Evaluating Google Trends as a proxy for symptom incidence: insights from the winter COVID-19 infection study in...

Google Trends is used in research and surveillance as a proxy for community infection incidence. Signals are difficult to validate, as most surveillance biases towards severe outcomes and certain demographics.Using Winter COVID-19 Infection Study (WCIS) data in England, symptom prevalence is estimated via generalized additive model with multilevel-regression and poststratification. Symptom duration was estimated using interval censored time delay modelling, converting prevalence to incidence. Google Trends and WCIS incidence and growth rates were compared using cross-correlation.Google Trends and WCIS agreement varied by symptom and age group. The national maximum growth rate cross-correlation for sore throat was 0.81, with 90% prediction intervals of [0.69, 0.90]. Google Trends growth rates generally lagged the WCIS growth rates across symptoms (cough: -5.0 days [-8.0, 0.0], fever: -3.0 days [-6.0, 1.0], loss of smell: -9.0 days [-13, -3.0], shortness of breath: -12 days [-16, -5.0], and sore throat: -4.0 days [-5.0, -2.0]).This work shows Google Trends and community symptom incidence can align, although substantial variation between symptoms and age groups exists, underscoring utility in predicting other surveillance indicators.