Research Article
A Mathematical Model of Helicobacter pylori Transmission Incorporating Antibiotic Resistance
Vincent Kyunguti Mwanthi*,
Stephen Karanja,
Loyford Njagi,
Mark Kimathi
Issue:
Volume 11, Issue 2, June 2026
Pages:
81-97
Received:
19 March 2026
Accepted:
30 March 2026
Published:
24 April 2026
Abstract: Helicobacter pylori (H. pylori) infection remains a major public health concern, particularly in developing countries with inadequate sanitation. The increasing rate of antibiotic resistance complicates treatment, prolongs infections, increases household transmission, and raises the risk of complications like stomach ulcers, highlighting the need for improved interventions. This study develops and analyzes a mathematical model of H. pylori transmission that incorporates antibiotic resistance, classifying infectious individuals into drug-sensitive, drug-resistant, and stomach ulcer cases. Individuals with drug-sensitive infections are treated with first-line antibiotics, those with drug-resistant infections are treated with second-line antibiotic therapy, and patients infected with stomach ulcer cases undergo specialized antibiotic management. Moreover, the transition from drug-resistant to drug-sensitive cases occurs as treatment suppresses resistant strains, letting sensitive strains dominate. Analytical results show that the basic reproduction number ℜc; is the sum of two reproduction numbers ℜs and ℜr representing the contribution of the sensitive and resistant strains, respectively. The disease-free equilibrium is locally asymptotically stable when ℜc<1, indicating possible eradication under effective control measures, while the endemic equilibrium is stable when ℜc>1, implying persistent transmission. Sensitivity analysis identifies critical parameters that influence the persistence of H. pylori in the population. Numerical simulations demonstrate that improved hygiene and sanitation, together with the use of appropriate and timely antibiotic therapy, significantly reduce the prevalence of sensitive and resistant strains, limit stomach ulcer development, and lower the overall infection burden.
Abstract: Helicobacter pylori (H. pylori) infection remains a major public health concern, particularly in developing countries with inadequate sanitation. The increasing rate of antibiotic resistance complicates treatment, prolongs infections, increases household transmission, and raises the risk of complications like stomach ulcers, highlighting the need f...
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Research Article
Fuzzy Logic Based Model for Predicting Neurasthenia in Nigerian University Students
Iyinoluwa Tioluwani Idowu*,
Emmanuel Oladimeji Ayodele
,
Sholanke Temitope Folasade,
Asmau Iyabo Ibrahim,
Peter Adebayo Idowu
Issue:
Volume 11, Issue 2, June 2026
Pages:
98-111
Received:
1 April 2026
Accepted:
25 April 2026
Published:
19 May 2026
DOI:
10.11648/j.ajmcm.20261102.12
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Abstract: This study developed a fuzzy logic–based predictive model for estimating the likelihood of neurasthenia among students, focusing on interpretable, and non-invasive factors. Risk factors, including academic stress, sleep quality, socioeconomic status, study habits, psychostimulant use, and emotional distress, were identified from literature and expert consultation. These were mapped to linguistic terms and triangular membership functions within a Mamdani fuzzy inference system designed in MATLAB Fuzzy Logic Toolbox (R2024b). A rule base was formulated from expert knowledge, and the system was simulated to evaluate the likelihood of Neurasthenia. The model incorporated six input variables and a three-level output classification: low likelihood, moderate likelihood, and high likelihood of having neurasthenia. Simulation results based on the 729 rules, indicated that 25% of cases were classified as low likelihood, 40% as moderate likelihood and 35% as high likelihood of Neurasthenia. These percentages reflect the distribution across a range of input combinations where unfavorable conditions, such as high academic stress, poor sleep, frequent stimulant use, and severe emotional distress, consistently produced a high-likelihood output, while positive conditions, such as effective study habits, good sleep, and low academic stress, resulted in low likelihood of Neurasthenia. The MATLAB desktop application successfully implemented the model, providing an intuitive interface for prediction. In conclusion. this study demonstrated the effectiveness of fuzzy logic in predicting neurasthenia risk by modeling uncertainty and enhancing interpretability. The tool provides a foundation for early detection and awareness in academic institutions, with potential applications in student health support systems.
Abstract: This study developed a fuzzy logic–based predictive model for estimating the likelihood of neurasthenia among students, focusing on interpretable, and non-invasive factors. Risk factors, including academic stress, sleep quality, socioeconomic status, study habits, psychostimulant use, and emotional distress, were identified from literature and expe...
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