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Explicit knowledge of total community-level immune seroprevalence is criticalto developing policies to mitigate the social and clinical impact ofSARS-CoV-2. Publicly available vaccination data are frequently cited as a proxyfor population immunity, but this metric ignores the effects ofnaturally-acquired immunity, which varies broadly throughout the country andworld. Without broad or random sampling of the population, accurate measurementof persistent immunity post natural infection is generally unavailable. Toenable tracking of both naturally-acquired and vaccine-induced immunity, we setup a synthetic random proxy based on routine hospital testing for estimatingtotal Immunoglobulin G (IgG) prevalence in the sampled community. Our approachanalyzes viral IgG testing data of asymptomatic patients who present forelective procedures within a hospital system. We apply multilevel regressionand poststratification to adjust for demographic and geographic discrepanciesbetween the sample and the community population. We then apply state-basedvaccination data to categorize immune status as driven by natural infection orby vaccine. We have validated the model using verified clinical metrics ofviral and symptomatic disease incidence to show the expected biologicalcorrelation of these entities with the timing, rate, and magnitude ofseroprevalence. In mid-July 2021, the estimated immunity level was 74% with theadministered vaccination rate of 45% in the two counties. The metric improvesreal-time understanding of immunity to COVID-19 as it evolves and thecoordination of policy responses to the disease, toward an inexpensive andeasily operational surveillance system that transcends the limits ofvaccination datasets alone.

Beyond Vaccination Rates: A Synthetic Random Proxy Metric of Total SARS-CoV-2 Immunity Seroprevalence in the Community