在加拿大四个省份的COVID-19病例估计
David Benatia1, Raphael Godefroy2, Joshua Lewis2
1Center for Research in Economics and Statistics, École nationale de la statistique et de l'administration économique, Institut Polytechnique de Paris, Palaiseau, France.
概括
这项研究估计了加拿大的COVID-19感染率,发现实际人口感染率明显高于报告的病例. 这表明广泛的未被诊断的冠状病毒疾病2019感染.
科学领域:
- 流行病学 流行病学
- 公共卫生 公共卫生
- 传染病建模 传染病建模
背景情况:
- 准确估计人口感染率对于了解疾病传播至关重要.
- 检测2019年冠状病毒病 (COVID-19) 往往是非随机的,可能会影响报告的感染率.
- 之前的估计没有完全考虑选择性测试策略引入的偏差.
研究的目的:
- 估计加拿大四个省份的COVID-19真实人口感染率.
- 为了纠正COVID-19病例数据中的非随机测试偏差.
- 为了量化未被诊断的COVID-19感染的程度.
主要方法:
- 利用了对进行的COVID-19测试和诊断病例的日常数据.
- 开发了一种方法来纠正非随机测试模式.
- 从测试数据中得出的梯度使用,将感染率推算到一般人群中.
主要成果:
- 估计的人口感染率明显超过了北克,安大略省,阿尔伯塔省和不列颠哥伦比亚省报告的阳性病例率.
- 北克省:1.7-2.6%,安大略省:0.7-1.4%,阿尔伯塔省:0.5-1.2%,不列颠哥伦比亚省:0.2-0.4%.
- 截至4月中旬,每例确诊的COVID-19病例含有大约12例未诊断的感染.
结论:
- 非随机测试可能导致对真正的COVID-19流行率的低估.
- 在研究的加拿大省份,大量的COVID-19感染仍未被诊断出来.
- 调查结果强调需要更广泛的测试策略来准确评估大流行影响.
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