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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 124 Documents
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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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