# Data driven transmission models to optimize influenza vaccination and pandemic mitigation strategies

> **NIH ALLCDC U01** · UNIVERSITY OF MICHIGAN AT ANN ARBOR · 2020 · $372,166

## Abstract

Project Summary/Abstract
Because influenza pandemics occur with little warning, vaccine development and distribution take place at a
slower timescale than transmission of the emergent strain. Similarly, although seasonal influenza epidemics
occur annually, they are also notoriously difficult to predict, and necessitate rapid response to changing
circumstances. While vaccination, antivirals and non-pharmaceutical interventions (NPIs) are available to
mitigate these challenges, imperfect protection and coverage mean that their direct and indirect protective
benefits are conditional on the state of immunity in the population. Therefore, the overall objective of this
application is to develop a sustainable, scalable pipeline of analytic, predictive, and visualization tools to
translate detailed clinical and cohort data to into timely population-level guidance on vaccination, antiviral use,
and NPIs. We will accomplish these goals through the following specific aims: Aim 1) We will use the extensive
clinical and cohort data resources generated by the Michigan Influenza Center to identify and address key
questions in influenza prevention and control; Aim 2) We will integrate these multiple sources of data using
statistical and simulation based models of infectious disease transmission. Specifically, we will Aim 2A) use
robust models of longitudinal serologic data to characterize response to natural infection and vaccination, and
then in Aim 2B) integrate this information into household-based transmission models to understand the impact
of these immune responses on susceptibility to influenza infection. Using the predictions of these individual-
level models parameterized using longitudinal cohort data, in Aim 2C) we will construct synthetic cohorts
representative of the age-specific distribution of immunity in different populations, e.g. the State of Michigan,
and use these data to develop targeted population-level strategies for influenza vaccination. In Aim 2D) we will
then apply the insights of these models to the layered application of antivirals and NPIs in an influenza
pandemic using a network-based simulation platform we have developed. All of these models will be designed,
implemented and analyzed in collaboration with CDC and other partners to ensure clearly-articulated
guidelines for modeling assumptions and inputs (Aim 3). This will be augmented by tools for automated model
verification, validation and synthesis which will ensure adherence to these standards and integrate the findings
of multiple modeling groups (Aims 4 & 5). All of these tools will be released publicly as open-source software
and interactive tools. All of these products will be implemented with the goal of communicating key findings as
well as uncertainty in model inputs, structure, and outcomes as clearly as possible to a wide array of scientific
and policy-focused stakeholders using state-of-the-art tools for data visualization (Aims 6,7 & 8). The outcome
of this project ...

## Key facts

- **NIH application ID:** 10071763
- **Project number:** 1U01IP001138-01
- **Recipient organization:** UNIVERSITY OF MICHIGAN AT ANN ARBOR
- **Principal Investigator:** Jonathan L Zelner
- **Activity code:** U01 (R01, R21, SBIR, etc.)
- **Funding institute:** ALLCDC
- **Fiscal year:** 2020
- **Award amount:** $372,166
- **Award type:** 1
- **Project period:** 2020-09-01 → 2025-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10071763

## Citation

> US National Institutes of Health, RePORTER application 10071763, Data driven transmission models to optimize influenza vaccination and pandemic mitigation strategies (1U01IP001138-01). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10071763. Licensed CC0.

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