估计COVID-19隐藏人口的大小
Esmaeil Mehraeen1, Fatemeh Khajeh Akhtaran2, Mohammad Reza Faridrohani2
1Department of Health Information Technology, Khalkhal University of Medical Sciences, Khalkhal, Iran.
Infectious disorders drug targets
|November 1, 2023
概括
受访者驱动的采样方法显示,卡尔卡尔市的COVID-19病例明显超过官方记录的数量. 这突显了COVID-19感染的隐藏负担,以及在流行病期间需要更广泛的监测.
科学领域:
- 流行病学 流行病学
- 公共卫生 公共卫生
- 传染性疾病 传染性疾病
背景情况:
- 无症状的个体有助于未被检测到的病毒传播.
- 仅仅依靠被诊断的人口低估了流行病的增长.
- 隐藏的COVID-19病例构成了重大的公共卫生挑战.
研究的目的:
- 用医疗中心与会者的数据估计隐藏的COVID-19人口的规模.
- 评估未被诊断的COVID-19病例的流行率.
主要方法:
- 采用受访者驱动抽样 (RDS) 来估计隐藏的COVID-19人口的贝叶斯大小.
- 开始采样23名种子参与者通过PCR证实COVID-19阳性.
- 通过社交网络 (友和亲属关系) 扩大样本,以达到隐藏的病例.
主要成果:
- 经过5次浪潮后,确定了50例未注册的COVID-19病例.
- 据估计,2022年在哈尔卡尔市发生了12198例COVID-19感染.
- 估计的感染人数远远超过官方统计数据.
结论:
- 在被研究的伊朗城镇中,COVID-19病例的"真实"数量明显高于官方数据.
- 受访者驱动的采样是估计流行病中感染规模的宝贵工具.
- 这些发现强调了在公共卫生规划中考虑隐藏感染的重要性.
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