Chinese Center for Disease Control and Prevention, 102206 Beijing, China.
School of Computer Science and Technology, Beijing Institute of Technology, 100081 Beijing, China.
Comput Math Methods Med. 2020 Dec 2;2020:8845459. doi: 10.1155/2020/8845459. eCollection 2020.
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.
传染病是全球人口面临的主要健康挑战。由于其快速传播可能会给现实世界带来巨大的困扰，因此除了在传染病爆发时采取适当措施遏制其传播外，在传染病威胁爆发之前进行适当的预测和预警，可以为政府卫生部门提供重要的早期合理应对基础，降低发病率和死亡率，并大大减少国家损失。但是，如果仅涉及传统的医疗数据，可能为时已晚或难以实现传染病爆发的预测和预警。最近，医疗大数据已成为研究热点，并在公共卫生、精准医学和疾病预测方面发挥着越来越重要的作用。本文主要研究借助医疗大数据对流感进行预测和预警的方法。众所周知，气象条件会对流感爆发产生影响。因此，我们试图通过对气象因素相关的大数据进行分析，找到一种确定流感爆发预警阈值的方法。结果表明，基于气象条件的分析以及流感爆发历史数据，可合理准确地建立流感爆发的预警阈值。
