Forecasting infectious disease outbreaks is hard. Forecasting emerging infectious diseases with limited historical data is even harder. In this paper, we investigate ways to improve emerging infectious disease forecasting under operational constraints. Specifically, we explore two options likely to be available near the start of an emerging disease outbreak: synthetic data and genetic information. For this investigation, we conducted an experiment where we trained deep learning models on different combinations of real and synthetic data, both with and without genetic information, to explore how these models compare when forecasting COVID-19 cases for US states. All models are developed with an eye towards forecasting the next pandemic. We find that models trained with synthetic data have better forecast accuracy than models trained on real data alone, and models that use genetic variants have better forecast accuracy compared to those that do not. All models outperformed a baseline persistence model (a feat only accomplished by 7 out of 22 real-time COVID-19 cases forecasting models as reported in [38]) and multiple models outperformed the COVIDHub-4_week_ensemble. This paper demonstrates the value of these underutilized sources of information and provides a blueprint for forecasting future pandemics.

翻译：预测传染病爆发十分困难，而预测历史数据有限的新发传染病则更为艰巨。本文研究在操作约束条件下改进新发传染病预测的方法。具体而言，我们探讨了在新发传染病爆发初期可能获取的两种数据源：合成数据与遗传信息。为此，我们开展实验，在包含/不包含遗传信息的条件下，使用不同组合的真实数据与合成数据训练深度学习模型，比较这些模型对美国各州COVID-19病例的预测效果。所有模型的开发均着眼于未来大流行病的预测需求。研究发现，使用合成数据训练的模型其预测准确性优于仅使用真实数据的模型，而整合遗传变异信息的模型则优于未使用的模型。所有模型均优于基准持续模型（根据文献[38]报告，22个实时COVID-19预测模型中仅有7个达到此水平），且多个模型性能超越COVIDHub-4周集成模型。本文证明了这些未被充分利用的信息源的价值，并为未来大流行病的预测提供了蓝图。