abstract: Aqueous humor (AH) is a watery fluid continuously circulating through the posterior and anterior chambers of the human eye and is essential to maintain a healthy intraocular pressure in the eye ball and keep the eye clean from waste products of metabolism and external agents. This paper presents a stationary compartment model of AH dynamics consisting of three integrated modules (M): M1 for AH production, M2 for AH passive flow and M3 for AH drainage. M1 is a zero-dimensional (0D) reduction of the velocity-extended Poisson-Nernst-Planck model and simulates solute transfer and fluid movement across the cellular structure of the ciliary epithelium (CE). M2 is the electric equivalent representation of Poiseuille flow across the series of two linear hydraulic resistors. M3 is a 0D reduction of the Darcy equations for a porous medium and simulates AH flow across the parallel between a nonlinear and a linear resistor. Compared to existing compartment approaches, the present model integrates at the macroscopic scale the multi-physical description of the human eye at the cellular scale. Numerical simulations suggest that (1) sodium channels in the CE are essential for maintaining proper AH dynamics; and (2) increased episcleral vein pressure reduces AH drainage, potentially explaining the development of secondary open-angle glaucoma. These insights advance the understanding of the mechanisms regulating AH dynamics and offer new perspectives for patient-specific therapies.

Abstract: Survival modeling is a crucial area in cancer research and precision oncology, enabling prediction of time-to-event outcomes such as overall, progression-free, and disease-free survival. The Cox proportional hazards model has long been the foundation of prognostic analysis due to its ease of interpretability, but the assumptions of linearity and proportional hazards limit its ability to capture complex, high-dimensional relationships in multi-omics data. Deep learning (DL)–based survival models address these limitations by providing flexible, nonlinear modeling and advanced representation learning. This review provides an overview of advances in survival modeling, tracing the evolution from traditional Cox regression to neural network–based approaches, including feed-forward survival models and modern DL architectures. To predict survival based on molecular and clinical information, two major strategies have emerged: (1) applying neural networks directly to multi-omics and clinical data within a Cox regression framework, (2) using variational autoencoders (VAEs) to learn compact latent representations of multi-omics data that are combined with clinical variables. Here we discuss in detail some recently developed VAE-based methods that improve prognostic performance, focusing on advanced training strategies and architectural designs that integrate unsupervised representation learning with Cox PH or non-linear extension of Cox models. Further, we highlight the opportunities to answer core biological questions and key advances in the DL paradigm such as optimization, regularization, and model interpretability, while noting that challenges remain in reproducibility, benchmarking, and clinical translation. In this review, we underscore the need for robust, interpretable, and standardized approaches to improve risk stratification by uncovering biologically meaningful patterns in multi-omics and clinical data, thereby advancing precision oncology.

Abstract: N-of-1 medicine reasons about one patient at a time using that patient's own molecular data, but lacks standardized evidence architectures. Current strategies depend on fragmented biomarkers, population-level associations, or expert preference, limiting auditability and reproducibility. Here we present SteeraMed Core, a framework that addresses these limitations by representing individual molecular state through PPI-network modules, linking it to candidate interventions via a steerability alignment score, and packaging the result as an auditable evidence chain. Retrospective positive-control evaluation across three disease and one aging methylation cohort supported the PPI-module design. Known drugs were recovered above random chance in rheumatoid arthritis (5.8-fold), breast cancer (5.1-fold), and aging (1.8-fold with literature-convergent candidates niacin and colchicine). In depression, nutraceuticals exceeded baseline recovery particularly in mid-age sub-cohorts (36-55 years, ~2.0-fold enrichment). Patient-level evidence chains, each composed of four layers (perturbed modules, drug-module alignment scores, mechanism annotations, and bootstrap confidence), illustrated how the framework links individual molecular state to ranked candidate interventions, aligning with the FDA Plausible Mechanism Framework's emphasis on traceable mechanistic evidence. These results support PPI modules as effective leverage points that aggregate weak individual signals into coherent functional units. Critically, PPI modules retained above-baseline drug recovery under progressive Gaussian noise, whereas single-gene representations collapsed, confirming greater robustness of module-level alignment. Together, these results establish the core of a Steerable Biomedical World Model that directly addresses the three limitations above: PPI modules replace fragmented biomarkers with coherent functional units; per-patient scoring replaces population-level associations with individual mechanism alignment; and evidence chains from public data replace expert preference with auditable reasoning. This core has been validated through retrospective positive-control recovery, not clinical efficacy. Converting it into a full learning system will require prospective cohorts with paired pre- and post-intervention measurements.

Abstract: Understanding the conditions under which the immune system can suppress or fail to contain a growing tumor remains a central open problem in oncology, with direct implications for the design of immunotherapy protocols. Nutrient availability in the tumor microenvironment is increasingly recognized as a determinant of both tumor aggressiveness and immune cell efficacy, yet the spatial interplay among tumor proliferation, immune infiltration, and nutrient depletion is difficult to disentangle experimentally. This study employs a rigorously analyzed spatiotemporal reaction–diffusion model to identify the parameter regimes — defined by immune cytotoxic strength χ, tumor-driven immune recruitment η, and nutrient consumption rate θ — that determine whether a tumor is controlled, coexists chronically, or escapes immune surveillance entirely. Equilibrium analysis shows that the tumor-free state is unconditionally unstable, while a chronic coexistence state is locally asymptotically stable, establishing a theoretical basis for the observed persistence of tumors under partial immune control. Numerical simulations under two biologically distinct parameter regimes demonstrate spatially resolved outcomes: in the immune-competent regime, cytotoxic infiltration suppresses tumor growth and maintains nutrient availability, whereas in the aggressive regime, rapid nutrient depletion creates a necrotic core in the spatial interior consistent with avascular solid tumor morphology, while the immune response remains spatially peripheral and functionally insufficient. These findings identify critical thresholds in immune killing rate and nutrient supply below which computational models predict inevitable tumor escape, providing quantitative targets for immunotherapy augmentation strategies. High-order implicit time integration (SSHBBDF, order four, A-stable) ensures the biological fidelity of all simulations by resolving the multiscale stiffness inherent in coupled reaction–diffusion cancer models.

Abstract: Nonlinear dynamical systems often exhibit complex collective behavior arising from the interaction of multiple elementary modes. In this work we investigate the aggregation of a countable family of dynamical modes generated by Lotka-Volterra systems and study the resulting structure in a Hilbert space of observables. The classical Lotka-Volterra equations form a fundamental class of nonlinear models describing interacting populations through coupled differential equations, while Koopman operator theory provides a framework in which nonlinear dynamics can be represented as a linear evolution acting on observable functions.We show that a countable aggregation of such dynamical modes admits a well-defined limit in a Hilbert space when the coefficients belong to l2. The resulting aggregated observable evolves according to the associated Koopman semigroup, yielding a linear representation of the underlying nonlinear dynamics in the observable space. We further prove that the geometry induced by this aggregated dynamics admits a canonical class of equivalent metrics generated by coercive operators, ensuring that the stability topology of the system is independent of the particular metric chosen within this class.Finally, we illustrate the theoretical framework by introducing a social risk functional defined as a quadratic observable associated with the induced metric. This example demonstrates how application-specific quantities can naturally arise from the geometric structure generated by aggregated nonlinear dynamics.

Abstract: 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 frame- work is underscored by the April–May 2026 outbreak of Andes orthohantavirus aboard 9 the MV Hondius cruise ship – the first such cluster in a maritime setting, with three deaths reported across multiple countries (WHO Disease Outbreak News: https://www.who.int/emergencies/disease-outbreak-news/item/2026-DON599). This event revealed critical gaps in existing models that treat humans solely as dead-end spillover hosts. Here, we present an integrated computational study that combines three complementary analyses. Preliminarly, we performed the first phylogenetic analysis of such virus, idenifying as Orthoantavirus andensense the responsible for the vessel outbreak. Second, we performed 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), identifying 184 upregulated and 19 downregulated evidencing the role of dominated by interferon-stimulated genes (ISGs), including CXCL10, CXCL11, MX2, DDX58, IRF7, STAT1, OASL, and CMPK2. We constructed a protein–protein interaction (PPI) network from STRING (176 nodes, 3,210 edges) and applied a composite network centrality score to rank regulatory hubs, identifying ISG15, IRF1, CXCL10, STAT1, and DDX58 as the most central nodes. Pathway enrichment analysis con- firms strong activation of interferon signalling (Reactome, p = 1.3×10⁻⁶³), antiviral defence (Gene Ontology, p = 3.8 × 10⁻⁵⁸), and NF-κB pathways, with concurrent suppression of ribosomal translation. We finally developed a coupled SEIRD epi-demiological 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 35 public-health intervention design.