在COVID-19期间结核病发病率模式的时间干扰:中国的时间序列分析
Jiarui Zhang1, Zhong Sun2, Qi Deng1
1School of Public Health, Kunming Medical University, Kunming, Yunnan, China.
PeerJ
|December 17, 2024
概括
由于COVID-19大流行,中国的结核病 (TB) 病例暂时减少,发病率恢复到大流行前的水平. 时间序列模型,特别是Prophet,有效地分析了这些结核病发病率模式.
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:
- 了解COVID-19大流行对结核病 (TB) 发病率的影响对于有效的控制策略至关重要.
- 关于大流行如何改变全球结核病模式的知识存在重大差距.
- 这项研究重点关注中国的肺结核病 (PTB).
研究的目的:
- 调查COVID-19大流行对中国肺结核 (PTB) 发病率模式的影响.
- 评估时间序列模型 (SARIMA,Prophet,LSTM) 在分析PTB趋势中的应用和性能.
- 为未来的结核病预防和控制工作提供见解.
主要方法:
- 利用COVID-19前的PTB数据 (2007-2018) 来训练和验证SARIMA,Prophet和LSTM模型.
- 将模型预测与2020-2023年的实际PTB病例数据进行比较,以评估流行病的影响.
- 使用RMSE,MAE和MAPE指标评估模型性能.
主要成果:
- 疫情前的PTB发病率呈现下降趋势,季节性高峰发生在3月和12月.
- 在COVID-19大流行期间,报告的PTB病例显著下降,低于预测水平.
- 到2023年,PTB发病率有迹象表明,它将恢复到大流行前的水平,尽管略低一些.
- 与SARIMA和LSTM相比,Prophet模型在适应和预测PTB趋势方面表现出卓越的性能.
结论:
- COVID-19大流行对中国的PTB发病率产生了暂时但显著的影响,减少了报告的病例.
- 随着大流行措施的缓解,预计PTB发病率将恢复到COVID-19前的模式.
- 先知模型是分析PTB趋势和为流行病后公共卫生战略提供信息的宝贵工具.
关键词:
在 COVID-19 疫情中,中国中国中国中国.发生率模式 发生率模式传染病趋势 传染病趋势在 LSTM 网络中,先知模型 先知模型萨里马模型 (SARIMA) 是一个模型.时间序列分析时间序列分析.结核病是一种疾病.更多相关视频
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