| 基于互联网数据的传染病预测模型研究进展 |
| 修订日期：2024-01-16 点此下载全文 |
| 引用本文：何琪乐,张瑾瑶,吴卓存,等.基于互联网数据的传染病预测模型研究进展[J].医学信息学杂志,2024,45(2):32-37 |
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| 基金项目:国家社会科学基金重点项目(项目编号:22AZD089)；国家社会科学基金重大项目(项目编号:22&ZD141)；中国医学科学院医学与健康科技创新工程(项目编号:2022-I2M-1-019)。 |
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| 中文摘要:目的/意义 系统梳理基于互联网数据的传染病预测模型相关研究,助力实现传染病监测关口前移,为构建传染病智慧化立体防治体系提供参考。方法/过程 对Web of Science核心数据库和中国知网收录的近20年基于互联网数据的传染病监测预警研究发展历程及研究方向进行梳理,分析当前主要问题与挑战,总结常见预测模型及其优化方向。结果/结论 互联网传染病监测研究呈监测疾病多样化、数据来源精细化和专业化等趋势。由于互联网数据的复杂性和不确定性,现有模型大多仅适用于短时或实时预测。通过构建组合模型、加强多源数据融合、完善关键词与影响因素选择等方式,可进一步优化模型,加强拟合效果和预测能力。 |
| 中文关键词:传染病监测预警 流行病情报学 预测模型 搜索引擎 互联网 |
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| Research Progress of the Infectious Disease Prediction Models Based on Internet Data |
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| Abstract:Significance The paper systematically reviews relevant research on infectious disease prediction models based on internet data, helps to realize the advancement of infectious disease surveillance, and provides references for the construction of intelligent three-dimensional prevention and treatment system of infectious diseases. Method/Process The development history and research direction of infectious disease surveillance and early warning based on internet data collected in the core database of Web of Science and CNKI in the past 20 years are reviewed, major existing problems and challenges are analyzed, and common prediction models and their optimization directions are summarized. Result/Conclusion The study on internet infectious disease surveillance shows the trend of diversification of monitoring diseases, refinement and specialization of data sources. Due to the complexity and uncertainty of internet data, most of the existing models are only suitable for short-term or real-time prediction. By constructing a combination model, strengthening multi-source data fusion, improving the selection of Keywords and influencing factors, the model can be further optimized and the fitting effect and prediction capacity can be strengthened. |
| keywords:infectious disease surveillance and early warning epidemic intelligence prediction model search engine internet |
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