Estimation of World Seroprevalence of SARS-CoV-2 antibodies

TL;DR

This study estimates global COVID-19 antibody prevalence by integrating serological surveys, vaccination, and case data through Bayesian models, providing a comprehensive picture of immunity levels worldwide as of mid-2021.

Contribution

The paper introduces Bayesian hierarchical and regression models to estimate global seroprevalence, addressing data gaps and timing differences across countries.

Findings

Global seroprevalence estimated between 38.6% and 59.2%.

Method effectively combines diverse data sources.

Provides a framework for future seroprevalence studies.

Abstract

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%].