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# Modelled July COVID vaccination averts most hospitalisations in US over-65s
- URL: https://www.coios.me/modelled-july-covid-vaccination-averts-most/
- Published: 2026-09-27T08:54:58.000Z
- Updated: 2026-09-27T09:59:58.000Z
- Description: A US transmission model calibrated to 2024-25 hospitalisation data found that universal annual COVID-19 vaccination in July gave 441 hospitalisations per 100,000 among adults aged 65 and over, against 578 with none, and the gain vanished if coverage fell by 5-10 points.
- Author: Daniel Ryan
- Tags: Weighing data and models, Forecasts and their record, Infection and pandemics, United States, #preprint, #new, #item, #source-2026-09

A stochastic individual-based model of SARS-CoV-2 transmission in a simulated US population of 5 million, stratified by age and risk group and calibrated to hospitalisation incidence from May 2024 to April 2025, compared vaccination timing, eligibility and dosing frequency. Annual universal vaccination in July gave 441 hospitalisations per 100,000 (95% UI 423-463) among adults aged 65 and over, against 578 (553-601) with no additional vaccination; if timing were unchanged, a hybrid universal-annual plus second dose for those 65 and over and immunocompromised adults gave 460 (440-487). The modelled summer advantage disappeared when coverage fell by 5-10 absolute percentage points, and benefit varied by regional seasonality.

*Why it is interesting: Quantifies how much of COVID-19's residual hospitalisation burden tracks vaccination timing rather than eligibility, now that US transmission peaks in summer across much of the South and West.*

Source

[medRxiv, 25 September 2026](https://doi.org/10.64898/2026.09.23.26363810?ref=coios.me)

DOI

10.64898/2026.09.23.26363810

Type

Preprint

Design

Stochastic individual-based SARS-CoV-2 transmission model, 5 million simulated US residents, calibrated to hospitalisation incidence May 2024-April 2025

Verdict

New finding

Driver

[Forecasts and their record](https://www.coios.me/d-forecasting/)

Driver

[Infection and pandemics](https://www.coios.me/d-infectious-disease/)