分析参考框架用于分析非COVID-19事件
María Del Pilar Villamil1, Nubia Velasco2, David Barrera3
1Department of Systems and Computing Engineering, Universidad de Los Andes, Bogotá, Colombia. mavillam@uniandes.edu.co.
Population health metrics
|October 21, 2023
概括
该ANE框架使用时间序列分析预测非COVID-19疾病. 这种可靠的工具有助于预测结核病等疾病的患者数量,解决医疗保健系统中断的问题.
科学领域:
- 公共卫生 公共卫生
- 流行病学 流行病学
- 医疗信息学 医疗信息学
背景情况:
- 由于COVID-19大流行严重扰乱了医疗保健,延迟了非COVID-19疾病的诊断.
- 2020年之前的现有分析模型是疾病特定的,而2020年后的模型主要集中在COVID-19上.
- 在非COVID-19疾病的疾病预测框架中存在关键差距.
研究的目的:
- 引入非COVID-19事件分析 (ANE) 框架.
- 为预测非COVID-19患者数量提供可靠和用户友好的工具.
- 解决公共卫生中适应性疾病预测模型的需求.
主要方法:
- 该框架采用时间序列分析和SARIMA模型.
- 它使用来自官方政府来源的每日数据进行预测.
- 该框架旨在提供灵活性,并纳入新的数据和来源.
主要成果:
- ANE框架在五个非COVID-19事件中表现出可靠性,包括结核病和自杀企图.
- 性能显示平均绝对百分比误差 (MAPE) 高达20%,在不同的事件动态中保持一致.
- 在预期病例和报告病例之间发现了显著的差距 (例如,结核病17%,自杀未遂19%),因地区而异.
结论:
- ANE框架是一个灵活可靠的工具,用于分析各种疾病数据.
- 该模型的适应性允许更新新数据,提高预测准确度.
- 该框架对于在医疗保健中断的情况下监测和管理非COVID-19疾病趋势至关重要.
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