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 48 forecasting models across six 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.

翻译：在新发传染病暴发或资源匮乏环境下进行传染病预测时，常因需要大规模疾病与协变量数据集、定制化训练及专家调参而受到限制，这些因素均会阻碍为新场景快速生成预测。为应对这些挑战，我们开发了Mantis——一种完全基于机理模拟训练的基础模型，该模型能够实现跨疾病、跨区域、跨预测指标的即用型预测，即使在历史数据有限的场景下亦能适用。我们在六种具有不同传播模式的疾病上，将Mantis与48种预测模型进行了对比评估，同时考察了点预测精度（平均绝对误差）与概率预测性能（加权区间评分与覆盖度）。尽管训练过程中未使用任何真实世界数据，在对先前未见过的早期大流行预测进行回溯测试时，Mantis的平均绝对误差低于美国疾病控制与预防中心（CDC）COVID-19预测中心的所有模型。在所有其他测试疾病中，Mantis在各项评估指标上均稳定位列前两名。Mantis进一步推广至训练数据中未包含传播机制的疾病，证明其能够捕捉基本的传染动力学规律，而非记忆疾病特异性模式。这些能力表明，如Mantis这类纯基于模拟的基础模型可为疾病预测提供实用基础：其具备通用性、准确性，并可在传统模型难以应对的场景中部署使用。