 This study investigates the relationship between Google Trends searches of symptoms associated with COVID-19 and confirmed COVID-19 cases and deaths. The authors developed a web application to evaluate Google Trends and traditional COVID-19 metrics via an interactive framework based on principal component analysis, PCA, and time series modeling. They found that the inclusion of digital online searches in statistical models may improve the no-casting and forecasting of the COVID-19 epidemic and could be used as one of the surveillance systems of COVID-19 disease. The web application provides nearly real-time data that anyone can use to make predictions of outbreaks, improve estimates of the dynamics of ongoing epidemics, and predict future or rebound waves. This article was authored by Alessandro Rabiolo, Eugenio Aladio, Esteban Morales, and others.