Estimating the effect of mobility on SARS-CoV-2 transmission during the first and second wave of the COVID-19 epidemic, Switzerland, March to December 2020

Abstract

Introduction Human mobility was considerably reduced during the COVID-19 pandemic. To support disease surveillance, it is important to understand the effect of mobility on transmission. Aim We compared the role of mobility during the first and second COVID-19 wave in Switzerland by studying the link between daily travel distances and the effective reproduction number (Rt ) of SARS-CoV-2. Methods We used aggregated mobile phone data from a representative panel survey of the Swiss population to measure human mobility. We estimated the effects of reductions in daily travel distance on Rt via a regression model. We compared mobility effects between the first (2 March–7 April 2020) and second wave (1 October–10 December 2020). Results Daily travel distances decreased by 73% in the first and by 44% in the second wave (relative to February 2020). For a 1% reduction in average daily travel distance, Rt was estimated to decline by 0.73% (95% credible interval (CrI): 0.34–1.03) in the first wave and by 1.04% (95% CrI: 0.66–1.42) in the second wave. The estimated mobility effects were similar in both waves for all modes of transport, travel purposes and sociodemographic subgroups but differed for movement radius. Conclusion Mobility was associated with SARS-CoV-2 Rt during the first two epidemic waves in Switzerland. The relative effect of mobility was similar in both waves, but smaller mobility reductions in the second wave corresponded to smaller overall reductions in Rt . Mobility data from mobile phones have a continued potential to support real-time surveillance of COVID-19.

The methodologies to assess the effectiveness of non-pharmaceutical interventions during COVID-19: a systematic review

“…Second, there can be great merit in analyses advancing our understanding of the mechanisms by which non-pharmaceutical interventions work. For example, interventions may influence behaviour and transmission through factors not captured by previous studies using mobility data from mobile phones, and, moreover, the relationship between population behavior and disease transmission may change over time [ 102 , 103 ]. Additional insights can be gained from analyses using behavioral data from other sources, e. g. surveys evaluating compliance with mask mandates [ 65 ] or the number of daily contacts [ 104 ].…”

The methodologies to assess the effects of non-pharmaceutical interventions during COVID-19: a systematic review

“…The majority of these analyses used mobility data from mobile phones (Table 2, D.2.1); however, more effort is needed to obtain a complete picture of how population behavior mediates the effects of non-pharmaceutical interventions. For example, interventions may influence behaviour and transmission through further factors not captured by previous studies, and, moreover, the relationship between mobility and disease transmission may change over time 96,97 . Additional insights can be gained from analyses using behavioral data from other sources, e. g. surveys evaluating compliance with mask mandates 62 or the number of daily contacts 98 .…”

“…We report our review according to the PRISMA 2020 statement 18 . A completed PRISMA 2020 checklist is provided in SI Appendix G. We conceptualized the review by drawing on experience from our own primary research in the field 4,97 . The search strategy was developed jointly and executed by an experienced information consultant.…”

Is It Important to Increase Physical Activity Among University Students During the Second-Wave COVID-19 Pandemic in Asian Countries? A Cross-Sectional Study of the Knowledge, Attitudes, and Practices in Asian Countries

“…10 , 25 At the beginning of 2021, there was a decrease in cases, but in the middle of the year, June 2021, there was a second wave. 8 , 11 The number of positive cases of COVID-19 increased and decreased, so they were grouped into one wave. 8 The occurrence of the second wave that was felt until the end of the year was caused by the opening of flights with looser quarantine regulations due to various considerations, especially economic, the holiday period, which had just ended, and the relaxation of health protocols due to cases that had been tending to decline.…”

“…[8][9][10] The emergence of the Delta variant in July 2021 and the increasingly active vaccination process caused the rise and fall of COVID-19 cases throughout the year. [10][11][12] At the end of the year, there was also a wider spread of cases due to the operation of workplaces and working together in closed spaces. 13 This second wave led to the enactment of lockdowns and restrictions on activities in several countries, including Indonesia and India.…”