Seasonal Web Search Query Selection for Influenza-Like Illness (ILI) Estimation
This incremental improvement addresses performance degradation in web analytics for public health monitoring.
The paper tackled the problem of spurious correlations in influenza-like illness (ILI) estimation from web search data by modeling seasonal variation and selecting queries correlated with residuals, resulting in a method that strongly favors ILI-related queries.
Influenza-like illness (ILI) estimation from web search data is an important web analytics task. The basic idea is to use the frequencies of queries in web search logs that are correlated with past ILI activity as features when estimating current ILI activity. It has been noted that since influenza is seasonal, this approach can lead to spurious correlations with features/queries that also exhibit seasonality, but have no relationship with ILI. Spurious correlations can, in turn, degrade performance. To address this issue, we propose modeling the seasonal variation in ILI activity and selecting queries that are correlated with the residual of the seasonal model and the observed ILI signal. Experimental results show that re-ranking queries obtained by Google Correlate based on their correlation with the residual strongly favours ILI-related queries.