Google searches accurately forecast RSV hospitalizations

Background: Hospitalization of children with respiratory syncytial virus (RSV) is common and costly. Traditional sources of hospitalization data, useful for public health decision-makers and physicians to make decisions, are themselves costly to acquire and are subject to delays from gathering to publication. Here we use Google searches for as a proxy for hospitalizations.Methods: Searches for RSV and numbers of hospitalizations in WA, MD, FL, and CT were examined from 2004--2018. Running correlation coefficients and phase angles between search and hospitalizations were calculated. Various machine learning models were compared to assess the ability of searches to forecast hospitalizations. Using search data from all 50 US states, we use K-means clustering to identify transmission clusters. We calculate the timing of the optimal timing of prophylaxis initiation as the week beginning the 24-week period covering 95% of all cases.Results: High correlations (u003e0.95) and low phase differences were seen between counts of hospitalizations and search volume in WA, MD, FL, and CT. Searching for began in FL and radiated outward and three distinct transmission clusters were identified: the south and northeast, the northwest and Appalachia, and the center of the country. Calculated initiation dates for prophylaxis closely followed those calculated using traditional data sources (correlation = 0.84).Conclusions: This work validates searches as a proxy for hospitalizations. Search query surveillance of is a rapid and no-cost addition to traditional hospitalization surveillance and may be useful for medical and public health decision-making.