Authors
Carson Dudley, Reiden Magdaleno, Christopher Harding, Ananya Sharma, Emily Martin, Marisa Eisenberg
Published in
Proceedings of the National Academy of Sciences of the United States of America. Volume 123. Issue 40. Pages e2602542123. Oct 06, 2026. Epub Sep 28, 2026.
Abstract
Infectious disease forecasting in novel outbreaks or low-resource settings is hampered by the need for large disease and covariate datasets, bespoke training, and expert tuning, all of which can hinder rapid generation of forecasts for new settings. To address these challenges, we developed Mantis, a foundation model trained entirely on mechanistic simulations, which enables out-of-the-box forecasting across diseases, regions, and outcomes, even in settings with limited historical data. We evaluated Mantis against 78 forecasting models across 16 diseases with diverse transmission modes, assessing both point forecast accuracy [mean absolute error (MAE)] and probabilistic performance (weighted interval score and coverage). Despite using no real-world data during training, Mantis achieved lower MAE than all models in the CDC's COVID-19 Forecast Hub when backtested on early pandemic forecasts which it had not previously seen. Across all other diseases tested, Mantis consistently ranked in the top two models across evaluation metrics. Mantis further generalized to diseases with transmission mechanisms not represented in its training data, demonstrating that it can capture fundamental contagion dynamics rather than memorizing disease-specific patterns. These capabilities illustrate that purely simulation-based foundation models such as Mantis can provide a practical foundation for disease forecasting: general-purpose, accurate, and deployable where traditional models struggle.
PMID:
42804637
Bibliographic data and abstract were imported from PubMed on 29 Sep 2026.
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