Disease outbreak prediction by multi-task learning

The requirements for treatments vary for different diseases. These have to 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 work on 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 are promising in the sense that, we achieved performance improvements in this setting.