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Abstract

Background

Given the societal, economic and health costs of COVID-19 non-pharmaceutical interventions (NPI), it is important to assess their effects. Human mobility serves as a surrogate measure for human contacts and compliance with NPI. In Nordic countries, NPI have mostly been advised and sometimes made mandatory. It is unclear if making NPI mandatory further reduced mobility.

Aim

We investigated the effect of non-compulsory and follow-up mandatory measures in major cities and rural regions on human mobility in Norway. We identified NPI categories that most affected mobility.

Methods

We used mobile phone mobility data from the largest Norwegian operator. We analysed non-compulsory and mandatory measures with before–after and synthetic difference-in-differences approaches. By regression, we investigated the impact of different NPI on mobility.

Results

Nationally and in less populated regions, time travelled, but not distance, decreased after follow-up mandatory measures. In urban areas, however, distance decreased after follow-up mandates, and the reduction exceeded the decrease after initial non-compulsory measures. Stricter metre rules, gyms reopening, and restaurants and shops reopening were significantly associated with changes in mobility.

Conclusion

Overall, distance travelled from home decreased after non-compulsory measures, and in urban areas, distance further decreased after follow-up mandates. Time travelled reduced more after mandates than after non-compulsory measures for all regions and interventions. Stricter distancing and reopening of gyms, restaurants and shops were associated with changes in mobility.

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/content/10.2807/1560-7917.ES.2023.28.17.2200382
2023-04-27
2024-04-26
http://instance.metastore.ingenta.com/content/10.2807/1560-7917.ES.2023.28.17.2200382
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References

  1. Brauner JM, Mindermann S, Sharma M, Johnston D, Salvatier J, Gavenčiak T, et al. Inferring the effectiveness of government interventions against COVID-19. Science. 2021;371(6531):eabd9338.  https://doi.org/10.1126/science.abd9338  PMID: 33323424 
  2. Mendolia S, Stavrunova O, Yerokhin O. Determinants of the community mobility during the COVID-19 epidemic: The role of government regulations and information. J Econ Behav Organ. 2021;184:199-231.  https://doi.org/10.1016/j.jebo.2021.01.023  PMID: 33551525 
  3. Hirt J, Janiaud P, Hemkens LG. Randomized trials on non-pharmaceutical interventions for COVID-19: a scoping review. BMJ Evid Based Med. 2022;27(6):334-44.  https://doi.org/10.1136/bmjebm-2021-111825  PMID: 35086864 
  4. Bönisch S, Wegscheider K, Krause L, Sehner S, Wiegel S, Zapf A, et al. Effects of coronavirus disease (COVID-19) related contact restrictions in Germany, March to May 2020, on the mobility and relation to infection patterns. Front Public Health. 2020;8:568287.  https://doi.org/10.3389/fpubh.2020.568287  PMID: 33134239 
  5. Velias A, Georganas S, Vandoros S. COVID-19: Early evening curfews and mobility. Soc Sci Med. 2022;292(114538):114538.  https://doi.org/10.1016/j.socscimed.2021.114538  PMID: 34823131 
  6. Snoeijer BT, Burger M, Sun S, Dobson RJB, Folarin AA. Measuring the effect of non-pharmaceutical interventions (NPIs) on mobility during the COVID-19 pandemic using global mobility data. NPJ Digit Med. 2021;4(1):81.  https://doi.org/10.1038/s41746-021-00451-2  PMID: 33986465 
  7. Weill JA, Stigler M, Deschenes O, Springborn MR. Social distancing responses to COVID-19 emergency declarations strongly differentiated by income. Proc Natl Acad Sci USA. 2020;117(33):19658-60.  https://doi.org/10.1073/pnas.2009412117  PMID: 32727905 
  8. Jeffrey B, Walters CE, Ainslie KEC, Eales O, Ciavarella C, Bhatia S, et al. Anonymised and aggregated crowd level mobility data from mobile phones suggests that initial compliance with COVID-19 social distancing interventions was high and geographically consistent across the UK. Wellcome Open Res. 2020;5(170):170.  https://doi.org/10.12688/wellcomeopenres.15997.1  PMID: 32954015 
  9. Saunes IS, Vrangbæk K, Byrkjeflot H, Jervelund SS, Birk HO, Tynkkynen LK, et al. Nordic responses to Covid-19: Governance and policy measures in the early phases of the pandemic. Health Policy. 2022;126(5):418-26.  https://doi.org/10.1016/j.healthpol.2021.08.011  PMID: 34629202 
  10. Inglesby TV, Nuzzo JB, O’Toole T, Henderson DA. Disease mitigation measures in the control of pandemic influenza. Biosecur Bioterror. 2006;4(4):366-75.  https://doi.org/10.1089/bsp.2006.4.366  PMID: 17238820 
  11. Aledort JE, Lurie N, Wasserman J, Bozzette SA. Non-pharmaceutical public health interventions for pandemic influenza: an evaluation of the evidence base. BMC Public Health. 2007;7(1):208.  https://doi.org/10.1186/1471-2458-7-208  PMID: 17697389 
  12. World Health Organization Writing GroupBell D, Nicoll A, Fukuda K, Horby P, Monto A, et al. Non-pharmaceutical interventions for pandemic influenza, national and community measures. Emerg Infect Dis. 2006;12(1):88-94.  https://doi.org/10.3201/eid1201.051371  PMID: 16494723 
  13. Yabe T, Tsubouchi K, Fujiwara N, Wada T, Sekimoto Y, Ukkusuri SV. Non-compulsory measures sufficiently reduced human mobility in Tokyo during the COVID-19 epidemic. Sci Rep. 2020;10(1):18053.  https://doi.org/10.1038/s41598-020-75033-5  PMID: 33093497 
  14. Ekomstatistikken. Mobiltelefoni - største tilbyder (abonnement). [Mobile telephony - largest provider (subscription)]. Lillesand: Nasjonal kommunikasjonsmyndighet. [Accessed: 4 Jan 2023]. Norwegian. Available from: https://ekomstatistikken.nkom.no/#/statistics/details?servicearea=Mobiltjenester&label=Mobiltelefoni%20-%20st%C3%B8rste%20tilbyder%20(abonnement)
  15. Tully MA, McMaw L, Adlakha D, Blair N, McAneney J, McAneney H, et al. The effect of different COVID-19 public health restrictions on mobility: a systematic review. PLoS One. 2021;16(12):e0260919.  https://doi.org/10.1371/journal.pone.0260919  PMID: 34879083 
  16. Hernando A, Mateo D, Bayer J, Barrios I. Radius of gyration as predictor of COVID-19 deaths trend with three-weeks offset. medRxiv. 2021.01.30.21250708. [Preprint].  https://doi.org/10.1101/2021.01.30.21250708  https://doi.org/10.1101/2021.01.30.21250708 
  17. Haddawy P, Lawpoolsri S, Sa-Ngamuang C, Su Yin M, Barkowsky T, Wiratsudakul A, et al. Effects of COVID-19 government travel restrictions on mobility in a rural border area of Northern Thailand: A mobile phone tracking study. PLoS One. 2021;16(2):e0245842.  https://doi.org/10.1371/journal.pone.0245842  PMID: 33534857 
  18. Heiler G, Reisch T, Hurt J, Forghani M, Omani A, Hanbury A, et al. Country-wide mobility changes observed using mobile phone data during COVID-19 pandemic. 2020 IEEE International Conference on Big Data (Big Data). Atlanta, United States 2020; pp. 3123-32.  https://doi.org/10.1109/BigData50022.2020.9378374  https://doi.org/10.1109/BigData50022.2020.9378374 
  19. Gauvin L, Bajardi P, Pepe E, Lake B, Privitera F, Tizzoni M. Socio-economic determinants of mobility responses during the first wave of COVID-19 in Italy: from provinces to neighbourhoods. J R Soc Interface. 2021;18(181):20210092.  https://doi.org/10.1098/rsif.2021.0092  PMID: 34343450 
  20. Levin R, Chao DL, Wenger EA, Proctor JL. Insights into population behavior during the COVID-19 pandemic from cell phone mobility data and manifold learning. Nat Comput Sci. 2021;1(9):588-97.  https://doi.org/10.1038/s43588-021-00125-9 
  21. Arkhangelsky D, Athey S, Hirshberg DA, Imbens GW, Wager S. Synthetic difference-in-differences. Am Econ Rev. 2021;111(12):4088-118.  https://doi.org/10.1257/aer.20190159 
  22. Statistisk sentralbyrå (SSB). Tettsteders befolkning og areal. [Population and area of towns]. Oslo: SSB. [Accessed: 24 Feb 2022]. Norwegian. Available from: https://www.ssb.no/befolkning/folketall/statistikk/tettsteders-befolkning-og-areal
  23. Park M, Lim JT, Wang L, Cook AR, Dickens BL. Urban-rural disparities for COVID-19: evidence from 10 countries and areas in the Western Pacific. Health Data Sci. 2021;2021:9790275.  https://doi.org/10.34133/2021/9790275  PMID: 36405354 
  24. Jeffrey B, Walters CE, Ainslie KEC, Eales O, Ciavarella C, Bhatia S, et al. Anonymised and aggregated crowd level mobility data from mobile phones suggests that initial compliance with COVID-19 social distancing interventions was high and geographically consistent across the UK. Wellcome Open Res. 2020;5(170):170.  https://doi.org/10.12688/wellcomeopenres.15997.1  PMID: 32954015 
  25. Lunke EB. Bystørrelse og reisevaner. [City size and travel habits]. Oslo: Institute of Transport Economics; 2020. Norwegian. Available from: https://www.toi.no/getfile.php/1354172-1601554527/Publikasjoner/T%C3%98I%20rapporter/2020/1786-2020/1786-2020-sam.pdf
  26. Dahlen ØP, Skirbekk H. How trust was maintained in Scandinavia through the first crisis of modernity. Corp Commun. 2021;26(1):23-39.  https://doi.org/10.1108/CCIJ-01-2020-0036 
  27. Koronakommisjonen. Kommisjonens hovedbudskap. [The Commission’s main message]. 2020. Norwegian. Available from: https://files.nettsteder.regjeringen.no/wpuploads01/blogs.dir/421/files/2021/04/Kommisjonens-hovedbudskap.pdf
  28. Nøkkeltall for Norge. Oslo: VG. [Accessed: 21 Feb 2022]. Norwegian. Available from: https://www.vg.no/spesial/corona
  29. Georganas S, Velias A, Vandoros S. On the measurement of disease prevalence. CEPR Covid Econ.2021;69:109-39.
  30. Jewell S, Futoma J, Hannah L, Miller AC, Foti NJ, Fox EB. It’s complicated: characterizing the time-varying relationship between cell phone mobility and COVID-19 spread in the US. NPJ Digit Med. 2021;4(1):152.  https://doi.org/10.1038/s41746-021-00523-3  PMID: 34707199 
  31. Wesolowski A, Eagle N, Noor AM, Snow RW, Buckee CO. The impact of biases in mobile phone ownership on estimates of human mobility. J R Soc Interface. 2013;10(81):20120986.  https://doi.org/10.1098/rsif.2012.0986  PMID: 23389897 
  32. Tizzoni M, Bajardi P, Decuyper A, Kon Kam King G, Schneider CM, Blondel V, et al. On the use of human mobility proxies for modeling epidemics. PLOS Comput Biol. 2014;10(7):e1003716.  https://doi.org/10.1371/journal.pcbi.1003716  PMID: 25010676 
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