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# Respiratory infection hospitalisation risk in New Zealand predicted by gradient boosting
- URL: https://www.coios.me/respiratory-infection-hospitalisation-risk-in-new-zealand/
- Published: 2026-10-31T11:07:44.000Z
- Updated: 2026-10-31T11:57:44.000Z
- Description: Gradient boosting models using demographics, morbidity indices and influenza vaccination status separated people with and without a respiratory infection hospitalisation, across about 5 million New Zealanders each year.
- Author: Daniel Ryan
- Tags: Data, models and systems, What linked data reveals, AI in medicine, Infection and pandemics, New Zealand, #preprint, #new, #item, #source-2026-10, #score-60

The study modelled annual respiratory infection hospitalisation in 4.85 to 5.36 million New Zealanders, 2017 to 2024\. Six algorithms used demographics, hospital and pharmaceutical morbidity indices and vaccination status, with cross-validation grouped by demographic profile. Annual prevalence was 1.01% to 1.60%; LightGBM mean AUROC was 0.8430 and XGBoost 0.8415\. Morbidity indices raised LightGBM average precision over demographics from 0.0720 to 0.1158, and vaccination status to 0.1283\. Trained on one year and tested on the next, LightGBM averaged AUROC 0.8429 and XGBoost 0.8423\. SHAP linked vaccination to lower estimated risk mostly in the oldest group, which describes model behaviour, not vaccine effect.

*Why it is interesting: Routine hospital and pharmacy records added most of the gain over demographics alone. Performance held when models trained on one year were tested on the next.*

Source

[medRxiv, 26 October 2026](https://doi.org/10.64898/2026.10.23.26366351?ref=coios.me)

Paper

Population-scale machine learning models to estimate individual-level risk for acute respiratory infection

Authors

Albrecht S, Smith E, Yeo HY, Park JY, Gul A, Hills I et al.

DOI

10.64898/2026.10.23.26366351

Type

Preprint

Design

Retrospective national linked administrative data, \~4.85-5.36 million people per year, 2017-2024; six ML algorithms, eight variable sets, five-fold grouped cross-validation plus next-year validation

Verdict

New finding

Driver

[What linked data reveals](https://www.coios.me/d-linked-data/)

Driver

[AI in medicine](https://www.coios.me/d-ai-medicine/)

Driver

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