Modeling and simulation of distribution and drug resistance of major pathogens in patients with respiratory system infections
Abstract Background Respiratory tract infections (RTIs) are one of the leading causes of morbidity and mortality worldwide. The increase in antimicrobial resistance in respiratory pathogens poses a major challenge to the effective management of these infections. Objective To investigate the distribu...
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2025-01-01
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| Series: | BMC Infectious Diseases |
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| Online Access: | https://doi.org/10.1186/s12879-025-10549-7 |
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| author | Li Yang Ermin Liang Yali Gao |
| author_facet | Li Yang Ermin Liang Yali Gao |
| author_sort | Li Yang |
| collection | DOAJ |
| description | Abstract Background Respiratory tract infections (RTIs) are one of the leading causes of morbidity and mortality worldwide. The increase in antimicrobial resistance in respiratory pathogens poses a major challenge to the effective management of these infections. Objective To investigate the distribution of major pathogens of RTIs and their antimicrobial resistance patterns in a tertiary care hospital and to develop a mathematical model to explore the relationship between pathogen distribution and antimicrobial resistance. Methods Five hundred patients with RTIs were included in the study and 475 bacterial strains were isolated from their respiratory specimens. Antimicrobial susceptibility testing and analysis of influencing factors were performed. A mathematical model was developed to simulate the relationship between pathogen distribution and drug resistance. Results The most common pathogens were Streptococcus pneumoniae (30%), Haemophilus influenzae (20%), Pseudomonas aeruginosa (15%), Staphylococcus aureus (10%) and Klebsiella pneumoniae (10%). The distribution of pathogens varied according to age group and type of RTIs, with higher proportions of Pseudomonas aeruginosa and Staphylococcus aureus in hospital-acquired and ventilator-associated pneumonia. Isolated pathogens showed high and increasing rates of resistance to commonly used antibiotics. Model simulations suggest that a shift in the distribution of pathogens toward more resistant strains may lead to a significant increase in overall resistance rates, even if antibiotic use patterns remain unchanged. Conclusion This study emphasizes the importance of regular monitoring of respiratory pathogen distribution and antimicrobial resistance patterns and the need for a comprehensive approach to managing RTIs, including implementation of antibiotic stewardship programs, infection control measures, and development of new therapies. |
| format | Article |
| id | doaj-art-ba09d311939242a5a373f7dd404499f7 |
| institution | Kabale University |
| issn | 1471-2334 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Infectious Diseases |
| spelling | doaj-art-ba09d311939242a5a373f7dd404499f72025-02-02T12:10:33ZengBMCBMC Infectious Diseases1471-23342025-01-0125111510.1186/s12879-025-10549-7Modeling and simulation of distribution and drug resistance of major pathogens in patients with respiratory system infectionsLi Yang0Ermin Liang1Yali Gao2Department of Respiratory Medicine, Anting Hospital of Jiading DistrictDepartment of Respiratory Medicine, Anting Hospital of Jiading DistrictDepartment of Respiratory Medicine, Anting Hospital of Jiading DistrictAbstract Background Respiratory tract infections (RTIs) are one of the leading causes of morbidity and mortality worldwide. The increase in antimicrobial resistance in respiratory pathogens poses a major challenge to the effective management of these infections. Objective To investigate the distribution of major pathogens of RTIs and their antimicrobial resistance patterns in a tertiary care hospital and to develop a mathematical model to explore the relationship between pathogen distribution and antimicrobial resistance. Methods Five hundred patients with RTIs were included in the study and 475 bacterial strains were isolated from their respiratory specimens. Antimicrobial susceptibility testing and analysis of influencing factors were performed. A mathematical model was developed to simulate the relationship between pathogen distribution and drug resistance. Results The most common pathogens were Streptococcus pneumoniae (30%), Haemophilus influenzae (20%), Pseudomonas aeruginosa (15%), Staphylococcus aureus (10%) and Klebsiella pneumoniae (10%). The distribution of pathogens varied according to age group and type of RTIs, with higher proportions of Pseudomonas aeruginosa and Staphylococcus aureus in hospital-acquired and ventilator-associated pneumonia. Isolated pathogens showed high and increasing rates of resistance to commonly used antibiotics. Model simulations suggest that a shift in the distribution of pathogens toward more resistant strains may lead to a significant increase in overall resistance rates, even if antibiotic use patterns remain unchanged. Conclusion This study emphasizes the importance of regular monitoring of respiratory pathogen distribution and antimicrobial resistance patterns and the need for a comprehensive approach to managing RTIs, including implementation of antibiotic stewardship programs, infection control measures, and development of new therapies.https://doi.org/10.1186/s12879-025-10549-7Respiratory tract infectionsAntimicrobial resistancePathogen distributionMathematical modelingAntibiotic stewardshipInfection control |
| spellingShingle | Li Yang Ermin Liang Yali Gao Modeling and simulation of distribution and drug resistance of major pathogens in patients with respiratory system infections BMC Infectious Diseases Respiratory tract infections Antimicrobial resistance Pathogen distribution Mathematical modeling Antibiotic stewardship Infection control |
| title | Modeling and simulation of distribution and drug resistance of major pathogens in patients with respiratory system infections |
| title_full | Modeling and simulation of distribution and drug resistance of major pathogens in patients with respiratory system infections |
| title_fullStr | Modeling and simulation of distribution and drug resistance of major pathogens in patients with respiratory system infections |
| title_full_unstemmed | Modeling and simulation of distribution and drug resistance of major pathogens in patients with respiratory system infections |
| title_short | Modeling and simulation of distribution and drug resistance of major pathogens in patients with respiratory system infections |
| title_sort | modeling and simulation of distribution and drug resistance of major pathogens in patients with respiratory system infections |
| topic | Respiratory tract infections Antimicrobial resistance Pathogen distribution Mathematical modeling Antibiotic stewardship Infection control |
| url | https://doi.org/10.1186/s12879-025-10549-7 |
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