Estimating the Direct Impact of Influenza Vaccination and Vaccine-Averted Illnesses Using an Extended Static Compartmental Model

A previously developed static compartment model informed influenza program decisions by estimating the direct impact of influenza vaccination. To improve the estimation of the burden averted by vaccination, we extended this model to consider multiple influenza seasons, allow natural and vaccine-induced immunity to wane over time allow influenza vaccine effectiveness (VE) to differ by hospitalized versus non-hospitalized influenza-associated illness, and measure deaths averted. For model demonstration, we created a synthetic Northern Hemisphere-like and year-round influenza activity dataset spanning four seasons among older adults (>65 years) and assumed VE against symptomatic illness of 37% (17%-53%) and VE against hospitalization of 45% (28%-59%). Under Northern Hemisphere-like activity and an existing vaccination program with an average annual vaccine coverage of 33%, vaccination averted 15.1% (95% UI: 12.2%-16.3%) of influenza-associated illnesses across four seasons; the annual prevented fraction for hospitalized deaths ranged from 14.4% (95% UI: 11.7%-16.8%) to 18.5% (95% UI: 14.9%-21.5%). Under a hypothetical year-round vaccination program with an annual coverage of 20%, vaccination averted 4.9% (95% UI: 3.8%-5.4%) of influenza-associated illnesses across four seasons; the annual prevented fraction for hospitalized deaths ranged from 5.5% (95% UI: 4.3%-6.5%) and 5.8% (95% UI: 4.8%-6.6%). These model extensions allow estimation of vaccine-averted burden across multiple seasons and diverse vaccination program settings, providing information to support influenza vaccination decision-making.

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Publication Details

Journal
medRxiv
Published
2026-10-26
DOI
https://doi.org/10.64898/2026.10.23.26366241
Primary Topic
Influenza Virus Research Studies
Type
preprint
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preprint

Estimating the Direct Impact of Influenza Vaccination and Vaccine-Averted Illnesses Using an Extended Static Compartmental Model

Young M. Yoo, Francisco Nogareda, Anna N. Chard, Melissa A R Rolfes et al.
medRxiv
Influenza Virus Research Studies
preprint

Estimating the Direct Impact of Influenza Vaccination and Vaccine-Averted Illnesses Using an Extended Static Compartmental Model

Young M. Yoo, Francisco Nogareda, Anna N. Chard, Melissa A R Rolfes, Lindsey M. Duca, Paula Couto, Jorge Jara, Stefano Tempia
preprint en

Abstract

A previously developed static compartment model informed influenza program decisions by estimating the direct impact of influenza vaccination. To improve the estimation of the burden averted by vaccination, we extended this model to consider multiple influenza seasons, allow natural and vaccine-induced immunity to wane over time allow influenza vaccine effectiveness (VE) to differ by hospitalized versus non-hospitalized influenza-associated illness, and measure deaths averted. For model demonstration, we created a synthetic Northern Hemisphere-like and year-round influenza activity dataset spanning four seasons among older adults (>65 years) and assumed VE against symptomatic illness of 37% (17%-53%) and VE against hospitalization of 45% (28%-59%). Under Northern Hemisphere-like activity and an existing vaccination program with an average annual vaccine coverage of 33%, vaccination averted 15.1% (95% UI: 12.2%-16.3%) of influenza-associated illnesses across four seasons; the annual prevented fraction for hospitalized deaths ranged from 14.4% (95% UI: 11.7%-16.8%) to 18.5% (95% UI: 14.9%-21.5%). Under a hypothetical year-round vaccination program with an annual coverage of 20%, vaccination averted 4.9% (95% UI: 3.8%-5.4%) of influenza-associated illnesses across four seasons; the annual prevented fraction for hospitalized deaths ranged from 5.5% (95% UI: 4.3%-6.5%) and 5.8% (95% UI: 4.8%-6.6%). These model extensions allow estimation of vaccine-averted burden across multiple seasons and diverse vaccination program settings, providing information to support influenza vaccination decision-making.

medRxiv
Centers for Disease Control and Prevention (US), World Health Organization Regional Office for the Americas (US), The Task Force for Global Health (US), Centers for Disease Control and Prevention (KE)
Influenza Virus Research Studies
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