# Modeling toolkit to evaluate multifaceted control strategies for seasonal and pandemic influenza

> **NIH ALLCDC U01** · UNIVERSITY OF TEXAS AT AUSTIN · 2020 · $374,998

## Abstract

PROJECT SUMMARY
We will develop a data-driven model of seasonal and pandemic influenza transmission throughout the US to
accelerate robust assessments of multifaceted influenza intervention strategies. We will work closely with the
CDC Modeling Network to advance the fidelity, transparency and translation of models as an evidence base for
influenza policy making, prevention and control. This project extends a metapopulation model of influenza
transmission within and between 217 major metropolitan areas in the US that we are developing in
collaboration with the CDC Modeling Network. The model includes travel between cities, age- and risk-group
specific susceptibility, probability of clinical outcomes, intervention efficacies and uptake rates, as well as the
impacts of local climate and school calendars on transmission rates. Using a range of public health,
epidemiological, societal and economic metrics, the model can flexibly evaluate thousands of candidate
intervention strategies, including time- and location-based combinations of vaccines, antivirals, and social
distancing measures with potential subgroup-specific prioritization.
Our proposal includes four major aims. In Aim 1, we will extend our US Influenza Model to include the co-
circulation of multiple viruses competing via transient heterosubtypic immunity. We will derive new estimates
for the duration and magnitude of heterosubtypic immunity and design strain-specific strategies for effectively
controlling co-circulating seasonal and pandemic influenza viruses. In Aim 2, we will evaluate intervention
strategies that leverage newly approved and combined antiviral drugs. We will fit within-host viral dynamic
models to clinical data on new antivirals to estimate the efficacy of various drug regimens in different
subpopulations with respect to disease severity, infectiousness, and the risk of antiviral resistance. In Aim 3,
we will build a granular within-city model of influenza transmission based on abundant data and local
collaborations with public health and healthcare leaders in the Austin-Round Rock Metropolitan Area. We will
apply the model to elucidate socioeconomic and geographic disparities in influenza risk and design
interventions that ameliorate such gaps. In Aim 4, we will build an interactive visualization platform that allows
users to specify epidemic scenarios, implement layered interventions as simulations unfold, and view the
model dynamics through the lens of a surveillance module based on the CDC’s FluView Interactive portal.
We will work extensively with the CDC Modeling Network to build a diverse portfolio of validated models and
best practices for collaborative decision support. Our projects will contribute flexible models for the evaluation
of multifaceted influenza interventions, elucidate competition among influenza viruses and the efficacies of
novel antivirals, and provide insights into socioeconomic disparities in influenza burden. Furthermore, our
innovative visu...

## Key facts

- **NIH application ID:** 10073008
- **Project number:** 1U01IP001136-01
- **Recipient organization:** UNIVERSITY OF TEXAS AT AUSTIN
- **Principal Investigator:** LAUREN ANCEL MEYERS
- **Activity code:** U01 (R01, R21, SBIR, etc.)
- **Funding institute:** ALLCDC
- **Fiscal year:** 2020
- **Award amount:** $374,998
- **Award type:** 1
- **Project period:** 2020-09-01 → 2025-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10073008

## Citation

> US National Institutes of Health, RePORTER application 10073008, Modeling toolkit to evaluate multifaceted control strategies for seasonal and pandemic influenza (1U01IP001136-01). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10073008. Licensed CC0.

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