使用贝叶斯结构时间序列和ARIMA预测Covid-19的趋势和疫苗的因果影响
Muhammed Navas Thorakkattle1, Shazia Farhin1, Athar Ali Khan1
1Department of Statistics and Operation Research, Faculty of Science, Aligarh Muslim University, Aligarh, Uttar Pradesh 202002 India.
Annals of data science
|April 16, 2024
概括
贝叶斯结构时间序列模型准确预测COVID-19趋势和疫苗影响. 虽然一些国家通过疫苗接种减少了死亡率,但印度面临医疗保健系统的压力,需要在全球范围内加快疫苗接种工作.
科学领域:
- 流行病学和公共卫生.
- 生物统计学和时间序列分析
- 传染病建模 传染病建模
背景情况:
- 像ARIMA这样的标准时间序列模型已被用于预测COVID-19模式和评估疫苗接种影响.
- 需要更具适应性和有效的方法来剖析流行病学研究中复杂的时间序列数据.
- 准确的预测和因果推理对于流行病期间的公共卫生政策至关重要.
研究的目的:
- 评估一种更具适应性和有效的状态空间方法来剖析时间序列组件.
- 从2020年3月到2021年6月,预测五个受影响国家 (美国,英国,阿联,巴林,印度) 的COVID-19模式.
- 通过贝叶斯结构时间序列 (BSTS) 模型,研究疫苗接种干预对COVID-19结果的因果关系.
主要方法:
- 利用状态空间模型,特别是贝叶斯结构时间序列 (BSTS),用于时间序列分析和预测.
- 在BSTS模型中使用干预分析来评估疫苗接种活动的因果影响.
- 将BSTS模型的准确性与COVID-19预测的自主回归集成移动平均 (ARIMA) 模型进行了比较.
主要成果:
- 与ARIMA模型相比,BSTS模型在预测COVID-19趋势和疫苗影响方面显示出更高的准确性.
- 预测显示,在60天内,研究国家确诊病例和死亡人数将增加.
- 疫苗接种在美国,英国和阿联有效降低了死亡率,但在印度没有显著降低,因为印度面临潜在的医疗保健系统过载.
结论:
- BSTS方法为分析流行病学时间序列和评估公共卫生干预提供了一个强大的方法.
- 疫苗接种战略需要量身定制的方法,在印度和巴林等国家需要加快努力.
- 持续监测和控制措施至关重要,特别是在英国和阿联,确认病例仍然很高.
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