# Forecasting influenza epidemics using a mechanistic epidemic model

> **NIH NIH R01** · KAISER FOUNDATION RESEARCH INSTITUTE · 2020 · $226,676

## Abstract

SUMMARY/ABSTRACT
 This project will fill a fundamental knowledge gap in influenza epidemiology, which is the lack of a
quantified relationship between viral antigenic drift and human susceptibility to influenza. We will then apply
this new knowledge to improve the accuracy and timeliness of influenza forecasts. Antigenic drift refers to
gradual changes in the surface proteins of influenza viruses, which allow new virus strains to escape acquired
immunity and to re-infect individuals who were previously infected with influenza. Antigenic cartography can
quantify the magnitude of antigenic drift (i.e. the antigenic distance) between influenza virus strains. To date,
however, the relationship between antigenic distance and susceptibility to infection has not been quantified for
human influenza.
 We will use a mechanistic model of influenza transmission and immunity to estimate the association
between increasing antigenic distance and increasing susceptibility to infection with influenza. For this, we will
take advantage of a unique data resource: active influenza surveillance conducted since the 2011/12 influenza
season through the US Influenza Vaccine Effectiveness Network. These data include population-based estimates
of the incidence of influenza, stratified by virus subtype/lineage and with antigenic and genetic characterization
of circulating influenza viruses, in three geographically distinct US states. The data also include influenza
vaccine coverage for the target populations. We will apply our mechanistic influenza model to these data and
quantify the drift/susceptibility association.
 We will then apply these findings to improve forecasting of seasonal influenza epidemics. Two different
approaches are currently taken to influenza forecasting. Short-term forecasts use near-real-time surveillance data
to predict the timing and intensity of the peak in influenza cases, with lead times of a few weeks. Long-term
forecasts use data on the relative prevalence of different influenza strains to predict which strains will dominate
the upcoming season. At present neither short- nor long-term forecasting methods make effective use of data on
pre-existing immunity to influenza due to vaccination or prior circulation of influenza strains. Having quantified
the drift/susceptibility association, we will test the forecasting abilities of our influenza model. We hypothesize
that including data on prior circulation of influenza and on vaccine coverage will allow us to forecast the
intensity and subtype/lineage distribution of upcoming influenza epidemics with lead times of 9+ months.
 The proposed research will benefit human health by 1) improving our understanding of the interplay
between human immunity and virus antigenic drift and 2) improving the accuracy and timeliness of influenza
forecasts, allowing more time for the allocation of resources for influenza prevention and treatment.

## Key facts

- **NIH application ID:** 9966852
- **Project number:** 5R01AI132362-04
- **Recipient organization:** KAISER FOUNDATION RESEARCH INSTITUTE
- **Principal Investigator:** RITA MANGIONE-SMITH
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $226,676
- **Award type:** 5
- **Project period:** 2017-08-01 → 2023-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9966852

## Citation

> US National Institutes of Health, RePORTER application 9966852, Forecasting influenza epidemics using a mechanistic epidemic model (5R01AI132362-04). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/9966852. Licensed CC0.

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