question: Objectives Due to the infectiousness of COVID-19, the mobility of individuals has sharply decreased, both in response to government policy and self-protection This analysis seeks to understand how mobility reductions reduce the spread of the coronavirus (SAR-CoV-2), using readily available data sources Methods Mobility data from Google is correlated with estimates of the effective reproduction rate, Rt, which is a measure of viral infectiousness (Google, 2020) The Google mobility data provides estimates of reductions in mobility, for six types of trips and activities Rt for US states are downloaded from an on-line platform that derives daily estimates based on data from the Covid Tracking Project (Wissel et al , 2020;Systrom et al , 2020) Fixed effects models are estimated relating mean Rt and 80% upper level credible interval estimates to changes in mobility and a time-trend value and with both 7-day and 14-day lags Results All mobility variables are correlated with median Rt and the upper level credible interval of Rt Staying at home is effective at reducing Rt, Time spent at parks has a small positive effect, while other activities all have larger positive effects The time trend is negative suggesting increases in self-protective behavior Predictions suggest that returning to baseline levels of activity for retail, transit, and workplaces, will increase Rt above 1 0, but not for other activities Mobility reductions of about 20–40% are needed to achieve an Rt below 1 0 (for the upper level 80% credible interval) and even larger reductions to achieve an Rt below 0 7 Conclusions Policy makers need to be cautious with encouraging return to normal mobility behavior, especially returns to workplaces, transit, and retail locations Activity at parks appears to not increase Rt as much This research also demonstrates the value of using on-line data sources to conduct rapid policy-relevant analysis of emerging issues