Mantis: A Foundation Model for Mechanistic Disease Forecasting

TL;DR

Mantis is a simulation-trained foundation model that enables accurate, out-of-the-box disease forecasting across various diseases and settings, even with limited real-world data.

Contribution

Developed Mantis, a novel foundation model trained solely on mechanistic simulations, capable of generalizing to multiple diseases without real-world training data.

Findings

Mantis outperformed all models in CDC COVID-19 forecasts during early pandemic stages.

Achieved top two rankings across diverse diseases and metrics.

Generalized to diseases with unseen transmission mechanisms.

Abstract

Infectious disease forecasting in novel outbreaks or low-resource settings is hampered by the need for large disease and covariate data sets, bespoke training, and expert tuning, all of which can hinder rapid generation of forecasts for new settings. To help 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 sixteen diseases with diverse modes of transmission, assessing both point forecast accuracy (mean absolute error) and probabilistic performance (weighted interval score and coverage). Despite using no real-world data during training, Mantis achieved lower mean absolute error 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.