标题： 使用路径签名预测巴西市镇的高登革热发病率 显示英文标题

标题： Predicting high dengue incidence in municipalities of Brazil using path signatures

摘要： 预测是否会出现传染病高发情况对于健康监测至关重要。 在登革热的流行病学中，环境条件可以显著影响病毒的传播。 结合流行病学指标和环境变量可以提高登革热发病率风险的预测。 本研究分析了巴西各市每周病例数、温度和湿度的数据集，以2014年至2023年的数据为基础，预测高登革热发病率的风险。 该框架涉及构建路径签名并应用套索回归进行二元结果分析。 灵敏度达到75%，而特异性非常高，范围从75%到100%。 最佳性能是在观察35周后收集的信息中表现出来的，使用嵌入技术进行数据增强。 路径签名的使用有效地捕捉了由影响登革热传播的流行病学和气候变量提供的信息流。 该框架可能在优化资源以预测巴西及其他国家各市的高登革热风险方面起到关键作用，在学习这些国家模式之后。 显示英文摘要

摘要： Predicting whether to expect a high incidence of infectious diseases is critical for health surveillance. In the epidemiology of dengue, environmental conditions can significantly impact the transmission of the virus. Utilizing epidemiological indicators alongside environmental variables can enhance predictions of dengue incidence risk. This study analyzed a dataset of weekly case numbers, temperature, and humidity across Brazilian municipalities to forecast the risk of high dengue incidence using data from 2014 to 2023. The framework involved constructing path signatures and applying lasso regression for binary outcomes. Sensitivity reached 75%, while specificity was extremely high, ranging from 75% to 100%. The best performance was observed with information gathered after 35 weeks of observations using data augmentation via embedding techniques. The use of path signatures effectively captures the stream of information given by epidemiological and climate variables that influence dengue transmission. This framework could be instrumental in optimizing resources to predict high dengue risk in municipalities in Brazil and other countries after learning these country patterns.