  Search engine logs have a great potential in tracking and predicting
outbreaks of infectious disease. More precisely, one can use the search volume
of some search terms to predict the infection rate of an infectious disease in
nearly real-time. However, conducting accurate and stable prediction of
outbreaks using search engine logs is a challenging task due to the following
two-way instability characteristics of the search logs. First, the search
volume of a search term may change irregularly in the short-term, for example,
due to environmental factors such as the amount of media or news. Second, the
search volume may also change in the long-term due to the demographic change of
the search engine. That is to say, if a model is trained with such search logs
with ignoring such characteristic, the resulting prediction would contain
serious mispredictions when these changes occur.
  In this work, we proposed a novel feature selection method to overcome this
instability problem. In particular, we employ a seasonal-adjustment method that
decomposes each time series into three components: seasonal, trend and
irregular component and build prediction models for each component
individually. We also carefully design a feature selection method to select
proper search terms to predict each component. We conducted comprehensive
experiments on ten different kinds of infectious diseases. The experimental
results show that the proposed method outperforms all comparative methods in
prediction accuracy for seven of ten diseases, in both now-casting and
forecasting setting. Also, the proposed method is more successful in selecting
search terms that are semantically related to target diseases.
