Journal of Preventive Medicine and Public Health

  • 제55권2호
  • /
  • Pages.134-143
  • /
  • 2022
  • /
  • 1975-8375(pISSN)
  • /
  • 2233-4521(eISSN)
대한예방의학회 (The Korean Society for Preventive Medicine)
권호 표지
DOI QR코드

DOI QR Code

In-depth Correlation Analysis of SARS-CoV-2 Effective Reproduction Number and Mobility Patterns: Three Groups of Countries

  • Setti, Mounir Ould (Institute of Public Health and Clinical Nutrition, University of Eastern Finland) ;
  • Tollis, Sylvain (Institute of Biomedicine, University of Eastern Finland)
  • 투고 : 2021.09.28
  • 심사 : 2021.12.13
  • 발행 : 2022.03.31
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

Objectives: Many governments have imposed-and are still imposing-mobility restrictions to contain the coronavirus disease 2019 (COVID-19) pandemic. However, there is no consensus on whether policy-induced reductions of human mobility effectively reduce the effective reproduction number (Rt) of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Several studies based on country-restricted data reported conflicting trends in the change of the SARS-CoV-2 Rt following mobility restrictions. The objective of this study was to examine, at the global scale, the existence of regional specificities in the correlations between Rt and human mobility. Methods: We computed the Rt of SARS-CoV-2 using data on worldwide infection cases reported by the Johns Hopkins University, and analyzed the correlation between Rt and mobility indicators from the Google COVID-19 Community Mobility Reports in 125 countries, as well as states/regions within the United States, using the Pearson correlation test, linear modeling, and quadratic modeling. Results: The correlation analysis identified countries where Rt negatively correlated with residential mobility, as expected by policymakers, but also countries where Rt positively correlated with residential mobility and countries with more complex correlation patterns. The correlations between Rt and residential mobility were non-linear in many countries, indicating an optimal level above which increasing residential mobility is counterproductive. Conclusions: Our results indicate that, in order to effectively reduce viral circulation, mobility restriction measures must be tailored by region, considering local cultural determinants and social behaviors. We believe that our results have the potential to guide differential refinement of mobility restriction policies at a country/regional resolution.

키워드

과제정보

Ari Voutilainen was part of the initial phase of the study but could not continue with the project. The R code used for analysis can be found here: https://github.com/mounirsetti/COVID-19-Rt-and-Mobility-Patterns. Mounir Ould Setti is employed by IQVIA, a global contract research organization with multiple clients from the pharmaceutical industry. However, IQVIA neither initiated the study nor funded it. Sylvain Tollis is funded by the Sigrid Juselius Foundation.

참고문헌

  1. Dong E, Du H, Gardner L. An interactive web-based dashboard to track COVID-19 in real time. Lancet Infect Dis 2020;20(5):533-534. https://doi.org/10.1016/s1473-3099(20)30120-1
  2. Google. COVID-19 community mobility report; 2020 [cited 2020 Aug 31]. Available from: https://www.google.com/covid19/mobility?hl=en.
  3. Kuniya T. Evaluation of the effect of the state of emergency for the first wave of COVID-19 in Japan. Infect Dis Model 2020;5:580-587.
  4. Cauchemez S, Boelle PY, Donnelly CA, Ferguson NM, Thomas G, Leung GM, et al. Real-time estimates in early detection of SARS. Emerg Infect Dis 2006;12(1):110-113. https://doi.org/10.3201/eid1201.050593
  5. Anderson RM, May RM. Population biology of infectious diseases: part I. Nature 1979;280(5721):361-367. https://doi.org/10.1038/280361a0
  6. Gostic KM, McGough L, Baskerville EB, Abbott S, Joshi K, Tedijanto C, et al. Practical considerations for measuring the effective reproductive number, Rt. PLoS Comput Biol 2020;16(12):e1008409. https://doi.org/10.1371/journal.pcbi.1008409
  7. Durmus H, Gokler ME, Metintas S. The effectiveness of community-based social distancing for mitigating the spread of the COVID-19 pandemic in Turkey. J Prev Med Public Health 2020;53(6):397-404. https://doi.org/10.3961/jpmph.20.381
  8. 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
  9. Dainton C, Hay A. Quantifying the relationship between lockdowns, mobility, and effective reproduction number (Rt) during the COVID-19 pandemic in the Greater Toronto Area. BMC Public Health 2021;21(1):1658. https://doi.org/10.1186/s12889-021-11684-x
  10. Ryu S, Ali ST, Noh E, Kim D, Lau EH, Cowling BJ. Transmission dynamics and control of two epidemic waves of SARS-CoV-2 in South Korea. BMC Infect Dis 2021;21(1):485. https://doi.org/10.1186/s12879-021-06204-6
  11. Wang S, Liu Y, Hu T. Examining the change of human mobility adherent to social restriction policies and its effect on COVID19 cases in Australia. Int J Environ Res Public Health 2020;17(21):7930. https://doi.org/10.3390/ijerph17217930
  12. Linka K, Peirlinck M, Kuhl E. The reproduction number of COVID-19 and its correlation with public health interventions. Comput Mech 2020;66(4):1035-1050. https://doi.org/10.1007/s00466-020-01880-8
  13. Oh J, Lee HY, Khuong QL, Markuns JF, Bullen C, Barrios OE, et al. Mobility restrictions were associated with reductions in COVID-19 incidence early in the pandemic: evidence from a real-time evaluation in 34 countries. Sci Rep 2021;11(1):13717. https://doi.org/10.1038/s41598-021-92766-z
  14. Aktay A, Bavadekar S, Cossoul G, Davis J, Desfontaines D, Fabrikant A, et al. Google COVID-19 community mobility reports: anonymization process description (version 1.1). arXiv [Preprint]. 2020 [cited 2021 Jun 20]. Available from: https://doi.org/10.48550/arXiv.2004.04145.
  15. Cori A, Cauchemez S, Ferguson NM, Fraser C, Dahlqwist E, Demarsh PA, et al. EpiEstim: estimate time varying reproduction numbers from epidemic curve; 2020 [cited 2020 Jul 8]. Available from: https://cran.r-project.org/web/packages/EpiEstim/index.html.
  16. Zhang T, Ding S, Zeng Z, Cheng H, Zhang C, Mao X, et al. Estimation of incubation period and serial interval for SARS-CoV-2 in Jiangxi, China, and an updated meta-analysis. J Infect Dev Ctries 2021;15(3):326-332. https://doi.org/10.3855/jidc.14025
  17. Lipsitch M, Joshi K, Cobey SE. Comment on Pan A, et al., "Association of public health interventions with the epidemiology of the COVID-19 outbreak in Wuhan, China," JAMA, Published online April 10, 2020, doi:10.1001/jama.2020.6130. 2020 [cited 2021 Jun 20]. Available from: https://dash.harvard.edu/handle/1/42660128.
  18. Durbin J, Watson GS. Testing for serial correlation in least squares regression: I. Biometrika 1950;37(3/4):409-428. https://doi.org/10.1093/biomet/37.3-4.409
  19. Durbin J, Watson GS. Testing for serial correlation in least squares regression. II. Biometrika 1951;38(1-2):159-178. https://doi.org/10.2307/2332325
  20. Cochrane D, Orcutt GH. Application of least squares regression to relationships containing auto-correlated error terms. J Am Stat Assoc 1949;44(245):32-61. https://doi.org/10.2307/2280349
  21. Sorokowska A, Sorokowski P, Hilpert P, Cantarero K, Frackowiak T, Ahmadi K, et al. Preferred interpersonal distances: a global comparison. J Cross Cult Psychol 2017;48(4):577-592. https://doi.org/10.1177/0022022117698039
  22. Lim JS, Noh E, Shim E, Ryu S. Temporal changes in the risk of superspreading events of coronavirus disease 2019. Open Forum Infect Dis 2021;8(7):ofab350. https://doi.org/10.1093/ofid/ofab350