# RFA-IP-22-004, Component A _ Credible Effectiveness Measures of Seasonal Influenza, COVID-19 and Other Respiratory Virus Vaccines against Ambulatory Care for Acute Illness in Texas (and Component D).

> **NIH ALLCDC U01** · BAYLOR RESEARCH INSTITUTE · 2024 · $1,400,000

## Abstract

COMPONENT A - PROJECT SUMMARY/ABSTRACT:
Influenza (Flu) viruses are constantly evolving, requiring vaccines to be reformulated every season. New SARS-
CoV-2 (SC2) variants have caused recurrent Coronavirus Infectious Disease – 2019 (COVID) surges in different
regions of the United States through the winter of 2021-22. Estimating ongoing real-world Flu and COVID
vaccine effectiveness (VE) against ambulatory care for acute illness (ACAI) are essential in evaluating the
protection provided by nationwide vaccination programs and for monitoring the duration of protection
afforded by respective vaccines each of which are high priorities for fulfilling the CDC’s mission of serving as
the nation’s health protection agency. Our long-term research goal is to advance the understanding of the
epidemiology and prevention of respiratory virus (RV) infections (i.e., seasonal and pandemic influenza, SC2
and Other Respiratory Viruses (ORVs) such as Respiratory Syncytial Virus [RSV]) while reducing the burden of
disease and improving the health of the population. We plan to systematically evaluate the VE against ACAI
associated with lab-confirmed influenza, COVID and vaccine-preventable ORVs with respective CDC
recommended vaccinations in the Baylor Scott & White Health, Central Texas (BSWCTX) enrollment eligible
population. The objective is to obtain reliable vaccination information and to provide accurate interim and
annual estimates of VE to prevent ACAI in respective RV vaccine age-eligible population. Our central
hypothesis is that timely and accurate measurement of VE and burden of illness due to vaccine preventable
RVs is sustainable. The rationale is that by assessing the interim and annual VE against vaccine preventable
RVs, the CDC ACIP can modify recommendations for receiving the vaccines and booster doses as well as use of
appropriate antiviral agents. The specific aims are to: 1) Measure effectiveness of seasonal and pandemic Flu,
COVID and vaccine-preventable ORV vaccines against ACAI for respective lab-confirmed mild to moderate
infection in at least 1,000 children and adults from the 2022-23 to 2026-27 seasons. 2) Monitor ongoing Flu
and SC2 viral evolution by genomic sequencing among at least 1,000 enrolled children and adults from the
2022-23 to 2026-27 seasons. 3) Perform potentially year-round SC2 surveillance during periods when Flu
viruses are not circulating to measure current COVID VE against ACAI for lab-confirmed mild to moderate SC2
infection in children and adults from the 2022-23 to 2026-27 seasons. To accomplish these aims, we will
estimate real-time VE in the ambulatory setting using a test-negative design, estimate burden of illness of
vaccine preventable RVs in the BSWCTX burden subset, and examine factors affecting VE. The proposed
research is innovative as we have adapted methods to include verified vaccinations and accurate lab diagnosis
of RV infections with one or both influenza and SC2 in participants who are systematically s...

## Key facts

- **NIH application ID:** 10910859
- **Project number:** 5U01IP001189-03
- **Recipient organization:** BAYLOR RESEARCH INSTITUTE
- **Principal Investigator:** Manjusha Gaglani
- **Activity code:** U01 (R01, R21, SBIR, etc.)
- **Funding institute:** ALLCDC
- **Fiscal year:** 2024
- **Award amount:** $1,400,000
- **Award type:** 5
- **Project period:** 2022-09-30 → 2025-09-29

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10910859

## Citation

> US National Institutes of Health, RePORTER application 10910859, RFA-IP-22-004, Component A _ Credible Effectiveness Measures of Seasonal Influenza, COVID-19 and Other Respiratory Virus Vaccines against Ambulatory Care for Acute Illness in Texas (and Component D). (5U01IP001189-03). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10910859. Licensed CC0.

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