COVID-19 concern in cyberspace predicts human reduced dispersal in the real world: Meta-regression analysis of time series relationships across U.S. states and 115 countries/territories()

Ma, Mac Zewei

Built upon the parasite-stress theory of sociality and the behavioral immune system theory, this research examined how concern about COVID-19 in cyberspace (i.e. online search volume for corona, coronavirus, covid-19, etc.) would predict human reduced dispersal in the real world (i.e. Google's COVID-19 Community Mobility Reports) between January 05, 2020 and May 22, 2021, accounting for COVID-19 cases per million, case fatality rate, death-thought accessibility, government stringency index, yearly trends, season, religious holiday, and serial autocorrelation. Meta-regressions analyzing the results of multiple regressions on weekly-level data showed that when people had higher levels of COVID-19 concern in cyberspace in a given week, the amount of time people spent at home increased from the previous week across U.S. states (Study 1) and 115 countries/territories (Study 2). Across studies, the association between COVID-19 concern and stay-at-home behavior was stronger in areas of higher levels of infectious-disease contagion risks. Compared with actual coronavirus threat, COVID-19 concern in cyberspace had a significantly larger effect on predicting human reduced dispersal in the real world, suggesting that online query data have an invaluable implication for predicting large-scale behavioral changes in response to life-threatening events in the real world and are indispensable for COVID-19 surveillance systems.