Membrane computing simulation of sexually transmitted bacterial infections in hotspots of individuals with various risk behaviors
ABSTRACTThe epidemiology of sexually transmitted infections (STIs) is complex due to the coexistence of various pathogens, the variety of transmission modes derived from sexual orientations and behaviors at different ages and genders, and sexual contact hotspots resulting in network transmission. Th...
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| Format: | Article |
| Language: | English |
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American Society for Microbiology
2024-02-01
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| Series: | Microbiology Spectrum |
| Subjects: | |
| Online Access: | https://journals.asm.org/doi/10.1128/spectrum.02728-23 |
| _version_ | 1797322735332884480 |
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| author | Marcelino Campos Juan Carlos Galán Mario Rodríguez-Domínguez José M. Sempere Carlos Llorens Fernando Baquero |
| author_facet | Marcelino Campos Juan Carlos Galán Mario Rodríguez-Domínguez José M. Sempere Carlos Llorens Fernando Baquero |
| author_sort | Marcelino Campos |
| collection | DOAJ |
| description | ABSTRACTThe epidemiology of sexually transmitted infections (STIs) is complex due to the coexistence of various pathogens, the variety of transmission modes derived from sexual orientations and behaviors at different ages and genders, and sexual contact hotspots resulting in network transmission. There is also a growing proportion of recreational drug users engaged in high-risk sexual activities, as well as pharmacological self-protection routines fostering non-condom practices. The frequency of asymptomatic patients makes it difficult to develop a comprehensive approach to STI epidemiology. Modeling approaches are required to deal with such complexity. Membrane computing is a natural computing methodology for the virtual reproduction of epidemics under the influence of deterministic and stochastic events with an unprecedented level of granularity. The application of the LOIMOS program to STI epidemiology illustrates the possibility of using it to shape appropriate interventions. Under the conditions of our basic landscape, including sexual hotspots of individuals with various risk behaviors, an increase in condom use reduces STIs in a larger proportion of heterosexuals than in same-gender sexual contacts and is much more efficient for reducing Neisseria gonorrhoeae than Chlamydia and lymphogranuloma venereum infections. Amelioration from diagnostic STI screening could be instrumental in reducing N. gonorrhoeae infections, particularly in men having sex with men (MSM), and Chlamydia trachomatis infections in the heterosexual population; however, screening was less effective in decreasing lymphogranuloma venereum infections in MSM. The influence of STI epidemiology of sexual contacts between different age groups (<35 and ≥35 years) and in bisexual populations was also submitted for simulation.IMPORTANCEThe epidemiology of sexually transmitted infections (STIs) is complex and significantly influences sexual and reproductive health worldwide. Gender, age, sexual orientation, sexual behavior (including recreational drug use and physical and pharmacological protection practices), the structure of sexual contact networks, and the limited application or efficiency of diagnostic screening procedures create variable landscapes in different countries. Modeling techniques are required to deal with such complexity. We propose the use of a simulation technology based on membrane computing, mimicking in silico STI epidemics under various local conditions with an unprecedented level of detail. This approach allows us to evaluate the relative weight of the various epidemic drivers in various populations at risk and the possible outcomes of interventions in particular epidemiological landscapes. |
| first_indexed | 2024-03-08T05:18:36Z |
| format | Article |
| id | doaj.art-007d90c43d55428187da9571af6a6fb0 |
| institution | Directory Open Access Journal |
| issn | 2165-0497 |
| language | English |
| last_indexed | 2024-03-08T05:18:36Z |
| publishDate | 2024-02-01 |
| publisher | American Society for Microbiology |
| record_format | Article |
| series | Microbiology Spectrum |
| spelling | doaj.art-007d90c43d55428187da9571af6a6fb02024-02-06T14:04:55ZengAmerican Society for MicrobiologyMicrobiology Spectrum2165-04972024-02-0112210.1128/spectrum.02728-23Membrane computing simulation of sexually transmitted bacterial infections in hotspots of individuals with various risk behaviorsMarcelino Campos0Juan Carlos Galán1Mario Rodríguez-Domínguez2José M. Sempere3Carlos Llorens4Fernando Baquero5Department of Microbiology, Ramón y Cajal University Hospital, IRYCIS, Madrid, SpainDepartment of Microbiology, Ramón y Cajal University Hospital, IRYCIS, Madrid, SpainDepartment of Microbiology, Ramón y Cajal University Hospital, IRYCIS, Madrid, SpainValencian Research Institute for Artificial Intelligence (VRAIN), Polytechnic University of Valencia, Valencia, SpainBiotechvana, Valencia, Scientific Park University of Valencia, Paterna, SpainDepartment of Microbiology, Ramón y Cajal University Hospital, IRYCIS, Madrid, SpainABSTRACTThe epidemiology of sexually transmitted infections (STIs) is complex due to the coexistence of various pathogens, the variety of transmission modes derived from sexual orientations and behaviors at different ages and genders, and sexual contact hotspots resulting in network transmission. There is also a growing proportion of recreational drug users engaged in high-risk sexual activities, as well as pharmacological self-protection routines fostering non-condom practices. The frequency of asymptomatic patients makes it difficult to develop a comprehensive approach to STI epidemiology. Modeling approaches are required to deal with such complexity. Membrane computing is a natural computing methodology for the virtual reproduction of epidemics under the influence of deterministic and stochastic events with an unprecedented level of granularity. The application of the LOIMOS program to STI epidemiology illustrates the possibility of using it to shape appropriate interventions. Under the conditions of our basic landscape, including sexual hotspots of individuals with various risk behaviors, an increase in condom use reduces STIs in a larger proportion of heterosexuals than in same-gender sexual contacts and is much more efficient for reducing Neisseria gonorrhoeae than Chlamydia and lymphogranuloma venereum infections. Amelioration from diagnostic STI screening could be instrumental in reducing N. gonorrhoeae infections, particularly in men having sex with men (MSM), and Chlamydia trachomatis infections in the heterosexual population; however, screening was less effective in decreasing lymphogranuloma venereum infections in MSM. The influence of STI epidemiology of sexual contacts between different age groups (<35 and ≥35 years) and in bisexual populations was also submitted for simulation.IMPORTANCEThe epidemiology of sexually transmitted infections (STIs) is complex and significantly influences sexual and reproductive health worldwide. Gender, age, sexual orientation, sexual behavior (including recreational drug use and physical and pharmacological protection practices), the structure of sexual contact networks, and the limited application or efficiency of diagnostic screening procedures create variable landscapes in different countries. Modeling techniques are required to deal with such complexity. We propose the use of a simulation technology based on membrane computing, mimicking in silico STI epidemics under various local conditions with an unprecedented level of detail. This approach allows us to evaluate the relative weight of the various epidemic drivers in various populations at risk and the possible outcomes of interventions in particular epidemiological landscapes.https://journals.asm.org/doi/10.1128/spectrum.02728-23sexually transmitted infectionsNeisseria gonorrhoeaeChlamydia trachomatislymphogranuloma venereumsexual orientationsexual behavior |
| spellingShingle | Marcelino Campos Juan Carlos Galán Mario Rodríguez-Domínguez José M. Sempere Carlos Llorens Fernando Baquero Membrane computing simulation of sexually transmitted bacterial infections in hotspots of individuals with various risk behaviors Microbiology Spectrum sexually transmitted infections Neisseria gonorrhoeae Chlamydia trachomatis lymphogranuloma venereum sexual orientation sexual behavior |
| title | Membrane computing simulation of sexually transmitted bacterial infections in hotspots of individuals with various risk behaviors |
| title_full | Membrane computing simulation of sexually transmitted bacterial infections in hotspots of individuals with various risk behaviors |
| title_fullStr | Membrane computing simulation of sexually transmitted bacterial infections in hotspots of individuals with various risk behaviors |
| title_full_unstemmed | Membrane computing simulation of sexually transmitted bacterial infections in hotspots of individuals with various risk behaviors |
| title_short | Membrane computing simulation of sexually transmitted bacterial infections in hotspots of individuals with various risk behaviors |
| title_sort | membrane computing simulation of sexually transmitted bacterial infections in hotspots of individuals with various risk behaviors |
| topic | sexually transmitted infections Neisseria gonorrhoeae Chlamydia trachomatis lymphogranuloma venereum sexual orientation sexual behavior |
| url | https://journals.asm.org/doi/10.1128/spectrum.02728-23 |
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