Lee Kwangmin, Kim Seongmin, Jo Seongil, Lee Jaeyong
Department of Big Data Convergence, Chonnam National University, Gwangju, South Korea.
Department of Statistics, Seoul National University, Seoul, South Korea.
J Appl Stat. 2024 Apr 2;51(15):3039-3058. doi: 10.1080/02664763.2024.2335569. eCollection 2024.
In this paper, we estimate the seroprevalence against COVID-19 by country and derive the seroprevalence over the world. To estimate seroprevalence among adults, we use serological surveys (also called the serosurveys) conducted within each country. When the serosurveys are incorporated to estimate world seroprevalence, there are two issues. First, there are countries in which a serological survey has not been conducted. Second, the sample collection dates differ from country to country. We attempt to tackle these problems using the vaccination data, confirmed cases data, and national statistics. We construct Bayesian models to estimate the numbers of people who have antibodies produced by infection or vaccination separately. For the number of people with antibodies due to infection, we develop a hierarchical model for combining the information included in both confirmed cases data and national statistics. At the same time, we propose regression models to estimate missing values in the vaccination data. As of 31st of July 2021, using the proposed methods, we obtain the credible interval of the world seroprevalence as .
在本文中，我们按国家估算了针对新冠病毒的血清阳性率，并得出了全球的血清阳性率。为了估算成年人中的血清阳性率，我们使用了每个国家内进行的血清学调查（也称为血清调查）。当将这些血清学调查纳入以估算全球血清阳性率时，存在两个问题。第一，有些国家尚未进行血清学调查。第二，各国的样本采集日期不同。我们尝试利用疫苗接种数据、确诊病例数据和国家统计数据来解决这些问题。我们构建贝叶斯模型，分别估算因感染或接种疫苗而产生抗体的人数。对于因感染而产生抗体的人数，我们开发了一个层次模型，用于合并确诊病例数据和国家统计数据中包含的信息。同时，我们提出回归模型来估算疫苗接种数据中的缺失值。截至2021年7月31日，使用所提出的方法，我们得出全球血清阳性率的可信区间为 。
