# Predictive Modeling of Influenza-Pneumococcal Coinfection

> **NIH NIH R01** · UNIVERSITY OF TENNESSEE HEALTH SCI CTR · 2020 · $380,000

## Abstract

Influenza A virus (IAV) and secondary bacterial infections (SBI) are responsible for a significant number
of illnesses and deaths each year. Management of these diseases is difficult, in part due to a lack of
understanding of complex interplay of host-pathogen interactions and inability to study pneumonia in
clinical settings. To advance the goal of developing effective therapeutics and predicting IAV and SBI risk,
new microbiologic tools that can assess how host immune responses work to limit viral burden and
enhance bacterial invasion in quantitative detail is essential. This project addresses gaps in immunological
knowledge of IAV and SBIs and gaps in developing predictive models and interpreting infection data by
using a tandem mathematical-experimental approach to quantify alveolar macrophage loss (Aim 1)
and SBI related type I interferon exacerbation (Aim 2). These studies will exploit the predictive models to
establish the intricate feedbacks in these responses, identify controlling parameters and dynamics that
govern different clinical outcomes, improve interpretation of immunological and clinical data, and reveal new
targets for treatment and prevention of influenza and related bacterial infections.

## Key facts

- **NIH application ID:** 9966869
- **Project number:** 5R01AI139088-03
- **Recipient organization:** UNIVERSITY OF TENNESSEE HEALTH SCI CTR
- **Principal Investigator:** Amber M Smith
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $380,000
- **Award type:** 5
- **Project period:** 2018-07-01 → 2023-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9966869

## Citation

> US National Institutes of Health, RePORTER application 9966869, Predictive Modeling of Influenza-Pneumococcal Coinfection (5R01AI139088-03). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9966869. Licensed CC0.

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