基于优化移动平均线预测限制的新兴流行病的实时风险排名 - 以COVID-19大流行为例
Zhongliang Wang1,2, Bin Liu1, Jie Luan1
1Naval Medical Center, Naval Medical University, Shanghai, China.
BMC public health
|May 31, 2023
概括
优化移动平均预测限值 (Op-MAPL) 模型提供了一个方便的,实时的COVID-19风险评估工具. 这种算法准确地预测了流行病峰值,并为有效的流行病控制提供了早期警告.
科学领域:
- 流行病学 流行病学
- 数学建模的数学建模
- 公共卫生 公共卫生
背景情况:
- 及时预测COVID-19风险趋势对于疫情控制至关重要.
- 现有的数学模型往往需要手动操作和许多参数,这限制了它们的效率和实际应用.
- 需要一个方便和快速的在线工具来监测新出现的传染病,并进行实时风险评估.
研究的目的:
- 开发一个用户友好和高效的在线模型,用于实时评估新出现的传染病的风险.
- 建立一个及时监测和预测COVID-19流行风险趋势的系统.
- 为预防和控制传染病提供有价值的参考资料.
主要方法:
- 优化移动平均预测限制 (Op-MAPL) 算法是为实时COVID-19数据的在线分析而开发的.
- 该Op-MAPL模型使用印度的Delta变种和美国的Omicron变种的数据进行了验证.
- 该模型用于评估上海和北京的Omicron变种的感染风险水平.
主要成果:
- 该Op-MAPL模型准确地预测了疫情峰值,未来7天的每日风险排名准确率为87.85%.
- 对上海和北京发出了早期预警信号,确定了流行病快速增长的时期.
- 该模型成功地表明了这两个城市的有效流行病控制和逐步缓解的时期.
结论:
- Op-MAPL模型提供了一种可靠的方法,用于评估COVID-19流行病风险,使用各种数据来源的单一指标.
- 这种前性方法使得传染病的有效实时监测和早期预警系统成为可能.
- 该Op-MAPL模型为加强传染病预防和控制策略提供了有价值的参考.
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