近年来,在预测登革热发病率方面,机器学习方法的应用迅速增加。然而,不同研究中采用的预测因素和模型差异很大。因此,我们进行了系统综述,总结了以往研究中的机器学习方法和预测因素.我们搜索了PubMed,ScienceDirect,以及截至2023年7月发表的文章的WebofScience数据库。所选论文不仅包括登革热发病率的预测,还包括机器学习方法。本研究共纳入23篇论文。预测因素包括气象因素(22,95.7%),历史登革热数据(14,60.9%),环境因素(4,17.4%),社会经济因素(4,17.4%),矢量监测数据(2,8.7%),和互联网搜索数据(3,13.0%)。在气象因素中,温度(20,87.0%),降雨量(20,87.0%),和相对湿度(14,60.9%)是最常用的。我们发现支持向量机(SVM)(6,26.1%),长短期记忆(LSTM)(5,21.7%),随机森林(RF)(4,17.4%),最小绝对收缩和选择算子(LASSO)(2,8.7%),合奏模型(2,8.7%),根据每篇文章中使用的评估指标,其他模型(4,17.4%)被确定为最佳模型。这些结果表明,气象因素是不可忽视的重要预测因素,SVM和LSTM算法是登革热预测中最常用的模型,具有良好的预测性能。这篇综述将有助于开发更强大的早期登革热预警系统,并促进机器学习方法在预测气候相关传染病中的应用。
In recent years, there has been a rapid increase in the application of machine learning methods about predicting the incidence of dengue fever. However, the predictive factors and models employed in different studies vary greatly. Hence, we conducted a systematic
review to summarize machine learning methods and predictors in previous studies. We searched PubMed, ScienceDirect, and Web of Science databases for articles published up to July 2023. The selected papers included not only the forecast of dengue incidence but also machine learning methods. A total of 23 papers were included in this study. Predictive factors included meteorological factors (22, 95.7%), historical dengue data (14, 60.9%), environmental factors (4, 17.4%), socioeconomic factors (4, 17.4%), vector surveillance data (2, 8.7%), and internet search data (3, 13.0%). Among meteorological factors, temperature (20, 87.0%), rainfall (20, 87.0%), and relative humidity (14, 60.9%) were the most commonly used. We found that Support Vector Machine (
SVM) (6, 26.1%), Long Short-Term Memory (LSTM) (5, 21.7%), Random Forest (RF) (4, 17.4%), Least Absolute Shrinkage and Selection Operator (LASSO) (2, 8.7%), ensemble model (2, 8.7%), and other models (4, 17.4%) were identified as the best models based on evaluation metrics used in each article. These results indicate that meteorological factors are important predictors that cannot be ignored and
SVM and LSTM algorithms are the most commonly used models in dengue fever prediction with good predictive performance. This
review will contribute to the development of more robust early dengue warning systems and promote the application of machine learning methods in predicting climate-related infectious diseases.