Background

Dengue modeling is a research topic that has increased in recent years. Early prediction and decision-making are key factors to control dengue. This Systematic Literature Review (SLR) analyzes three modeling approaches of dengue: diagnostic, epidemic, intervention. These approaches require models of prediction, prescription and optimization. This SLR establishes the state-of-the-art in dengue modeling, using machine learning, in the last years.

Methods

Several databases were selected to search the articles. The selection was made based on Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology. Sixty-four articles were obtained and analyzed to describe their strengths and limitations. Finally, challenges and opportunities for research on machine-learning for dengue modeling were identified.

Results

Logistic regression was the most used modeling approach for the diagnosis of dengue (59.1%). The analysis of the epidemic approach showed that linear regression (17.4%) is the most used technique within the spatial analysis. Finally, the most used intervention modeling is General Linear Model with 70%.

Conclusions

We conclude that cause-effect models may improve diagnosis and understanding of dengue. Models that manage uncertainty can also be helpful, because of low data-quality in healthcare. Finally, decentralization of data, using federated learning, may decrease computational costs and allow model building without compromising data security.

中文翻译：

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基于机器学习技术的登革热模型：系统文献综述

背景

登革热建模是近年来增加的一个研究课题。早期预测和决策是控制登革热的关键因素。本系统文献综述 (SLR) 分析了登革热的三种建模方法：诊断、流行、干预。这些方法需要预测、处方和优化模型。该 SLR 在过去几年中使用机器学习建立了登革热建模的最新技术。

方法

选择了几个数据库来搜索文章。该选择是根据系统评价和元分析的首选报告项目 (PRISMA) 方法进行的。获得并分析了 64 篇文章以描述它们的优点和局限性。最后，确定了登革热建模的机器学习研究的挑战和机遇。

结果

Logistic 回归是诊断登革热最常用的建模方法 (59.1%)。流行病方法的分析表明，线性回归 (17.4%) 是空间分析中最常用的技术。最后，最常用的干预建模是一般线性模型，占 70%。

结论

我们得出结论，因果模型可能会提高对登革热的诊断和理解。由于医疗保健中的数据质量低，管理不确定性的模型也很有帮助。最后，使用联邦学习实现数据去中心化，可以降低计算成本并允许在不影响数据安全性的情况下构建模型。