ArticleBMC public health2024
The fuzzy system ensembles entomological, epidemiological, demographic and environmental data to unravel the dengue transmission risk in an endemic city.
Article in BMC public health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
- Limited Spectroscopy Data and Machine Learning for Detection of Zika Virus Infection inTropical medicine and infectious disease · 2025Article
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10 authors.
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Abstract
backgroundThe effectiveness of dengue control interventions depends on an effective integrated surveillance system that involves analysis of multiple variables associated with the natural history and transmission dynamics of this arbovirus. Entomological indicators associated with other biotic and abiotic parameters can assertively characterize the spatiotemporal trends related to dengue transmission risk. However, the unpredictability of the non-linear nature of the data, as well as the uncertainty and subjectivity inherent in biological data are often neglected in conventional models.
methodsAs an alternative for analyzing dengue-related data, we devised a fuzzy-logic approach to test ensembles of these indicators across categories, which align with the concept of degrees of truth to characterize the success of dengue transmission by Aedes aegypti mosquitoes in an endemic city in Brazil. We used locally gathered entomological, demographic, environmental and epidemiological data as input sources using freely available data on digital platforms. The outcome variable, risk of transmission, was aggregated into three categories: low, medium, and high. Spatial data was georeferenced and the defuzzified values were interpolated to create a map, translating our findings to local public health managers and decision-makers to direct further vector control interventions.
resultsThe classification of low, medium, and high transmission risk areas followed a seasonal trend expected for dengue occurrence in the region. The fuzzy approach captured the 2020 outbreak, when only 14.06% of the areas were classified as low risk. The classification of transmission risk based on the fuzzy system revealed effective in predicting an increase in dengue transmission, since more than 75% of high-risk areas had an increase in dengue incidence within the following 15 days.
conclusionsOur study demonstrated the ability of fuzzy logic to characterize the city's spatiotemporal heterogeneity in relation to areas at high risk of dengue transmission, suggesting it can be considered as part of an integrated surveillance system to support timely decision-making.
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