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Search engine logs have a great potential in tracking and predictingoutbreaks of infectious disease. More precisely, one can use the search volumeof some search terms to predict the infection rate of an infectious disease innearly real-time. However, conducting accurate and stable prediction ofoutbreaks using search engine logs is a challenging task due to the followingtwo-way instability characteristics of the search logs. First, the searchvolume 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, thesearch volume may also change in the long-term due to the demographic change ofthe search engine. That is to say, if a model is trained with such search logswith ignoring such characteristic, the resulting prediction would containserious mispredictions when these changes occur.  In this work, we proposed a novel feature selection method to overcome thisinstability problem. In particular, we employ a seasonal-adjustment method thatdecomposes each time series into three components: seasonal, trend andirregular component and build prediction models for each componentindividually. We also carefully design a feature selection method to selectproper search terms to predict each component. We conducted comprehensiveexperiments on ten different kinds of infectious diseases. The experimentalresults show that the proposed method outperforms all comparative methods inprediction accuracy for seven of ten diseases, in both now-casting andforecasting setting. Also, the proposed method is more successful in selectingsearch terms that are semantically related to target diseases.

Seasonal-adjustment Based Feature Selection Method for Large-scale Search Engine Logs