In this paper, we estimate the seroprevalence against COVID-19 by country and derive the seroprevalence over the world. To estimate seroprevalence, 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 95% credible interval of the world seroprevalence as [38.6%, 59.2%].

翻译：在本文中,我们按国家对COVID-19的血清阳性反应率进行估计,并得出全世界血清阳性反应率。为了估计血清阳性反应率,我们使用每个国家进行的血清调查(也称为血清调查)进行。当血清调查被纳入对世界血清反应率的估计时,有两个问题。第一,有些国家还没有进行血清调查。第二,抽样收集日期因国家而异。我们试图利用疫苗接种数据、确诊病例数据和国家统计数据来解决这些问题。我们建造了巴伊西亚模型,以分别估计感染或接种后产生抗体的人数。关于感染后患抗体的人数,我们开发了一种等级模型,以综合确诊病例数据和国家统计数据中的信息。与此同时,我们提出了在疫苗接种数据中估计缺失值的回归模型。从2021年7月31日起,我们采用拟议方法,我们获得了95%的世界血清阳性反应的可靠间隔,即[38.6%,59.2%]。