Prior research has discussed the impact of the COVID-19 pandemic on people's lifestyles and the reduced human development index. However, little is known about how the COVID-19 pandemic and socioeconomic factors influence global mobility and, in turn, impact our mental health. Also, little is known about how computational models would predict global mobility under the influence of COVID-19 and socioeconomic variables. The primary objectives of this paper are to investigate the influence of the COVID-19 pandemic and socioeconomic factors on people's mobility worldwide and to develop a regression model to predict the future impact on mobility due to the COVID-19 lockdown. The two datasets used for this study are the mobility and the COVID-19 dataset. The data taken into consideration was for 14 months, i.e., from April 1, 2020 to May 31, 2021. The mobility dataset contained retail and recreation, grocery and pharmacy, parks, transit stations, workplaces, and residential areas. In contrast, the COVID-19 and socioeconomic dataset contained total confirmed cases, total deaths, total tests, population density, human development index, and other variables. Multiple regression models were built to predict the pandemic's impact on different mobility variables around the world. Variables such as the total number of cases and total deaths per million were negatively correlated with people's mobility at retail and recreation centers, indicating fear and uncertainty. There was a significant negative correlation between reported cases of domestic violence and mobility to the workplace. This indicates the increased stress and anxiety level among individuals due to imposed lockdown during the pandemic. Implications of computational modeling are discussed.
Artificial Intelligence, Machine Learning, and Mental Health in Pandemics A Computational Approach 2022, Pages 189-208