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5 articles for “compartmental models”
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Mathematical Models for COVID-19 Pandemic: A Comparative Analysis
Abstract: The COVID-19 pandemic has really underlined the importance of mathematical modeling in understanding disease-spread dynamics and especially informing public health interventions. The paper aims to provide a comprehensive comparative analysis of various mathematical models used for COVID-19 studies, with a focus on assumptions underlying those models, strengths, and also the limitations in their applications as well as special focus is given to compartmental models, agent-based models, machine learning-enhanced models, and …
Published in Recent Trends in Mathematics · Vol. 1, Issue 1, 2024 · pp. 42–53 Read article
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Mathematical Modeling of Epidemics Using Stochastic Differential Equations: A Review
Abstract: The accurate modeling of infectious disease dynamics is crucial for predicting outbreaks and informing public health interventions. While deterministic models such as the SIR (Susceptible-Infected-Recovered) framework have traditionally been used to understand disease transmission, they often fail to account for the randomness inherent in real-world scenarios. Disease spread is influenced by numerous uncertain factors, including individual behavioral changes, environmental fluctuations, and imperfect data reporting. These uncertainties can significantly impact model …
Published in Recent Trends in Mathematics · Vol. 1, Issue 2, 2024 · pp. 1–6 Read article
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A Threshold-Weighted Mathematical Fusion Model for Epidemic Outbreak Prediction Using SEIR Residual Dynamics and Cloud-Based Machine Learning
Abstract: Accurate prediction of epidemic outbreaks is critical for effective public health management, resource planning, early warning generation, and timely intervention by municipal authorities. Traditional compartmental models such as Susceptible–Exposed–Infectious–Recovered (SEIR) offer valuable epidemiological insights and mathematical interpretability; however, they may not adequately capture the complex nonlinear relationships present in real-world urban health systems. Conversely, data-driven machine learning techniques can identify hidden patterns in large datasets but often lack epidemiological structure …
Published in Research & Reviews : Journal of Statistics · Vol. 15, Issue 2, 2026 · pp. 12–19 Read article
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Accelerate Drug Development with Pharmacokinetic Softwares
Abstract: During continuously evolving technologies and mechanized equipments, there has been a tremendous growth in the pharmaceutical sector over the past few decades. New technologies, software, devices, and techniques are being developed or researched upon with each passing day. By leveraging pharmacokinetic software, researchers can efficiently analyze vast datasets from preclinical and clinical studies, extracting actionable insights that inform decision-making early in the development cycle. This predictive capability not only accelerates …
Published in Research & Reviews: A Journal of Drug Formulation, Development and Production · Vol. 11, Issue 3, 2024 · pp. 1–8 Read article
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Assessment of Fire Resistance of Offshore Structures under Special Environmental Loads: Emphasizing Structural Integrity and Safety Measures for Extreme Conditions
Abstract: Offshore structures are exposed to a myriad of environmental challenges, including high temperatures and fire hazards, necessitating robust fire resistance measures to ensure structural integrity and the safety of personnel and facilities. This study aims to evaluate the fire resistance of offshore structures under special environmental loads, with a focus on the structural integrity and safety measures in place to withstand extreme conditions. The research methodology includes a comprehensive review …
Published in Journal of Offshore Structure and Technology · Vol. 11, Issue 1, 2024 · pp. 1–9 Read article