Transfer Learning using 66 Diseases for Disease Forecasting Applications
This work advances disease forecasting by demonstrating the broad benefits and limitations of transfer learning across many diseases, providing a resource for the infectious disease forecasting community.
The authors trained models on data from 66 infectious diseases and multiple data streams, finding that incorporating other data streams improves forecasting in 84.9% of cases, though adding dissimilar data can degrade performance. They also compiled a public database for the forecasting community.
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.