预测COVID-19病例数和分析菲律宾行为指标的矢量自回归:生态时间趋势研究
Angelica Anne Eligado Latorre1,2, Keiko Nakamura1, Kaoruko Seino1
1Department of Global Health Entrepreneurship, Tokyo Medical and Dental University, Tokyo, Japan.
JMIR formative research
|June 27, 2023
概括
使用诸如公共利益和流动性变化等行为数据预测COVID-19病例证明是有效的. 纳入公共利益的模型2在预测病例方面表现出卓越的准确性,突出了其监控潜力.
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:
- 传统的监控系统面临数据延迟,导致反应性措施.
- 行为数据分析提供了一个积极的方法来补充现有的监视.
- 了解公共行为对于有效的传染病管理至关重要.
研究的目的:
- 使用载体自回归模型预测COVID-19病例.
- 分析行为指标 (公共利益,流动性) 与COVID-19病例之间的关系.
- 评估结合行为数据的模型的预测准确度.
主要方法:
- 用于COVID-19病例预测的病因学,时间趋势,生态学研究设计.
- 载体自回归模型配备了移动性变化,公共利益和病例数据.
- 用于模型评估的格兰杰因果关系测试和预测准确度指标 (MAPE).
主要成果:
- 包括公众利益在内的第2模型在复苏期间,与第1模型 (MAPE=74.2%) 相比,显示出明显更高的准确性 (MAPE=21.4%).
- 格兰杰因果关系测试表明,随着时间的推移,公众利益成为COVID-19病例的重要预测因素.
- 移动性的变化也改善了病例预测,特别是在早期时期.
结论:
- 这项研究是第一个使用菲律宾行为指标预测COVID-19病例的研究.
- 研究结果表明,将公共利益和流动性数据纳入预测模型可以提高预测准确度.
- 行为指标对于改善公共卫生监测和实现及时干预是有价值的.
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