# Developing novel methods to assess the pandemic potential of influenza A viruses using mathematical modeling and evolutionary methods

> **NIH NIH F31** · EMORY UNIVERSITY · 2024 · $48,974

## Abstract

PROJECT SUMMARY/ABSTRACT
Influenza viruses are a constant public health threat. Their high mutation rates, combined with a large number
of infections each year, generate novel variants; additionally, novel influenza viruses from animal reservoirs like
swine and wild birds can occasionally infect humans. Seasonal influenza viruses cause circa 500,000 deaths
annually, and pandemic influenza viruses have caused between 0.5-50 million deaths. However, despite the
acknowledged risk of pandemic influenza virus emergence, there are limited methods to assess the pandemic
potential of influenza strains. This proposal aims to develop two complementary methods to assess the risk of
novel influenza isolates, using mathematical modeling and evolutionary analyses. I hypothesize that these
approaches will prove useful in the estimation of important epidemiological parameters and in the quantification
of aggregate pandemic risk of IAV strains. The methods will leverage data already commonly gathered for
novel influenza viruses but broaden the applicability and increase the throughput of these established datasets
and approaches. In Aim 1, I will use data from experimental transmission studies in animal models to estimate
population-level epidemiological parameters. To do so, I will extend existing epidemiological approaches based
on serology to estimate the rates of onward transmission based on viral titers. This method allows for the
estimation of key parameters of interest, such as the basic reproduction number and the generation interval,
prior to virus establishment in humans. In Aim 2, I will use comparative evolutionary approaches to quantify
and predict pandemic risk. I will use phylogenetic comparative methods and ancestral and descendant state
analysis to estimate the pandemic risk of novel influenza viruses based on their evolutionary history. This
method would increase the speed with which risk assessments could be generated and would help to identify
key influenza gene segments that are important in predicting risk. Taken together, these methods will improve
our ability to accurately assess the pandemic risk of influenza isolates, which could help to improve public
health guidance, vaccine reformulations, and therapeutic development. Furthermore, such methods could be
easily extended to other viral pathogens of concern.

## Key facts

- **NIH application ID:** 10998045
- **Project number:** 1F31AI186550-01
- **Recipient organization:** EMORY UNIVERSITY
- **Principal Investigator:** Elizabeth Somsen
- **Activity code:** F31 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $48,974
- **Award type:** 1
- **Project period:** 2024-08-01 → 2027-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10998045

## Citation

> US National Institutes of Health, RePORTER application 10998045, Developing novel methods to assess the pandemic potential of influenza A viruses using mathematical modeling and evolutionary methods (1F31AI186550-01). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10998045. Licensed CC0.

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