Social Distancing with Movement Restrictions and the Effective Replication Number of COVID-19: Multi-Country Analysis Based on Phone Mobility Data

Abstract

Background Linking phone mobility data to the effective replication number (Rt) could help evaluation of the impact of social distancing on the coronavirus disease 2019 (COVID-19) spread and estimate the time lag (TL) needed for the effect of movement restrictions to appear. Methods We used a time-series analysis to discover how patterns of five indicators of mobility data relate to changes in Rt of 125 countries distributed over three groups based on Rt-mobility correlation. Group 1 included 71 countries in which Rt correlates negatively with residential and positively with other mobility indicators. Group 2 included 25 countries showing an opposite correlation pattern to Group 1. Group 3 included the 29 remaining countries. We chose the best-fit TL based on forecast and linear regression models. We used linear mixed models to evaluate how mobility indicators and the stringency index (SI) relate with Rt. SI reflects the strictness of governmental responses to COVID-19. Results With a median of 14 days, TLs varied across countries as well as across groups of countries. There was a strong negative correlation between SI and Rt in most countries belonging to Group 1 as opposed to Group 2. SI (units of 10%) associated with decreasing Rt in Group 1 [β -0.15, 95% CI -0.15 - (-0.14)] and Group 3 [-0.05, -0.07 - (-0.03)], whereas, in Group 2, SI associated with increasing Rt (0.13, 0.11 - 0.16). Conclusion Mobile phone mobility data could contribute evaluations of the impact of social distancing with movement restrictions on the spread of the COVID-19.

The effect of different COVID-19 public health restrictions on mobility: A systematic review

“…Of the 14 included studies, four studies included data from multiple countries across Europe, North America, Asia, Australasia and Africa [ 24 , 28 , 30 , 32 ]. Four studies were conducted in the United States of America (USA) [ 19 , 21 , 23 , 25 ] and other studies used data from Australia [ 31 ], Canada [ 26 ], India [ 29 ], Nigeria [ 27 ], South Africa [ 20 ] and Turkey [ 22 ] ( Table 1 ).…”

“…Changes across most or all of the Google Mobility variables where reported separately by 9 of the 14 studies [ 19 – 21 , 25 , 27 , 29 – 32 ], whereas four studies only reported aggregated scores score [ 22 , 23 , 26 , 28 ] or just assessed changes in one variable [ 24 ].…”

“…Four other studies generated an overall Google Mobility score by averaging changes in the across some or all of the variables [ 22 , 23 , 26 , 28 ]. In an analysis of Google Mobility score across 125 countries, stricter public health measures, measured using the Oxford COVID-19 Government Response Tracker, led to greater reductions in mobility [ 28 ], and the effects of these restrictions on mobility appeared to impact infection rates at 14 days ( Table 3 ).…”

“…Summan & Nandi [ 30 ] demonstrated that countries with stricter restrictions experienced a greater decline in visits to parks after both 30 and 45 days, which was not the case for other mobility variables ( Table 3 ). Of note, two studies did report on changes in park use due to uncertainty as to whether visiting a park presented an increased risk of transmission or not [ 21 , 28 ].…”