A novel coronavirus was first reported in Wuhan, China in December 2109 and was declared a global pandemic by the World Health Organization on 11 March 2020. Identification of the critical factors that predict reduced mobility and human interaction is critical to developing successful transmission mitigation efforts globally. Governments and localities around the world have responded with wide-ranging policies related to containment and closure such as travel restrictions and stay-at-home-orders, as well as economic benefits, expanded testing, and public health education, among others. Anonymized GPSenabled smartphone data is a novel tool to track human mobility and is becoming widely available. This study explores the relationships between containment and closure policies, disease trends, and human mobility patterns in 40 countries in Western, Eastern, Northern, and Southern Europe and North America. The principal component analysis was applied to reduce variable dimensions, followed by multivariate multiple regression. The model parameter estimations indicate that total cases, canceling activities (school, work, events), mask policies and the pandemic declaration all were significant predictors (p<.001) of change in workplace mobility from baseline.