NDLI: The fuzzy system ensembles entomological, epidemiological, demographic and environmental data to unravel the dengue transmission risk in an endemic city

Content Provider	Springer Nature : BioMed Central
Author	de Souza Leandro, André de Oliveira, Felipe Lopes, Renata Defante Rivas, Açucena Veleh Martins, Caroline Amaral Silva, Isaac Villela, Daniel A. M. Teixeira, Marcello Goulart Xavier, Samanta Cristina das Chagas Maciel-de-Freitas, Rafael
Abstract	Background The 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. Methods As 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. Results The 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. Conclusions Our 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.
Related Links	https://bmcpublichealth.biomedcentral.com/counter/pdf/10.1186/s12889-024-19942-4.pdf
Ending Page	14
Page Count	14
Starting Page	1
File Format	HTM / HTML
ISSN	14712458
DOI	10.1186/s12889-024-19942-4
Journal	BMC Public Health
Issue Number	1
Volume Number	24
Language	English
Publisher	BioMed Central
Publisher Date	2024-09-27
Access Restriction	Open
Subject Keyword	Public Health Medicine Epidemiology Biostatistics Vaccine Environmental Health Aedes Surveillance Arboviruses Vector control Hotspots Medicine/Public Health
Content Type	Text
Resource Type	Article
Subject	Public Health, Environmental and Occupational Health
Journal Impact Factor	3.5/2023
5-Year Journal Impact Factor	3.9/2023

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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