特定年龄的SARS-CoV-2死亡率和免疫模式
Megan O'Driscoll1,2, Gabriel Ribeiro Dos Santos3,4, Lin Wang3,4
1Department of Genetics, University of Cambridge, Cambridge, UK. mo487@cam.ac.uk.
Nature
|November 2, 2020
概括
由于数据不一致,估计COVID-19流行病的真实规模是困难的. 本研究使用特定年龄的死亡数据和血清流行率来估计45个国家的感染率和感染死亡率.
科学领域:
- 流行病学
- 传染病模型
- 公共卫生
背景情况:
- 由于数据不一致,估计COVID-19流行病的规模和SARS-CoV-2感染的严重程度是具有挑战性的.
- 观察到的COVID-19死亡人数仅占感染人数的一小部分,报告的差异,尤其是老年人和养老院的情况,使比较复杂化.
- 由于长期护理机构的负担和报告实践的变化,无法直接比较各国的死亡率和传播水平.
研究的目的:
- 使用特定年龄的COVID-19死亡数据和血清流行研究,调查多个国家的感染和死亡模式的一致性.
- 通过利用一致的年龄特定死亡率数据,制定人口感染率和感染死亡率 (IFR) 的可靠估计.
- 评估人口年龄结构和养老院负担对不同国家的IFR异质性的影响.
主要方法:
- 利用来自45个国家的特定年龄的COVID-19相关死亡数据.
- 结合了22项血清流行研究的结果以进行交叉验证.
- 开发了一个建模框架来估计感染率和IFR,分析年龄分布和人口结构.
主要成果:
- 在65岁以下的个体中,死亡的年龄分布在各个环境中是一致的,使得人口感染份额的可靠估计成为可能.
- 在30岁以后,感染死亡率呈日志线性增长,其中5至9岁儿童的死亡率最低.
- 据估计,到2020年9月1日,包括在内的45个国家中,大约有5%的人口被感染,一些拉丁美洲国家的传播率更高.
结论:
- 年龄特定的死亡数据,特别是在年轻年龄组,为估计流行病进展和人口感染率提供了可靠的方法.
- 人口年龄结构和养老院的动态部分解释了各国之间的IFR差异,但其他因素也可能有所贡献.
- 开发的建模框架为各国提供了一种有价值的工具,可以使用随时可用的特定年龄的死亡数据来评估其流行病状况.
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