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Abstract

Introduction

The current pandemic of coronavirus disease (COVID-19) is unparalleled in recent history as are the social distancing interventions that have led to a considerable halt on the economic and social life of so many countries.

Aim

We aimed to generate empirical evidence about which social distancing measures had the most impact in reducing case counts and mortality.

Methods

We report a quasi-experimental (observational) study of the impact of various interventions for control of the outbreak through 24 April 2020. Chronological data on case numbers and deaths were taken from the daily published figures by the European Centre for Disease Prevention and Control and dates of initiation of various control strategies from the Institute of Health Metrics and Evaluation website and published sources. Our complementary analyses were modelled in R using Bayesian generalised additive mixed models and in STATA using multilevel mixed-effects regression models.

Results

From both sets of modelling, we found that closure of education facilities, prohibiting mass gatherings and closure of some non-essential businesses were associated with reduced incidence whereas stay-at-home orders and closure of additional non-essential businesses was not associated with any independent additional impact.

Conclusions

Our findings are that schools and some non-essential businesses operating ‘as normal’ as well as allowing mass gatherings were incompatible with suppressing disease spread. Closure of all businesses and stay at home orders are less likely to be required to keep disease incidence low. Our results help identify what were the most effective non-pharmaceutical interventions in this period.

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/content/10.2807/1560-7917.ES.2021.26.28.2001401
2021-07-15
2021-07-27
http://instance.metastore.ingenta.com/content/10.2807/1560-7917.ES.2021.26.28.2001401
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References

  1. Jacobs LA. Rights and quarantine during the SARS global health crisis: differentiated legal consciousness in Hong Kong, Shanghai, and Toronto. Law Soc Rev. 2007;41(3):511-52.  https://doi.org/10.1111/j.1540-5893.2007.00313.x 
  2. Johnson HC, Gossner CM, Colzani E, Kinsman J, Alexakis L, Beauté J, et al. Potential scenarios for the progression of a COVID-19 epidemic in the European Union and the European Economic Area, March 2020. Euro Surveill. 2020;25(9):2000202.  https://doi.org/10.2807/1560-7917.ES.2020.25.9.2000202  PMID: 32156332 
  3. Brooks SK, Webster RK, Smith LE, Woodland L, Wessely S, Greenberg N, et al. The psychological impact of quarantine and how to reduce it: rapid review of the evidence. Lancet. 2020;395(10227):912-20.  https://doi.org/10.1016/S0140-6736(20)30460-8  PMID: 32112714 
  4. Office for Budget Responsibility (OBR). Coronavirus lockdown to deliver large (but hopefully temporary) shock to the economy and public finances. London: OBR; 2020. Available from: https://obr.uk/category/coronavirus
  5. Massaro E, Ganin A, Perra N, Linkov I, Vespignani A. Resilience management during large-scale epidemic outbreaks. Sci Rep. 2018;8(1):1859.  https://doi.org/10.1038/s41598-018-19706-2  PMID: 29382870 
  6. Adam D. Special report: The simulations driving the world’s response to COVID-19. Nature. 2020;580(7803):316-8.  https://doi.org/10.1038/d41586-020-01003-6  PMID: 32242115 
  7. Sridhar D, Majumder MS. Modelling the pandemic. BMJ. 2020;369:m1567.  https://doi.org/10.1136/bmj.m1567  PMID: 32317328 
  8. Borenstein S, Johnson CK. Modeling coronavirus: ‘Uncertainty is the only certainty’. New York: Associated Press; 2020. Available from: https://apnews.com/88866498ff5c908e5f28f7b5b5e5b695
  9. Andrews C. Predicting the pandemic: mathematical modelling tackles Covid-19. London: Engineering and Technology; 2020. Available from: https://eandt.theiet.org/content/articles/2020/04/predicting-the-pandemic-mathematical-modelling-tackles-covid-19
  10. McCoy D. Faith in coronavirus modelling is no substitute for sound political judgment. London: The Guardian; 2020. Available from: https://www.theguardian.com/commentisfree/2020/apr/10/modelling-pandemic-politicians-decisions-science
  11. Rosenbaum PR. How to see more in observational studies: Some new quasi-experimental devices. Annu Rev Stat Appl. 2015;2(1):21-48.  https://doi.org/10.1146/annurev-statistics-010814-020201 
  12. Harris AD, McGregor JC, Perencevich EN, Furuno JP, Zhu J, Peterson DE, et al. The use and interpretation of quasi-experimental studies in medical informatics. J Am Med Inform Assoc. 2006;13(1):16-23.  https://doi.org/10.1197/jamia.M1749  PMID: 16221933 
  13. Besag J, York J, Mollié A. Bayesian image restoration, with two applications in spatial statistics. Ann Inst Stat Math. 1991;43(1):1-20.  https://doi.org/10.1007/BF00116466 
  14. Obaromi D. Spatial modelling of some conditional autoregressive priors in a disease mapping model: the Bayesian approach. BJSTR. 2019;14(3):10680-6.  https://doi.org/http://dx.doi.org/10.26717/BJSTR.2019.14.002555 
  15. Bi Q, Wu Y, Mei S, Ye C, Zou X, Zhang Z, et al. Epidemiology and transmission of COVID-19 in 391 cases and 1286 of their close contacts in Shenzhen, China: a retrospective cohort study. Lancet Infect Dis. 2020;20(8):911-9.  https://doi.org/10.1016/S1473-3099(20)30287-5  PMID: 32353347 
  16. Backer JA, Klinkenberg D, Wallinga J. Incubation period of 2019 novel coronavirus (2019-nCoV) infections among travellers from Wuhan, China, 20-28 January 2020. Euro Surveill. 2020;25(5):2000062.  https://doi.org/10.2807/1560-7917.ES.2020.25.5.2000062  PMID: 32046819 
  17. Lauer SA, Grantz KH, Bi Q, Jones FK, Zheng Q, Meredith HR, et al. The incubation period of coronavirus disease 2019 (COVID-19) from publicly reported confirmed cases: estimation and application. Ann Intern Med. 2020;172(9):577-82.  https://doi.org/10.7326/M20-0504  PMID: 32150748 
  18. Elias C, Sekri A, Leblanc P, Cucherat M, Vanhems P. The incubation period of COVID-19: A meta-analysis. Int J Infect Dis. 2021;104:708-10.  https://doi.org/10.1016/j.ijid.2021.01.069  PMID: 33548553 
  19. Docherty AB, Harrison EM, Green CA, Hardwick HE, Pius R, Norman L, et al. Features of 16,749 hospitalised UK patients with COVID-19 using the ISARIC WHO Clinical Characterisation Protocol; medRxiv 2020. https://doi.org/10.1101/2020.04.23.20076042
  20. Zhou F, Yu T, Du R, Fan G, Liu Y, Liu Z, et al. Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study. Lancet. 2020;395(10229):1054-62.  https://doi.org/10.1016/S0140-6736(20)30566-3  PMID: 32171076 
  21. Verity R, Okell LC, Dorigatti I, Winskill P, Whittaker C, Imai N, et al. Estimates of the severity of coronavirus disease 2019: a model-based analysis. Lancet Infect Dis. 2020;20(6):669-77.  https://doi.org/10.1016/S1473-3099(20)30243-7  PMID: 32240634 
  22. Chatterjee S, Hadi AS. Regression analysis by example. 4th ed. Hoboken: John Wiley & Sons; 2015.
  23. Regorz A. How to interpret a collinearity diagnostics table in SPSS. Regorz Statiztik; 2020. Available from: http://www.regorz-statistik.de/en/collinearity_diagnostics_table_SPSS.html
  24. Wright O. People told to wait ten days for coronavirus test results. London: The Times; 2020. Available from: https://www.thetimes.co.uk/article/people-told-to-wait-ten-days-for-coronavirus-test-results-7dpjb0t96
  25. Pan A, Liu L, Wang C, Guo H, Hao X, Wang Q, et al. Association of public health interventions with the epidemiology of the COVID-19 outbreak in Wuhan, China. JAMA. 2020;323(19):1915-23.  https://doi.org/10.1001/jama.2020.6130  PMID: 32275295 
  26. Zhang J, Litvinova M, Liang Y, Wang Y, Wang W, Zhao S, et al. Changes in contact patterns shape the dynamics of the COVID-19 outbreak in China. Science. 2020;368(6498):1481-6.  https://doi.org/10.1126/science.abb8001  PMID: 32350060 
  27. Zhang J, Litvinova M, Wang W, Wang Y, Deng X, Chen X, et al. Evolving epidemiology and transmission dynamics of coronavirus disease 2019 outside Hubei province, China: a descriptive and modelling study. Lancet Infect Dis. 2020;20(7):793-802.  https://doi.org/10.1016/S1473-3099(20)30230-9  PMID: 32247326 
  28. Haug N, Geyrhofer L, Londei A, Dervic E, Desvars-Larrive A, Loreto V, et al. Ranking the effectiveness of worldwide COVID-19 government interventions. Nat Hum Behav. 2020;4(12):1303-12.  https://doi.org/10.1038/s41562-020-01009-0  PMID: 33199859 
  29. Koh WC, Naing L, Chaw L, Rosledzana MA, Alikhan MF, Jamaludin SA, et al. What do we know about SARS-CoV-2 transmission? A systematic review and meta-analysis of the secondary attack rate and associated risk factors. PLoS One. 2020;15(10):e0240205.  https://doi.org/10.1371/journal.pone.0240205  PMID: 33031427 
  30. Viner RM, Russell SJ, Croker H, Packer J, Ward J, Stansfield C, et al. School closure and management practices during coronavirus outbreaks including COVID-19: a rapid systematic review. Lancet Child Adolesc Health. 2020;4(5):397-404.  https://doi.org/10.1016/S2352-4642(20)30095-X  PMID: 32272089 
  31. Cowling BJ, Lau EH, Lam CL, Cheng CK, Kovar J, Chan KH, et al. Effects of school closures, 2008 winter influenza season, Hong Kong. Emerg Infect Dis. 2008;14(10):1660-2.  https://doi.org/10.3201/eid1410.080646  PMID: 18826841 
  32. Hens N, Ayele GM, Goeyvaerts N, Aerts M, Mossong J, Edmunds JW, et al. Estimating the impact of school closure on social mixing behaviour and the transmission of close contact infections in eight European countries. BMC Infect Dis. 2009;9(1):187.  https://doi.org/10.1186/1471-2334-9-187  PMID: 19943919 
  33. Flasche S, Edmunds WJ. The role of schools and school-aged children in SARS-CoV-2 transmission. Lancet Infect Dis. 2021;21(3):298-9. PMID: 33306982 
  34. Folkhälsomyndigheten [Public Health Agency of Sweden]. COVID-19 in schoolchildren: A comparison between Finland and Sweden. Stockholm: Public Health Agency of Sweden; 2020. Available from: www.folkhalsomyndigheten.se/contentassets/c1b78bffbfde4a7899eb0d8ffdb57b09/covid-19-school-aged-children.pdf
  35. von Bismarck-Osten C, Borusyak K, Schönberg U. The role of schools in transmission of the SARS-CoV-2 virus: Quasi-experimental evidence from Germany. Essen: Ruhr Economic Papers; 2020. Available from: https://www.rwi-essen.de/media/content/pages/publikationen/ruhr-economic-papers/rep_20_882.pdf
  36. Shen K, Yang Y, Wang T, Zhao D, Jiang Y, Jin R, et al. Diagnosis, treatment, and prevention of 2019 novel coronavirus infection in children: experts’ consensus statement. World J Pediatr. 2020;16(3):223-31.  https://doi.org/10.1007/s12519-020-00343-7  PMID: 32034659 
  37. Public Health England (PHE). Investigation of novel SARS-COV-2 variant: variant of concern 202012/01. London: PHE; 2020. Available from: https://www.gov.uk/government/publications/investigation-of-novel-sars-cov-2-variant-variant-of-concern-20201201
  38. Hoang V-T, Gautret P. Infectious diseases and mass gatherings. Curr Infect Dis Rep. 2018;20(11):44.  https://doi.org/10.1007/s11908-018-0650-9  PMID: 30155747 
  39. Botelho-Nevers E, Gautret P. Outbreaks associated to large open air festivals, including music festivals, 1980 to 2012. Euro Surveill. 2013;18(11):20426.  https://doi.org/10.2807/ese.18.11.20426-en  PMID: 23517872 
  40. Wang M, Yan M, Xu H, Liang W, Kan B, Zheng B, et al. SARS-CoV infection in a restaurant from palm civet. Emerg Infect Dis. 2005;11(12):1860-5.  https://doi.org/10.3201/eid1112.041293  PMID: 16485471 
  41. 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. PMID: 33323424 
  42. Brainard J, Jones NR, Lake IR, Hooper L, Hunter PR. Community use of face masks and similar barriers to prevent respiratory illness such as COVID-19: a rapid scoping review. Euro Surveill. 2020;25(49):2000725.  https://doi.org/10.2807/1560-7917.ES.2020.25.49.2000725  PMID: 33303066 
  43. Cowling BJ, Leung GM. Face masks and COVID-19: don’t let perfect be the enemy of good. Euro Surveill. 2020;25(49):2001998.  https://doi.org/10.2807/1560-7917.ES.2020.25.49.2001998  PMID: 33303063 
  44. Watanabe S. Asymptotic equivalence of Bayes cross validation and widely applicable information criterion in singular learning theory. J Mach Learn Res. 2010;11(Dec):3571-94.
  45. Pettit L. The conditional predictive ordinate for the normal distribution. J R Stat Soc B. 1990;52(1):175-84.  https://doi.org/10.1111/j.2517-6161.1990.tb01780.x 
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