基于数据融合的血吸虫病感染预测框架研究

Teegwende Zougmore, Sadouanouan Malo, B. Gueye, S. Ouaro
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引用次数: 0

摘要

我们提出了一个概念框架来预测淡水来源血吸虫病寄生虫侵染的风险。我们的方法旨在结合预测模型输出的两个信息源。提议的框架被分解成三个y形分支。左分支是基于物联网平台采集数据的机器学习算法构建的水质预测模型。这些数据代表了淡水来源的物理和化学参数,这些参数影响引起血吸虫病的蜗牛和寄生虫的发育。右边的分支是一个非自治数学模型,它通过推导出的繁殖数$R_{0}$决定了血吸虫病传播生命周期中所有参与者的密度演化。在中间分支中,考虑到两个信息的不确定性和互补性,进行融合处理。融合的输出是关于感染风险的最终决定。这项工作的重点是识别适用于水质预测的机器学习算法和数学模型的识别。工作还包括给出了融合问题的特点处理。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Toward a Data Fusion Based Framework to Predict Schistosomiasis Infection
We propose a conceptual framework to predict the risk of freshwater source infestation by Schistosomiasis parasites. Our approach aims to combine two sources of information which are outputs of prediction models. The proposed framework is broken down into three Y-shaped branches. The left branch is a water quality prediction model built on the basis of machine learning algorithms applied on data collected by an IoT platform. These data represent physical and chemical parameters of a freshwater source which affect the development of snails and parasites that cause Schistosomiasis. The branch on the right is a non autonomous mathematical model which through its derived reproduction number $R_{0}$ determines the density evolution of all actors involved in Schistosomiasis transmission life cycle. In the middle branch happens a fusion process which combines the two information by taking into account their uncertainty and complementary. The output of the fusion is the final decision about the risk of infestation. This work has focused on the identification of applicable machine learning algorithms for water quality prediction and the identification of a mathematical model. The work has consisted also to give the characteristics of the fusion problem to handle.
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