早期预警和预测COVID-19使用零膨胀负二项式回归模型和负二项式回归模型
Wanwan Zhou1, Daizheng Huang2, Qiuyu Liang3
1Department of Epidemiology and Biostatistics, Guangxi Medical University, 22 Shuangyong Road, Qingxiu District, Nanning, Guangxi, 530021, China.
BMC infectious diseases
|September 19, 2024
概括
百度搜索索引有效地帮助早期检测COVID-19和趋势预测. 搜索术语在疫情期间演变,突出其作为监控系统补充的作用.
科学领域:
- 流行病学 流行病学
- 公共卫生 公共卫生
- 数据科学数据科学数据科学
背景情况:
- 现有的传染病监测系统在早期发现疫情时面临着挑战.
- 百度搜索索引为实时监测公共卫生趋势提供了一个潜在的工具.
研究的目的:
- 调查百度搜索索引用于COVID-19的早期预警和流行趋势预测的有用性.
- 分析搜索引擎查询量与COVID-19病例数之间的相关性.
主要方法:
- 时间序列分析和斯皮尔曼相关性被用于分析每日COVID-19病例和百度搜索索引数据的8个关键词.
- 用零膨胀负二项式和负二项式回归模型进行预测.
主要成果:
- 百度搜索索引查找"流感"和"肺炎"等关键词,与病原体识别前病例增加相关.
- 在识别后",SARS"",肺炎"和"冠状病毒"等术语显示出强烈的相关性 (0.690.89).
- 搜索数据预测了COVID-19趋势,预测的时间长达15天,预测的病例在某些地区超过实际数量.
结论:
- 百度搜索索引是COVID-19早期预警和趋势预测的宝贵工具,尽管相关的关键词随着时间的推移而变化.
- 互联网搜索数据可以显著补充传统的监控系统,特别是在诊断延迟或资源稀缺的情况下.
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