Disease outbreak prediction by data integration and multi-task learning

The requirements for treatments vary for different diseases. These have to be considered in order to plan ahead the expenditures for the health care system. In this sense, disease surveillance has a significant impact on resource planning. To this end, we study the problem of predicting the number of incidences for a given disease based on the internet search and access log statistics. A number of papers appear in the literature that study this problem of predicting outbreaks, especially for Influenza. In this paper, in addition to investigating disease incidences other than Influenza, we propose to use the statistics for different diseases together for achieving transfer learning. We argue that we can increase prediction performance by considering diseases together in a multi-task learning setting due to our assumption of structure sharing. The results we obtained are promising as we achieved performance improvements in this setting. The code and data-sets used in the study are available from http://mtan.etu.edu.tr/Supplementary/Outbreak-prediction/.