Predicting social distancing index during COVID-19 outbreak through online search engines trends

Lins Filho, P. C.; Freitas, J. L. d. M.; Araujo, M. M. S. d.; Macedo, T. S. d.; Ferreira, A. K. A.; Melo, M. C. F. d.; Silva, E. L. M. S. d.; Caldas, A. d. F.

Online-available information has been considered an accessory tool to estimate epidemiology and collect data on diseases and population behavior patterns. This study aimed to explore the potential use of Google and YouTube relative search volume to predict social distancing index in Brazil during COVID-19 outbreak and verify the correlation between social distancing measures with the course of the epidemic. Data concerning the social distancing index, epidemiological data on COVID-19 in Brazil and the search engines trends for Coronavirus were retrieved from online databases. Multiple linear regression was performed and resulted in a statistically significant model evidencing that Google and YouTube relative search volumes are predictors of the social distancing index. The Spearman correlation test revealed a weak correlation between social distancing measures and the course of the COVID-19 epidemic. Health authorities can apply these data to define the proper timing and location for practicing appropriate risk communication strategies.