  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 regression
and poststratification to adjust for demographic and geographic discrepancies
between the sample and the community population. We then apply state-based
vaccination data to categorize immune status as driven by natural infection or
by vaccine. We have validated the model using verified clinical metrics of
viral and symptomatic disease incidence to show the expected biological
correlation of these entities with the timing, rate, and magnitude of
seroprevalence. In mid-July 2021, the estimated immunity level was 74% with the
administered vaccination rate of 45% in the two counties. The metric improves
real-time understanding of immunity to COVID-19 as it evolves and the
coordination of policy responses to the disease, toward an inexpensive and
easily operational surveillance system that transcends the limits of
vaccination datasets alone.
