传染病预测模型常因数据单一而脆弱，这项研究用66种疾病数据做迁移学习，解决了数据短缺问题。做公共卫生预测或流行病建模的团队，可以直接用公开数据库试试。
该研究通过迁移学习，利用66种传染病的数据流训练预测模型，显著提升了20种疾病数据流的预测性能。研究发现，整合多数据流在84.9%的时间序列和模型结构中改善了预测效果，但数据质量至关重要，添加与目标数据差异过大的数据可能降低性能。研究者还公开了一个多疾病数据库，供传染病预测社区使用。
Transfer Learning using 66 Diseases for Disease Forecasting Applications
Disease forecasting models typically rely on a single data stream, making models brittle when histories are short or noisy. Recent top-performing models have shown that synthesizing multiple reporting systems for the same disease improves performance. Other recent work takes this idea a step further, using transfer learning to train a forecasting model for one disease using data from a different disease. We expand upon each of these approaches greatly, training machine learning models on data that span 66 infectious diseases and several data streams. We investigate the value of incorporating different data streams for forecasting 20 different disease data streams. We find that incorporating other data streams improves forecasting in the vast majority (84.9%) of time series and model structures considered. However, our work highlights that the quality of the added data matters, where adding data extremely different from the target data stream can sometimes degrade forecast performance. A major contribution of this work is in compiling a publicly-available database of data for use by the infectious disease forecasting community.