Beyond Vaccination Rates: A Synthetic Random Proxy Metric of Total SARS-CoV-2 Immunity Seroprevalence in the Community
Yajuan Si, Leonard Covello, Siquan Wang, Theodore Covello, Andrew, Gelman

TL;DR
This paper introduces a novel synthetic proxy metric for estimating total SARS-CoV-2 community immunity by combining hospital IgG testing data with vaccination records, capturing both natural and vaccine-induced immunity.
Contribution
The study develops and validates a new method to estimate community-wide SARS-CoV-2 immunity, accounting for natural infection and vaccination, using routinely collected hospital testing data.
Findings
Estimated immunity level was 74% in July 2021.
The model correlates well with disease incidence metrics.
Provides a cost-effective, real-time immunity surveillance tool.
Abstract
Explicit knowledge of total community-level immune seroprevalence is critical to developing policies to mitigate the social and clinical impact of SARS-CoV-2. Publicly available vaccination data are frequently cited as a proxy for population immunity, but this metric ignores the effects of naturally-acquired immunity, which varies broadly throughout the country and world. Without broad or random sampling of the population, accurate measurement of persistent immunity post natural infection is generally unavailable. To enable tracking of both naturally-acquired and vaccine-induced immunity, we set up a synthetic random proxy based on routine hospital testing for estimating total Immunoglobulin G (IgG) prevalence in the sampled community. Our approach analyzes viral IgG testing data of asymptomatic patients who present for elective procedures within a hospital system. We apply multilevel…
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Taxonomy
TopicsCOVID-19 epidemiological studies · SARS-CoV-2 and COVID-19 Research · Vaccine Coverage and Hesitancy
