arXiv (All)
open access
Mathematical AI Predicts 40% and 57% Influenza Vaccine Effectiveness Against H1N1 and H3N2, Respectively, for the 2026--2027 U.S. Season
Background: Clinical influenza vaccine effectiveness (VE) has often been low over the past two decades, highlighting the need for preseason VE forecasting. Timely forecasts could help health authorities plan prevention strategies and allocate healthcare resources. However, preseason forecasting of c
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Resurgence of Large-Scale Influenza A (H1N1) Outbreaks: Modeling the Interplay of Transmission, Loss of Immunity, and Vaccination.#10219
- A Decade of the Center for Disease Control and Prevention's FluSight Influenza Forecasting#343649
- Dynamics of the Immune Responses to Repeat Influenza Vaccination Exposures#363666
- Safety and Effectiveness of a Seasonal Live Attenuated Influenza Vaccine in a Human Challenge Model of Infection With Wild-type (wt) Influenza A/California/2009 (H1N1)-Like Virus#148550
- Local Influenza Forecasts Outperform State-Level Forecasts in the United States#343645
Record · ID 1424419
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive