Estimation of Asthma Symptom Onset Using Internet Search Queries: Lag-Time Series Analysis

Background: Asthma affects over 330 million people worldwide. Timing of an asthma event is extremely important and lack of identification of asthma increases the risk of death. A major challenge for health systems is the length of time between symptom onset and care seeking, which could result in delayed treatment initiation and worsening of symptoms. Objective: This study evaluates the utility of the internet search query data for the identification of the onset of asthma symptoms. Methods: Pearson correlation coefficients between the time series of hospital admissions and Google searches were computed at lag times from 4 weeks before hospital admission to 4 weeks after hospital admission. An autoregressive integrated moving average (ARIMAX) model with an autoregressive process at lags of 1 and 2 and Google searches at weeks -1 and -2 as exogenous variables were conducted to validate our correlation results. Results: Google search volume for asthma had the highest correlation at 2 weeks before hospital admission. The ARIMAX model using an autoregressive process showed that the relative searches from Google about asthma were significant at lags 1 (P<.001) and 2 (P=.04). Conclusions: Our findings demonstrate that internet search queries may provide a real-time signal for asthma events and may be useful to measure the timing of symptom onset.

Automated summary

This study analyzed the utility of internet search query data for identifying the onset of asthma symptoms. Results showed that Google search volume for asthma had the highest correlation at 2 weeks before hospital admission and the ARIMAX model confirmed the significance of Google searches at lags of 1 and 2 weeks. Internet search queries can provide a real-time signal for asthma events and may be useful for measuring the timing of symptom onset.