Google search volume predicts the emergence of COVID-19 outbreaks: Google Trends and COVID-19 outbreak - Lippi, Giuseppe; Mattiuzzi, Camilla; Cervellin, Gianfranco (Acta Biomedica Atenei Parmensis, 2020)
Background and aim: Digital epidemiology is increasingly used for supporting traditional epidemiology. This study was hence aimed to explore whether the Google search volume may have been useful to predict the trajectory of coronavirus disease 2019 (COVID-19) outbreak in Italy. Materials and Methods: We accessed Google Trends for collecting data on weekly Google searches for the keywords “tosse” (i.e., cough), “febbre” (i.e., fever) and “dispnea” (dyspnea) in Italy, between February and May 2020. The number of new weekly cases of COVID-19 in Italy was also obtained from the website of the National Institute of Health. Results: The peaks of Google searches for the three terms predicted by 3 weeks that of newly diagnosed COVID-19 cases. The peaks of weekly Google searches for “febbre” (fever), “tosse”( cough) and “dispnea” (dyspnea) were 1.7-, 2.2- and 7.7-fold higher compared to the week before the diagnosis of the first national case. No significant correlation was found between the number of newly diagnosed COVID-19 cases and Google search volumes of “tosse” (cough) and “febbre” (fever), whilst “dyspnea” (dyspnea) was significantly correlated (r= 0.50; p=0.034). The correlation between newly diagnosed COVID-19 cases and “tosse” (cough; r=0.65; p=0.008) or “febbre” (fever; 0.69; p=0.004) become statistically significant with a 3-week delay. All symptoms were also significantly inter-correlated. Conclusions; Continuously monitoring the volume of Google searches and mapping their origin can be a potentially valuable instrument to help predicting and identifying local recrudescence of COVID-19.