预测欧盟国家中COVID-19病例和死亡的次国家发病率
Alexis Robert1, Lloyd A C Chapman2,3, Rok Grah4
1Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, UK. alexis.robert@lshtm.ac.uk.
BMC infectious diseases
|February 14, 2024
概括
这项研究开发了一种时空模型,以预测地方层面的COVID-19传播风险. 该模型通过确定针对性干预的高风险区域来帮助公共卫生.
科学领域:
- 流行病学 流行病学
- 公共卫生 公共卫生
- 计算建模 计算建模
背景情况:
- 经常出现的COVID-19波浪需要工具来量化传播风险和识别有风险的地理区域.
- 当地爆发风险受到复杂,异质的免疫模式 (感染,接种疫苗,衰退,免疫逃脱) 和人口因素的影响.
- 国家一级预测模型难以捕捉免疫的空间和人口异质性.
研究的目的:
- 开发和验证一个时空回归模型,用于预测地方的COVID-19病例和死亡人数.
- 评估模型的预测性能,并将其与现有的集合模型进行比较.
- 创建一个可访问的工具来可视化预测和探索传输场景.
主要方法:
- 应用了时空回归模型来预测法国,捷克和意大利的次国家COVID-19病例和死亡人数.
- 该模型包含了年龄分层的数据,并将报告的病例与测试数量和疫苗覆盖率等共变量联系起来.
- 用适当的评分规则评估预测性表现,与欧洲COVID-19预测中心相比,并通过模拟进行评估.
主要成果:
- 每周病例预测至4周的全国中位数相对差异为25% (IQR:12-50%),随着预测时间的延长,准确性下降.
- 与病例预测相比,死亡预测显示出更稳定的准确性.
- 该模型提供了比整体模型更窄的预测间隔,强调了基于场景的预测对捕获传输动态的重要性.
结论:
- 开发的模型有效地捕捉到当地COVID-19爆发动态,并在次国家级量化短期传播风险.
- 模型输出有助于识别最有可能发生疫情的地区.
- 一个开源的RShiny App使得这些预测和场景分析可供公共卫生专业人员使用.
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