arXiv subjects

Mohammed A. Y. Mohammed

Publications and source records attributed to Mohammed A. Y. Mohammed.

4 recordsLinked to original sources

arXiv2026

Threshold Dynamics and Integrated Control of Cholera Transmission with Sanitation and Vaccination

Cholera transmission is sustained by interacting human and environmental pathways, making integrated control essential in settings with inadequate water, sanitation, and hygiene infrastructure. We develop and analyze a Susceptible--Infected--Recovered--Water (SIWR) model with Monod-type environmental transmission and three interventions: human sanitation, environmental sanitation, and vaccination. The control reproduction number decomposes as $\mathcal{R}_0=\mathcal{R}_0^{(h)}+\mathcal{R}_0^{(e)}$, separating direct and environmental transmission and yielding closed-form elasticity measures of pathway-specific contributions. We further quantify interactions between sanitation measures through an explicit Bliss independence synergy index. We establish local and global stability of the disease-free equilibrium and prove that the model undergoes a forward transcritical bifurcation at $\mathcal{R}_0=1$, excluding backward bifurcation. Global sensitivity analysis identifies direct transmission, the half-saturation constant, pathogen shedding, and human sanitation as major determinants of epidemic burden. Numerical simulations demonstrate that coordinated sanitation interventions can produce synergistic reductions in epidemic burden, providing a quantitative framework for evaluating integrated cholera control strategies.

math.DS↗

arXiv2026

Comparing Bayesian and Frequentist Inference in Biological Models: A Comparative Analysis of Accuracy, Uncertainty, and Identifiability

Mathematical models support inference and forecasting in ecology and epidemiology, but results depend on the estimation framework. We compare Bayesian and Frequentist approaches across three biological models using four datasets: Lotka-Volterra predator-prey dynamics (Hudson Bay), a generalized logistic model (lung injury and 2022 U.S. mpox), and an SEIUR epidemic model (COVID-19 in Spain). Both approaches use a normal error structure to ensure a fair comparison. We first assessed structural identifiability to determine which parameters can theoretically be recovered from the data. We then evaluated practical identifiability and forecasting performance using four metrics: mean absolute error (MAE), mean squared error (MSE), 95 percent prediction interval (PI) coverage, and weighted interval score (WIS). For the Lotka-Volterra model with both prey and predator data, we analyzed three scenarios: prey only, predator only, and both. The Frequentist workflow used QuantDiffForecast (QDF) in MATLAB, which fits ODE models via nonlinear least squares and quantifies uncertainty through parametric bootstrap. The Bayesian workflow used BayesianFitForecast (BFF), which employs Hamiltonian Monte Carlo sampling via Stan to generate posterior distributions and diagnostics such as the Gelman-Rubin R-hat statistic. Results show that Frequentist inference performs best when data are rich and fully observed, while Bayesian inference excels when latent-state uncertainty is high and data are sparse, as in the SEIUR COVID-19 model. Structural identifiability clarifies these patterns: full observability benefits both frameworks, while limited observability constrains parameter recovery. This comparison provides guidance for choosing inference frameworks based on data richness, observability, and uncertainty needs.

q-bio.QM↗

arXiv2026

A Scalable Transaction Management Framework for Consistent Document-Oriented NoSQL Databases

NoSQL databases are widely used in modern applications due to their scalability and schema flexibility, yet they often rely on eventual consistency models that limit reliable transaction processing. This study proposes a four-stage transaction management framework for document-oriented NoSQL databases, with MongoDB as the reference platform. The framework combines transaction lifecycle management, operation classification, pre-execution conflict detection, and an adaptive locking strategy with timeout-based deadlock prevention. Formal correctness analysis shows that the proposed approach guarantees conflict serializability under defined conditions. An experimental evaluation using the Yahoo Cloud Serving Benchmark (YCSB) workloads A, B, and F, with concurrency levels ranging from 1 to 100 clients, demonstrates a reduction in transaction abort rates from 8.3% to 4.7%, the elimination of observed deadlocks, and a 34.2% decrease in latency variance. Throughput improvements ranging from 6.3% to 18.4% are observed under high concurrency, particularly for read-modify-write workloads. Distributed experiments on clusters of up to 9 nodes confirm scalability, achieving 15.2% higher throughput and 53% lower abort rates than baseline systems. Comparisons with MongoDB's native transactions, CockroachDB, and TiDB indicate that the proposed framework strikes a good balance between consistency guarantees and performance overhead. Sensitivity analysis identifies optimal parameter settings, including a lock timeout of 100 ms, an initial backoff of 10 ms, and a maximum backoff of 500 ms. These results show that carefully designed consistency mechanisms can significantly improve data integrity in NoSQL systems without undermining scalability.

cs.DB↗

arXiv2023

Enhancement of database access performance by improving data consistency in a non-relational database system (NoSQL)

This study aims to enhance data consistency in NoSQL databases, traditionally designed with BASE properties, as opposed to the strong consistency guaranteed by ACID principles in RDBMS. We introduce a comprehensive four-stage server-side model engineered explicitly for MongoDB. This model covers transaction management, bifurcation of read and write transactions, assessment of transaction readiness, and transaction execution via a specialized locking algorithm. Utilizing the Yahoo Cloud Services Benchmark (YCSB), particularly for update-heavy workloads (A, B, and F), our model exhibited significant improvements. Specifically, the average throughput, read, and update latencies improved to 2864.726 ms, 32806.275 ms, and 51845.629 ms, respectively, from the baseline metrics of 2914.110 ms, 26510.930 ms, and 32457.662 ms. These results demonstrate the efficacy of our proposed model in enhancing consistency not only in document-based NoSQL databases like MongoDB but also in other NoSQL database variants, including key-value, graph, and wide-column stores.

cs.DB↗