暴露通知系统活动作为SARS-COV-2病例预测的主要指标
Eliah Aronoff-Spencer1, Sepideh Mazrouee1, Rishi Graham1
1School of Medicine, Division of Infectious Diseases and Global Public Health, University of California San Diego, La Jolla, CA, United States of America.
PloS one
|August 18, 2023
概括
数字暴露通知 (EN) 系统可以改善COVID-19病例预测. 将EN数据集成到模型中显著提高了预测准确性,为早期预警系统提供了潜力.
科学领域:
- 流行病学 流行病学
- 公共卫生信息学 公共卫生信息学
- 计算生物学 计算生物学
背景情况:
- COVID-19大流行刺激了数字暴露通知 (EN) 系统的发展.
- 这些系统为公共卫生干预提供了新的途径.
- 关于利用实时EN数据进行预测性流行病学建模的研究有限.
研究的目的:
- 评估加利福尼亚州CA Notify (谷歌果暴露通知 - GAEN平台) 的实时数据对短期COVID-19病例预测的实用性.
- 为了确定是否纳入EN活动与传统模型相比,提高了预测准确性.
主要方法:
- 扩展了一个使用历史案例计数来预测未来案件负载的统计模型.
- 从CA Notify集成的匿名EN活动数据进入预测模型.
- 对比模型性能 (带有和没有EN数据) 与实际报告的案件负载,用于1-7天的预测.
主要成果:
- 时间序列分析揭示了EN系统活动与COVID-19病例量之间的时间关联.
- 纳入EN数据显著改善了短期案件负载预测的准确性.
- 贝叶斯推理证实了EN项的非零影响,将平均绝对百分比误差和平均平方预测误差降低了5-32%.
结论:
- 基于智能手机的EN系统显然提高了短期流行病学预测的准确性.
- 这些预测模型有望作为本地预警系统用于资源分配和干预计划的部署.
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