使用百度搜索索引预测中国肺结核发病率:一个ARIMAX模型方法
Jing Yang1,2, Jie Zhou1,2, Tingyan Luo1,2
1Guangxi Key Laboratory of AIDS Prevention and Treatment.
Environmental health and preventive medicine
|November 5, 2023
概括
互联网搜索数据,特别是百度指数,可以有效预测中国肺结核 (PTB) 趋势. 与传统的ARIMA模型相比,使用百度搜索数据的ARIMAX模型显著提高了预测准确性.
科学领域:
- 流行病学 流行病学
- 公共卫生监督 公共卫生监督
- 数据科学数据科学数据科学
背景情况:
- 互联网搜索数据与传染病流行病相关.
- 肺结核 (PTB) 仍然是一个重大的公共卫生问题.
- 使用在线搜索趋势进行预测建模是疾病监测的一个新兴领域.
研究的目的:
- 在中国开发肺结核 (PTB) 趋势的预测模型.
- 探索百度搜索索引和PTB流行趋势之间的关系.
- 评估不同时间序列模型对PTB预测的有效性.
主要方法:
- 在中国收集的PTB病例数据 (2011年1月至2022年8月).
- 利用斯皮尔曼等级相关性和交互分析来识别相关的百度关键字,并构建一个全面的PTB搜索索引.
- 对比自回归集成移动平均 (ARIMA) 和ARIMAX模型用于预测性能.
主要成果:
- PTB发病率显示出波动性的下降趋势.
- 在PTB综合搜索指数和发病率之间发现了强烈的正相关性 (0.834,P < 0.001).
- 在预测准确度方面,ARIMAX模型 (MAPE 5.33%) 显著超过了ARIMA模型 (MAPE 13.19%).
结论:
- 阿里马克斯模型展示了对PTB趋势的卓越适配和预测准确性.
- 百度搜索索引是预测中国PTB病例的一个有价值的工具.
- 整合在线搜索数据增强了对PTB的公共卫生监测能力.
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