Centro de Controle de Zoonoses da Secretaria Municipal de Saúde de Foz do Iguaçu,, Foz do Iguaçu, PR, Brazil.
Laboratório de Mosquitos Transmissores de Hematozoários, Instituto Oswaldo Cruz, Fiocruz - IOC, Rio de Janeiro, RJ, Brazil.
BMC Public Health. 2024 Sep 27;24(1):2587. doi: 10.1186/s12889-024-19942-4.
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.
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.
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.
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.
登革热控制干预措施的效果取决于一个有效的综合监测系统，该系统涉及分析与这种虫媒病毒的自然史和传播动力学相关的多个变量。与其他生物和非生物参数相关的昆虫学指标可以有力地描述与登革热传播风险相关的时空趋势。然而，在传统模型中，往往忽略了数据的非线性性质的不可预测性，以及生物数据固有的不确定性和主观性。
作为分析登革热相关数据的替代方法，我们设计了一种模糊逻辑方法来测试这些指标在不同类别中的集合，这些指标与程度真理的概念一致，以表征巴西一个流行地区埃及伊蚊传播登革热的成功。我们使用本地收集的昆虫学、人口统计学、环境和流行病学数据作为输入源，使用数字平台上免费提供的数据。输出变量，即传播风险，被汇总为三个类别：低、中和高。空间数据被地理参考，去模糊值被内插以创建一个地图，将我们的发现转化为当地公共卫生管理者和决策者，以指导进一步的病媒控制干预。
低、中、高传播风险区域的分类遵循了该地区预期的登革热发生的季节性趋势。模糊方法捕捉到了 2020 年的疫情，当时只有 14.06%的地区被归类为低风险。基于模糊系统的传播风险分类有效地预测了登革热传播的增加，因为在接下来的 15 天内，超过 75%的高风险地区的登革热发病率有所增加。
我们的研究表明，模糊逻辑能够描述城市与高传播风险地区相关的时空异质性，表明它可以被认为是综合监测系统的一部分，以支持及时决策。
