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Communication in Biomathematical Sciences
Published by Indonesian Biomathematical Society
ISSN : -     EISSN : 25492896     DOI : 10.5614/cbms
Core Subject : Social,
Social Sciences
Full research articles in the area of Applications of Mathematics in biological processes and phenomena
Arjuna Subject : Matematika - Matematika Terapan
Articles 6 Documents
 1 
Search results for , issue "Vol. 8 No. 2 (2025)" : 6 Documents clear
Optimal Control Strategies for the Population Management of the Bali Starling: A Mathematical Modeling Approach Gandhiadi, G.K.; Tastrawati, N.K.; Gautama, P.W.; Dharmawan, K.
Communication in Biomathematical Sciences Vol. 8 No. 2 (2025)
Publisher : The Indonesian Bio-Mathematical Society

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/cbms.2025.8.2.3

Abstract

The Bali Starling (Leucopsar rothschildi), an endemic species of Bali, faces severe threats from habitat loss, poaching, and environmental changes, necessitating effective conservation strategies. This study presents a mathematical model to describe the population dynamics of the Bali Starling within the breeding center at USS Tegal Bunder, TNBB, integrating optimal control theory to improve conservation efforts. The model incorporates key biological factors such as growth, transfer, and habituation processes, and utilizes Pontryagin’s Maximum Principle to determine an optimal control strategy that balances population sustainability with resource efficiency. Numerical simulations compare controlled and uncontrolled scenarios, highlighting the impact of different control cost weights (q) on population management. The results suggest that moderate control interventions (q = 0.06 − 0.10) are most effective, ensuring sustainable population growth while min- imizing intervention costs. These findings provide valuable insights for optimizing captive breeding programs and offer a scientific basis for adaptive conservation strategies to protect endangered species like the Bali Starling.
A Nonlinear Delay Mathematical Model for Predicting Chlamydia Dynamics and Intervention Effects Zeb, Shah; Mohd Yatim, Siti Ainor; Kamran, Ayesha; Zulfiqar, Sabahat; Rafiq, Muhammad; Mohamad Noor, Nursyazwani
Communication in Biomathematical Sciences Vol. 8 No. 2 (2025)
Publisher : The Indonesian Bio-Mathematical Society

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/cbms.2025.8.2.1

Abstract

Chlamydia is a widespread sexually transmitted infection in Europe, often leading to complications such as rectal discomfort, throat inflammation, and reactive arthritis. This study presents a novel nonlinear delay differential equation model that enhances the classical SEIAISR framework to more accurately represent Chlamydia transmission dynamics. The model integrates biologically justified exponential time delays to reflect incubation periods and the delayed impact of interventions like condom use, routine screening, partner reduction, and microbiome health. We establish the existence and uniqueness of solutions using the Banach fixed point theorem and analyze the model’s dynamics by computing the basic reproduction number and studying equilibria and their stability via Lyapunov functions and Routh-Hurwitz criteria. A sensitivity analysis identifies key epidemiological drivers. For numerical simulation, we employ Euler’s method, the Runge-Kutta 4th order (RK4) method, and a specially developed non-standard finite difference (NSFD) scheme. The NSFD approach preserves critical properties such as positivity and stability, making it suitable for realistic long-term predictions. Results highlight the importance of timely interventions and show the superiority of structurepreserving numerical methods. The findings support the development of more targeted and effective strategies to reduce chlamydia transmission and complications among high-risk groups, reinforcing evidence-based decisionmaking within the healthcare system.
A Novel Mathematical Model for Overweight, Obesity, and Their Impact on Diabetes and Hypertension Delgado Moya, Erick Manuel; Rodriguez, Ranses Alfonso; Pietrus, Alain; Bernard, Severine
Communication in Biomathematical Sciences Vol. 8 No. 2 (2025)
Publisher : The Indonesian Bio-Mathematical Society

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/cbms.2025.8.2.5

Abstract

In this paper, we present a new mathematical model describing the dynamics of overweight and obesity and their impact on diabetes and hypertension. In constructing the model, we consider negative and positive interactions among individuals with normal weight, overweight, and obesity, as well as social factors influencing overweight and hypertension diagnoses. As a novel contribution to transmission dynamics, we interpret the basic reproduction number from two perspectives: negative and positive interactions. Focusing on parameters linked to social factors and their health impact, we present theoretical results characterizing their influence on the basic reproduction number and compute corresponding sensitivity indices. Additionally, we perform a global sensitivity analysis of model parameters using first- and total-order Sobol’ indices with various methods and sampling techniques, concluding that parameters associated with social factors are among the most influential. We conduct computational simulations of the basic reproduction number and model’s compartments to examine the influence of social-factor parameters on overweight and hypertension. Our findings indicate the need to explore strategies to prevent the rise of overweight, obesity, and diabetes in the population. Social factors associated with overweight and hypertension diagnosis have a substantial impact on the progression of these dynamics. Recognizing this influence enables the identification of the most vulnerable groups and the design of more precise and effective interventions.
Mathematical Modelling of Carbon Dioxide Emissions in Agricultural Systems Mor, Ashish; Das, Kalyan; Srinivas, M.N.
Communication in Biomathematical Sciences Vol. 8 No. 2 (2025)
Publisher : The Indonesian Bio-Mathematical Society

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/cbms.2025.8.2.2

Abstract

This study formulates a dynamic mathematical model to investigate the interplay between human activities and CO2 emissions within the context of agriculture. The model incorporates a system of differential equations describing the interactions among human population growth (H1), human economic activities (H2), atmospheric CO2 concentration (H3), forest biomass density (H4), and vehicle population (H5). Key processes include the effects of deforestation, economic activities, and vehicle emissions on CO2 levels, as well as the mitigating role of forest biomass.The model parameters account for natural growth rates, carrying capacities, and interaction coefficients that represent both the exacerbation and alleviation of CO2 emissions. The delay parameter τ captures the temporal lag in the effects of population growth and deforestation. This framework aims to provide insights into the dynamic interactions and feedback loops influencing CO2 emissions, with a particular emphasis on sustainable practices and policies to mitigate environmental degradation in agricultural contexts.
Modeling COVID-19 Dynamics with a Medical Treatment Strategy: A Case Study of Thailand Chen, Yinghui; Modnak, Chairat
Communication in Biomathematical Sciences Vol. 8 No. 2 (2025)
Publisher : The Indonesian Bio-Mathematical Society

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/cbms.2025.8.2.6

Abstract

Since 2020, Thailand has been impacted by the COVID-19 pandemic, which continues to persist into 2025. In response, the country has implemented various disease control measures, including public health campaigns and vaccination programs. While these strategies are still in place, they are now applied with less intensity, allowing people to return to a more normal way of life. However, this relaxed approach can contribute to continued disease transmission. In this study, we shift focus from conventional control measures-such as vaccination, mask-wearing, and social distancing-to strategies aimed at coexisting with the disease while minimizing its spread. Specifically, we investigate the impact of treating symptomatic and severe patients to reduce their infectiousness and thereby lower the risk of transmission to others. To achieve this, we develop a mathematical model of COVID-19 transmission dynamics and apply it using Thailand's 2025 data. We analyze the stability of both the disease-free and endemic equilibrium points and explore an optimal control problem related to medical treatment strategies. Our findings suggest that reducing the infectiousness of symptomatic and severe cases through effective treatment can help slow down the spread of COVID-19, supporting safer coexistence in a society returning to normalcy.
A Fractional SIR Model for Hepatitis A Virus: Lyapunov Stability and Effects of Awareness and Vaccination Safi, Burhanuddin; Das, Agniva; Hasmani, A.H.
Communication in Biomathematical Sciences Vol. 8 No. 2 (2025)
Publisher : The Indonesian Bio-Mathematical Society

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/cbms.2025.8.2.4

Abstract

Although Hepatitis A Virus (HAV) causes non-chronic infection, it poses serious health threats, particularly among children and older individuals due to poor sanitation and weak immunity. To better capture the memory-dependent progression of HAV, a novel SIR-type epidemic model is developed using Caputo fractional derivatives. The model incorporates awareness campaigns and a precautionary vaccination strategy represented by a Holling type-II functional response. We analytically established positivity, boundedness, and both local and global stability of equilibrium points using Jacobian matrices and Lyapunov functions are presented. Realworld data from the United States are used to estimate possible parameters through mean absolute error (MAE) minimization. Additionally, numerical simulations were perforemd to support the qualitative results revealing that fractional-order dynamics offer more accurate and realistic forecasts compared to classical integer-order models. Moreover, sensitivity analysis further identified the infection rate and recruitment rate as dominant drivers of HAV spread. Overall, the findings confirm that combining awareness and vaccination substantially reduces the infection levels and that fractional modelling provides critical advantages in disease forecasting and control planning.

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