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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 () 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 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, 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 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 . Mobility data from mobile phones have a continued potential to support real-time surveillance of COVID-19.

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/content/10.2807/1560-7917.ES.2022.27.10.2100374
2022-03-10
2022-09-27
http://instance.metastore.ingenta.com/content/10.2807/1560-7917.ES.2022.27.10.2100374
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References

  1. Nørgaard SK, Vestergaard LS, Nielsen J, Richter L, Schmid D, Bustos N, et al. Real-time monitoring shows substantial excess all-cause mortality during second wave of COVID-19 in Europe, October to December 2020. Euro Surveill. 2021;26(2):1-5.  https://doi.org/10.2807/1560-7917.ES.2021.26.1.2002023  PMID: 33446304 
  2. Kishore N, Kiang MV, Engø-Monsen K, Vembar N, Schroeder A, Balsari S, et al. Measuring mobility to monitor travel and physical distancing interventions: a common framework for mobile phone data analysis. Lancet Digit Health. 2020;2(11):e622-8.  https://doi.org/10.1016/S2589-7500(20)30193-X  PMID: 32905027 
  3. Oliver N, Lepri B, Sterly H, Lambiotte R, Deletaille S, De Nadai M, et al. Mobile phone data for informing public health actions across the COVID-19 pandemic life cycle. Sci Adv. 2020;6(23):eabc0764.  https://doi.org/10.1126/sciadv.abc0764  PMID: 32548274 
  4. Grantz KH, Meredith HR, Cummings DAT, Metcalf CJE, Grenfell BT, Giles JR, et al. The use of mobile phone data to inform analysis of COVID-19 pandemic epidemiology. Nat Commun. 2020;11(1):4961.  https://doi.org/10.1038/s41467-020-18190-5  PMID: 32999287 
  5. Backer JA, Mollema L, Vos ER, Klinkenberg D, van der Klis FR, de Melker HE, et al. Impact of physical distancing measures against COVID-19 on contacts and mixing patterns: repeated cross-sectional surveys, the Netherlands, 2016-17, April 2020 and June 2020. Euro Surveill. 2021;26(8):1-9.  https://doi.org/10.2807/1560-7917.ES.2021.26.8.2000994  PMID: 33632374 
  6. Pullano G, Valdano E, Scarpa N, Rubrichi S, Colizza V. Evaluating the effect of demographic factors, socioeconomic factors, and risk aversion on mobility during the COVID-19 epidemic in France under lockdown: a population-based study. Lancet Digit Health. 2020;2(12):e638-49.  https://doi.org/10.1016/S2589-7500(20)30243-0  PMID: 33163951 
  7. Charoenwong B, Kwan A, Pursiainen V. Social connections with COVID-19-affected areas increase compliance with mobility restrictions. Sci Adv. 2020;6(47):eabc3054.  https://doi.org/10.1126/sciadv.abc3054  PMID: 33097473 
  8. Kraemer MUG, Yang CH, Gutierrez B, Wu CH, Klein B, Pigott DM, et al. The effect of human mobility and control measures on the COVID-19 epidemic in China. Science. 2020;368(6490):493-7.  https://doi.org/10.1126/science.abb4218  PMID: 32213647 
  9. Jia JS, Lu X, Yuan Y, Xu G, Jia J, Christakis NA. Population flow drives spatio-temporal distribution of COVID-19 in China. Nature. 2020;582(7812):389-94.  https://doi.org/10.1038/s41586-020-2284-y  PMID: 32349120 
  10. Badr HS, Du H, Marshall M, Dong E, Squire MM, Gardner LM. Association between mobility patterns and COVID-19 transmission in the USA: a mathematical modelling study. Lancet Infect Dis. 2020;20(11):1247-54.  https://doi.org/10.1016/S1473-3099(20)30553-3  PMID: 32621869 
  11. Unwin HJT, Mishra S, Bradley VC, Gandy A, Mellan TA, Coupland H, et al. State-level tracking of COVID-19 in the United States. Nat Commun. 2020;11(1):6189.  https://doi.org/10.1038/s41467-020-19652-6  PMID: 33273462 
  12. Hsiang S, Allen D, Annan-Phan S, Bell K, Bolliger I, Chong T, et al. The effect of large-scale anti-contagion policies on the COVID-19 pandemic. Nature. 2020;584(7820):262-7.  https://doi.org/10.1038/s41586-020-2404-8  PMID: 32512578 
  13. Banholzer N, van Weenen E, Lison A, Cenedese A, Seeliger A, Kratzwald B, et al. Estimating the effects of non-pharmaceutical interventions on the number of new infections with COVID-19 during the first epidemic wave. PLoS One. 2021;16(6):e0252827.  https://doi.org/10.1371/journal.pone.0252827  PMID: 34077448 
  14. Chang S, Pierson E, Koh PW, Gerardin J, Redbird B, Grusky D, et al. Mobility network models of COVID-19 explain inequities and inform reopening. Nature. 2021;589(7840):82-7.  https://doi.org/10.1038/s41586-020-2923-3  PMID: 33171481 
  15. Zhou Y, Xu R, Hu D, Yue Y, Li Q, Xia J. Effects of human mobility restrictions on the spread of COVID-19 in Shenzhen, China: a modelling study using mobile phone data. Lancet Digit Health. 2020;2(8):e417-24.  https://doi.org/10.1016/S2589-7500(20)30165-5  PMID: 32835199 
  16. Kissler SM, Kishore N, Prabhu M, Goffman D, Beilin Y, Landau R, et al. Reductions in commuting mobility correlate with geographic differences in SARS-CoV-2 prevalence in New York City. Nat Commun. 2020;11(1):4674.  https://doi.org/10.1038/s41467-020-18271-5  PMID: 32938924 
  17. Xiong C, Hu S, Yang M, Luo W, Zhang L. Mobile device data reveal the dynamics in a positive relationship between human mobility and COVID-19 infections. Proc Natl Acad Sci USA. 2020;117(44):27087-9.  https://doi.org/10.1073/pnas.2010836117  PMID: 33060300 
  18. Pan Y, Darzi A, Kabiri A, Zhao G, Luo W, Xiong C, et al. Quantifying human mobility behaviour changes during the COVID-19 outbreak in the United States. Sci Rep. 2020;10(1):20742.  https://doi.org/10.1038/s41598-020-77751-2  PMID: 33244071 
  19. Iacus SM, Santamaria C, Sermi F, Spyratos S, Tarchi D, Vespe M. Human mobility and COVID-19 initial dynamics. Nonlinear Dyn. 2020;101(3):1901-19.  https://doi.org/10.1007/s11071-020-05854-6  PMID: 32905053 
  20. Praharaj S, Han H. A longitudinal study of the impact of human mobility on the incidence of COVID-19 in India. medRxiv.2020.12.21.20248523;  https://doi.org/10.1101/2020.12.21.20248523  https://doi.org/10.1101/2020.12.21.20248523 
  21. Persson J, Parie JF, Feuerriegel S. Monitoring the COVID-19 epidemic with nationwide telecommunication data. Proc Natl Acad Sci USA. 2021;118(26):e2100664118.  https://doi.org/10.1073/pnas.2100664118  PMID: 34162708 
  22. Yechezkel M, Weiss A, Rejwan I, Shahmoon E, Ben-Gal S, Yamin D. Human mobility and poverty as key drivers of COVID-19 transmission and control. BMC Public Health. 2021;21(1):596.  https://doi.org/10.1186/s12889-021-10561-x  PMID: 33765977 
  23. Bryant P, Elofsson A. Estimating the impact of mobility patterns on COVID-19 infection rates in 11 European countries. PeerJ. 2020;8:e9879.  https://doi.org/10.7717/peerj.9879  PMID: 32983643 
  24. Lemaitre JC, Perez-Saez J, Azman AS, Rinaldo A, Fellay J. Assessing the impact of non-pharmaceutical interventions on SARS-CoV-2 transmission in Switzerland. Swiss Med Wkly. 2020;150:w20295.  https://doi.org/10.4414/smw.2020.20295  PMID: 32472939 
  25. Noland RB. Mobility and the effective reproduction rate of COVID-19. J Transp Health. 2021;20:101016.  https://doi.org/10.1016/j.jth.2021.101016  PMID: 33542894 
  26. Badr HS, Gardner LM. Limitations of using mobile phone data to model COVID-19 transmission in the USA. Lancet Infect Dis. 2021;21(5):e113.  https://doi.org/10.1016/S1473-3099(20)30861-6  PMID: 33152270 
  27. Gatalo O, Tseng K, Hamilton A, Lin G, Klein E, CDC MInD-Healthcare Program. Associations between phone mobility data and COVID-19 cases. Lancet Infect Dis. 2021;21(5):e111.  https://doi.org/10.1016/S1473-3099(20)30725-8  PMID: 32946835 
  28. Nouvellet P, Bhatia S, Cori A, Ainslie KEC, Baguelin M, Bhatt S, et al. Reduction in mobility and COVID-19 transmission. Nat Commun. 2021;12(1):1090.  https://doi.org/10.1038/s41467-021-21358-2  PMID: 33597546 
  29. Casa Nova A, Ferreira P, Almeida D, Dionísio A, Quintino D. Are mobility and COVID-19 related? A dynamic analysis for Portuguese districts. Entropy (Basel). 2021;23(6):786.  https://doi.org/10.3390/e23060786  PMID: 34205561 
  30. World Health Organization (WHO). Public health criteria to adjust public health and social measures in the context of COVID-19. Geneva: WHO; 2020. Available from: https://apps.who.int/iris/handle/10665/332073
  31. intervista. Mobility monitoring COVID-19. Zurich: Statistical Office of the Canton of Zurich, Swiss National COVID-19 Science Task Force, and KOF Swiss Economic Institute; 2021. Available from: https://www.intervista.ch/media/2020/03/Report_Mobilit%C3%A4ts-Monitoring_Covid-19.pdf
  32. Swiss National COVID-19 Science Task Force. Reproductive number. Zurich: ETH Board; 2020. Available from: https://sciencetaskforce.ch/en/current-situation
  33. Huisman JS, Scire J, Angst DC, Li J, Neher RA, Maathuis MH, et al. Estimation and worldwide monitoring of the effective reproductive number of SARS-CoV-2. medRxiv.2020.11.26.20239368;  https://doi.org/10.1101/2020.11.26.20239368  https://doi.org/10.1101/2020.11.26.20239368 
  34. COVID-19 Government Response Tracker. Oxford: Blavatnik School of Government, University of Oxford; 2020. Available from: https://www.bsg.ox.ac.uk/research/research-projects/coronavirus-government-response-tracker
  35. Cori A, Ferguson NM, Fraser C, Cauchemez S. A new framework and software to estimate time-varying reproduction numbers during epidemics. Am J Epidemiol. 2013;178(9):1505-12.  https://doi.org/10.1093/aje/kwt133  PMID: 24043437 
  36. Cantonal Ministers of Public Health and Federal Office of Public Health (FOPH). COVID-19-Bewältigung: Strategische Grundlagen der GDK und des EDI-BAG. [COVID-19 management: Strategic bases of the Swiss Conference of the Cantonal Ministers of Public Health (GDK) and the FDHA-FOPH]. Bern: FOPH; 2020. German. Available from: https://www.bag.admin.ch/dam/bag/de/dokumente/cc/kom/covid-19-strategische-grundlagen-gdk-edi-bag.pdf.download.pdf/COVID-19-Bew%C3%A4ltigung%20%E2%80%93%20Strategische%20Grundlagen%20der%20GDK%20und%20des%20EDI-BAG.pdf
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