Infectious diseases are a major health challenge for the worldwide population. Since their rapid spread can cause great distress to the real world, in addition to taking appropriate measures to curb the spread of infectious diseases in the event of an outbreak, proper prediction and early warning before the outbreak of the threat of infectious diseases can provide an important basis for early and reasonable response by the government health sector, reduce morbidity and mortality, and greatly reduce national losses. However, if only traditional medical data is involved, it may be too late or too difficult to implement prediction and early warning of an infectious outbreak. Recently, medical big data has become a research hotspot and has played an increasingly important role in public health, precision medicine, and disease prediction. In this paper, we focus on exploring a prediction and early warning method for influenza with the help of medical big data. It is well known that meteorological conditions have an influence on influenza outbreaks. So, we try to find a way to determine the early warning threshold value of influenza outbreaks through big data analysis concerning meteorological factors. Results show that, based on analysis of meteorological conditions combined with influenza outbreak history data, the early warning threshold of influenza outbreaks could be established with reasonable high accuracy.

中文翻译：

---

如何通过大数据分析和机器学习确定气象因素对流感的预警阈值

传染病是全球人口面临的重大健康挑战。由于它们的快速传播会给现实世界带来很大的困扰，除了在疫情爆发时采取适当的措施遏制传染病的传播外，在传染病威胁爆发之前进行适当的预测和预警可以提供一个政府卫生部门及早合理应对，降低发病率和死亡率，大大减少国家损失的重要依据。但是，如果只涉及传统的医学数据，那么实施传染病暴发的预测和预警可能为时已晚或太难。近年来，医疗大数据成为研究热点，在公共卫生、精准医疗、疾病预测等方面发挥着越来越重要的作用。在本文中，我们专注于探索一种借助医学大数据的流感预测预警方法。众所周知，气象条件对流感爆发有影响。因此，我们试图通过气象因素的大数据分析来寻找确定流感暴发预警阈值的方法。结果表明，基于气象条件分析并结合流感暴发历史数据，可以合理且高精度地建立流感暴发预警阈值。我们试图通过气象因素的大数据分析来寻找确定流感暴发预警阈值的方法。结果表明，基于气象条件分析并结合流感暴发历史数据，可以合理且高精度地建立流感暴发预警阈值。我们试图通过气象因素的大数据分析来寻找确定流感暴发预警阈值的方法。结果表明，基于气象条件分析并结合流感暴发历史数据，可以合理且高精度地建立流感暴发预警阈值。