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Predictive models for evaluation of Legionella spp: a systematic review

2024-11-28Master thesis Embargoed access
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dc.contributor.advisorSilva, Maria Manuela Vieira da
dc.contributor.authorCarneiro, Bárbara Beatriz Andrade
dc.date.accessioned2025-02-27T11:21:40Z
dc.date.available2025-02-27T11:21:40Z
dc.date.issued2024-11-28
dc.date.submitted2024-11-28
dc.description.abstractLegionella spp. is a significant waterborne bacterium, responsible for Legionnaires’ disease. Predictive models are crucial for assessing contamination risks and preventing outbreaks. This systematic review aims to identify the various predictive models used directly or indirectly to evaluate the presence of Legionella, as well as assess the reliability of these predictive models. Following PRISMA guidelines, this review included studies from 2014 to 2024, retrieved form PubMed, ScienceDirect, and Web of Science, focusing on predictive models for Legionella. The SPIDER methodology was used to define eligibility criteria, and the JBI Critical Appraisal Checklist was applied to evaluate the methodological quality of the studies. Seventeen studies were included, utilizing machine learning models and traditional statistical models. Key variables influencing the growth od legionella spp. were water temperature, chlorine levels, biofilm presence, and environmental conditions such as humidity. Machine learning models demonstrated greater accuracy but posed challenges in interpretation, while traditional models offered more transparency but lacked precision in dynamic environments. The review found that predictive models incorporating environmental, biological and infrastructural variables offer significant potential in preventing outbreaks of Legionella. Machine learning models demonstrated higher accuracy, but their complexity posed challenges for practical implementation. Traditional models, while less complex, offered greater transparency but were limited in precision. Additionally, climate change-related factos, such as rising temperatures and increased rainfall, are likely to increase contamination risks from legionella spp., underscoring the need for more adaptable predictive models. While no single model is universally applicable, the integration of machine learning approaches with statistical methods appears promising. Future efforts should focus on refining these models to improve their generalization and adaptation to changing environmental conditions.por
dc.identifier.tid203874072
dc.identifier.urihttp://hdl.handle.net/10400.22/29732
dc.language.isoeng
dc.rights.uriN/A
dc.subjectLegionella spp
dc.subjectLegionella pneumophila
dc.subjectPredictive models
dc.subjectClimate change
dc.subjectPublic health
dc.titlePredictive models for evaluation of Legionella spp: a systematic reviewpor
dc.typemaster thesis
dspace.entity.typePublication

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