Prediction of malaria using deep learning models: A case study on city clusters in the state of Amazonas, Brazil, from 2003 to 2018

ABSTRACT Background: Malaria is curable. Nonetheless, over 229 million cases of malaria were recorded in 2019, along with 409,000 deaths. Although over 42 million Brazilians are at risk of contracting malaria, 99% percent of all malaria cases in Brazil are located in or around the Amazon rainforest. Despite declining cases and deaths, malaria remains a major public health issue in Brazil. Accurate spatiotemporal prediction of malaria propagation may enable improved resource allocation to support efforts to eradicate the disease. Methods: In response to calls for novel research on malaria elimination strategies that suit local conditions, in this study, we propose machine learning (ML) and deep learning (DL) models to predict the probability of malaria cases in the state of Amazonas. Using a dataset of approximately 6 million records (January 2003 to December 2018), we applied k-means clustering to group cities based on their similarity of malaria incidence. We evaluated random forest, long-short term memory (LSTM) and dated recurrent unit (GRU) models and compared their performance. Results: The LSTM architecture achieved better performance in clusters with less variability in the number of cases, whereas the GRU presents better results in clusters with high variability. Although Diebold-Mariano testing suggested that both the LSTM and GRU performed comparably, GRU can be trained significantly faster, which could prove advantageous in practice. Conclusions: All models showed satisfactory accuracy and strong performance in predicting new cases of malaria, and each could serve as a supplemental tool to support regional policies and strategies.

摘要
背景：疟疾是可治愈的疾病。然而2019年全球共记录疟疾病例超2.29亿例，死亡病例达40.9万例。尽管巴西有超4200万人面临疟疾感染风险，但该国99%的疟疾病例均集中在亚马逊雨林及其周边区域。尽管疟疾病例与死亡数呈下降趋势，疟疾仍是巴西主要的公共卫生问题。精准的疟疾传播时空预测，有助于优化资源配置，助力疟疾根除工作。
方法：为响应适配本地情境的疟疾消除策略创新性研究的呼吁，本研究提出机器学习（Machine Learning, ML）与深度学习（Deep Learning, DL）模型，用于预测亚马逊州的疟疾病例发生概率。本研究使用2003年1月至2018年12月间约600万条记录的数据集，通过k-means聚类（k-means clustering）算法，依据疟疾发病率的相似性对城市进行分组。随后评估了随机森林、长短期记忆网络（Long-Short Term Memory, LSTM）以及门控循环单元（Gated Recurrent Unit, GRU）模型，并对比了三者的性能表现。
结果：LSTM架构在病例数变异性较低的聚类组中表现更优，而GRU则在高变异性聚类组中取得更佳结果。尽管迪博尔德-马里亚诺检验（Diebold-Mariano testing）显示LSTM与GRU的性能相当，但GRU的训练速度显著更快，这一优势在实际应用中颇为显著。
结论：所有模型在预测新增疟疾病例时均展现出令人满意的准确率与优异性能，均可作为辅助工具支撑区域疟疾防控政策与策略的制定。