# Statistical adjustments of sample representation in community-level estimates of COVID-19 transmission and immunity

> **NIH NIH U01** · UNIVERSITY OF MICHIGAN AT ANN ARBOR · 2024 · $582,763

## Abstract

Abstract
Throughout the COVID-19 pandemic, government policy and healthcare implementation responses have been
guided by reported positivity rates and vaccination rates in the community. The selection bias of these test
data questions their validity as measures of the actual viral incidence in the community and as predictors of
clinical burden. Publicly available vaccination data are frequently cited as a proxy for population immunity, but this
metric ignores the effects of naturally-acquired immunity. The health disparities concerning asymptomatic and
symptomatic patients are not yet studied. The proposal develops a valid metric to estimate the true viral incidence
and naturally/vaccine-acquired immunity prevalence in the community, examine the health disparities and social
inequality, and monitor the epidemic over time as an operational surveillance system. The approach collects
routine testing data on SARS-CoV-2 exposure and antibody seropositivity among patients in a hospital system
and performs statistical adjustments of sample representation using multilevel regression and poststratification
(MRP), which adjusts for measured differences between the sample and population and also yields stable small
area estimates. The data collection and analysis procedure can provide information to entire communities with
generalizability and focus on burdens within specific demographics, with close attention to vulnerable populations
on disparities across health outcomes, social determinants, and behaviors. In particular, the research will yield
group-specific estimates of disparities with respect to asymptomatic and symptomatic patients and how these
discrepancies may impact the socio-demographically dependent spread of disease and its subsequent treatment.
The MRP adjustment will be made publicly accessible via a web interface and promote broad investigations with
integrated data sources toward a national study.

## Key facts

- **NIH application ID:** 10771230
- **Project number:** 5U01MD017867-03
- **Recipient organization:** UNIVERSITY OF MICHIGAN AT ANN ARBOR
- **Principal Investigator:** Yajuan Si
- **Activity code:** U01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $582,763
- **Award type:** 5
- **Project period:** 2022-04-01 → 2025-12-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10771230

## Citation

> US National Institutes of Health, RePORTER application 10771230, Statistical adjustments of sample representation in community-level estimates of COVID-19 transmission and immunity (5U01MD017867-03). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10771230. Licensed CC0.

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