What question did this study set out to answer?

The aim is to compare various forecasting models for disease burden using GBD 2023 data across high-income countries.

April 14, 2026Open Access

Foundation models, deep learning, and statistical methods for disease burden forecasting: a 10-model comparison using GBD 2023 data

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HFHayden Farquhar

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Implication

Compares deep learning and statistical methods to forecast disease burden in high-income countries, suggesting improved accuracy using ensemble models.

Key Points

The aim is to compare various forecasting models for disease burden using GBD 2023 data across high-income countries.
Evaluated 9 forecasting models from statistical, machine learning, and deep learning paradigms.
Constructed a naive persistence baseline for comparison.
Tested all possible inverse-MAE-weighted ensemble combinations to enhance predictive accuracy.
Models were retrained on historical data (1990-2023) for projections through 2040.
PatchTST had the lowest individual model MAE at 36.70 DALYs/100,000.
A 5-model ensemble achieved the best overall MAE of 33.01.
Chronos model provided the best-calibrated prediction intervals with 62.9% coverage.
Projected increases in anxiety and dementia rates by 2040, while decreases in IHD and stroke were noted.

Cite This Study

Hayden Farquhar (2026) studied this question.

synapsesocial.com/papers/69ddda22e195c95cdefd7b1b https://doi.org/10.5281/zenodo.19528760

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Implication

Compares deep learning and statistical methods to forecast disease burden in high-income countries, suggesting improved accuracy using ensemble models.

Foundation models, deep learning, and statistical methods for disease burden forecasting: a 10-model comparison using GBD 2023 data

Discussion

Member takes

Implication

Key Points

Cite This Study

Foundation models, deep learning, and statistical methods for disease burden forecasting: a 10-model comparison using GBD 2023 data

Discussion

Member takes

Implication

Key Points

Cite This Study

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