Skip to main content

How Effective Were and Are Lockdowns?

  • Chapter
  • First Online:
SARS-CoV-2 and Coronacrisis

Abstract

Efficiency and necessity of lockdown measures implemented on a scale of the whole world, cause much controversy. In this Chapter, we analyse whether total lockdowns are helpful in stopping spread of Coronavirus disease—2019 (COVID-19) and future similar global diseases, by means of investigating herd immunity formation to Severe acute respiratory syndrome-related coronavirus-2, the viral causative agent of COVID-19 disease (SARS-CoV-2). In the current absence of a vaccine, herd immunity remains the only way to stabilise human population reaction to the novel viral pathogen. We suppose that a real hazard of COVID-19 lockdowns is associated with the common governmental belief that it is the lockdowns that saved humanity from excessive mortality connected with COVID-19. Our research based on SIR compartmental model of SARS-CoV-2 spread proves that it is a very dangerous misbelief with far-reaching consequences. It is SARS-CoV-2 relatively low contagiousness and case fatality rate that led to avoidance of millions of deaths, not lockdowns. Non-evidence-based reliance of governments just on total lockdown as a universal measure of the pandemic containment may be much more devastating in the future, in case of possible consecutive waves of SARS-CoV-2 or any other viral pathogen.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 119.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 159.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 159.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    In spite of China had many quarantine zones, at no time Chinese government imposed the full lockdown on a scale of the whole country. Russia has many in common with China, e.g. diverse population density in different regions, large territory, different natural conditions in different provinces. None the less, regretfully Russian government did not follow the Chinese way during the first wave of the pandemic in managing various territorial zones in regard to SARS-CoV-2 spread.

References

  1. L'Angiocola PD, Monti M (2020) COVID-19: the critical balance between appropriate governmental restrictions and expected economic, psychological and social consequences in Italy. Are we going in the right direction? Acta Biomed 91(2):35–38. doi: https://doi.org/10.23750/abm.v91i2.9575

  2. Taghrir MH, Akbarialiabad H, Ahmadi MM (2020) Efficacy of mass quarantine as leverage of health system governance during COVID-19 outbreak: a mini The Netherland policy review. Arch Iran Med 23(4):265–267. https://doi.org/10.34172/aim.2020.08

    Article  PubMed  Google Scholar 

  3. Quigley MC, Attanayake J, King A, Prideaux F (2020, May) A multi-hazards earth science perspective on the COVID-19 pandemic: the potential for concurrent and cascading crises. Environ Syst Decis 16:1–17. doi: https://doi.org/10.1007/s10669-020-09772-1

  4. Çelik I, Saatçi E, Eyüboğlu AF (2020, April) Emerging and reemerging respiratory viral infections up to COVID-19. Turk. J. Med. Sci 15. doi: https://doi.org/10.3906/sag-2004-126

  5. Gralinski LE, Menachery VD (2020) Return of the Coronavirus: 2019-nCoV. Viruses 12(2):E135. https://doi.org/10.3390/v12020135

    Article  PubMed  Google Scholar 

  6. Kakodkar P, Kaka N, Baig MN (2020) A comprehensive literature review on the clinical presentation, and management of the pandemic coronavirus disease 2019 (COVID-19). Cureus. 12(4):e7560. https://doi.org/10.7759/cureus.7560

    Article  PubMed  PubMed Central  Google Scholar 

  7. Kock RA, Karesh WB, Veas F et al (2020) 2019-nCoV in context: lessons learned? Lancet Planet Health. 4(3):e87–e88. https://doi.org/10.1016/S2542-5196(20)30035-8

    Article  PubMed  PubMed Central  Google Scholar 

  8. Kwok KO, Lai F, Wei WI, et al. (2020, March) Herd immunityestimating the level required to halt the COVID-19 epidemics in affected countries. J Infect 21:S0163–4453(20)30154–7. doi: https://doi.org/10.1016/j.jinf.2020.03.027

  9. Prompetchara E, Ketloy C, Palaga T (2020) Immune responses in COVID-19 and potential vaccines: lessons learned from SARS and MERS epidemic. Asian Pac J Allergy Immunol 38(1):1–9. https://doi.org/10.12932/AP-200220-0772

    Article  CAS  PubMed  Google Scholar 

  10. Syal K (2020, April) COVID-19: herd immunity and convalescent plasma transfer therapy. J Med Viro. 13:32281679. doi: https://doi.org/10.1002/jmv.25870

  11. Jarlov H (2020) Anti-SARS-CoV-2 screening. https://docs.google.com/spreadsheets/d/17Tf1Ln9VuE5ovpnhLRBJH-33L5KRaiB3NhvaiF3hWC0/edit#gid=0. Accessed 15 June 2020

  12. Chavez S, Long B, Koyfman A, et al. (2020, March) Coronavirus disease (COVID-19): a primer for emergency physicians. Am J Emerg Med 24:7102516. doi: https://doi.org/10.1016/j.ajem.2020.03.036

  13. Kolifarhood G, Aghaali M, Saadati HM et al (2020) Epidemiological and clinical aspects of COVID-19; a narrative review. Arch Acad Emerg Med 8(1):e41

    PubMed  PubMed Central  Google Scholar 

  14. Lai CC, Wang CY, Wang YH, et al. (2020, March) Global epidemiology of coronavirus disease 2019 (COVID-19): disease incidence, daily cumulative index, mortality, and their association with country healthcare resources and economic status. Int J Antimicrob Agents 19:105946. doi: https://doi.org/10.1016/j.ijantimicag.2020.105946

  15. Ohannessian R, Duong TA, Odone A (2020) Global telemedicine implementation and integration within health systems to fight the COVID-19 pandemic: a call to action. JMIR Public Health Surveill 6(2):e18810. https://doi.org/10.2196/18810

    Article  PubMed  PubMed Central  Google Scholar 

  16. Şahin U, Şahin T (2020) Forecasting the cumulative number of confirmed cases of COVID-19 in Italy, UK and USA using fractional nonlinear grey Bernoulli model. Chaos Soliton Fract 138:109948. https://doi.org/10.1016/j.chaos.2020.109948

    Article  Google Scholar 

  17. Buldú JM, Antequera DR, Aguirre J (2020) The resumption of sports competitions after COVID-19 lockdown: the case of the Spanish football league. Chaos Soliton Fract 138:109964. https://doi.org/10.1016/j.chaos.2020.109964

    Article  Google Scholar 

  18. Dal Molin Ribeiro MH, Da Silva RG, Cocco Mariani V, Dos Santos CL (2020) Short-term forecasting COVID-19 cumulative confirmed cases: perspectives for Brazil. Chaos Soliton Fract 138:109853. https://doi.org/10.1016/j.chaos.2020.109853

    Article  Google Scholar 

  19. Brugnago E, Da Silva RM, Manchein C, Beimsa MW (2020) How relevant is the decision of containment measures against COVID-19 applied ahead of time? Chaos Soliton Fract 140:110164. https://doi.org/10.1016/j.chaos.2020.110164

    Article  Google Scholar 

  20. National Institute of Infectious Diseases (2020) Field briefing: diamond princess COVID-19 cases. Tokyo, Japan. https://www.niid.go.jp/niid/en/2019-ncov-e/9407-covid-dp-fe-01.html. Accessed 25 February 2020

  21. Nishiura H (2020) Backcalculating the incidence of infection with COVID-19 on the diamond princess. J Clin Med 9(3):657. https://doi.org/10.3390/jcm9030657

    Article  CAS  PubMed Central  Google Scholar 

  22. Rocklöv J, Sjödin H, Wilder-Smith A (2020) COVID-19 outbreak on the diamond princess cruise ship: estimating the epidemic potential and effectiveness of public health countermeasures. J Trav Med taaa030. doi:https://doi.org/10.1093/jtm/taaa030

  23. Russell TW, Hellewell J, Jarvis CI et al (2020) Estimating the infection and case fatality ratio for coronavirus disease (COVID-19) using age-adjusted data from the outbreak on the Diamond Princess cruise ship, February 2020. Euro Surveill 25(12):2000256. https://doi.org/10.2807/1560-7917.ES.2020.25.12.2000256

    Article  PubMed Central  Google Scholar 

  24. Nishiura H, Kobayashi T, Yang Y et al (2020) The rate of underascertainment of novel coronavirus (2019-nCoV) infection: estimation using Japanese passengers data on evacuation flights. J Clin Med 9(2):419. https://doi.org/10.3390/jcm9020419

    Article  PubMed Central  Google Scholar 

  25. Ministry of Health, Labour and Welfare of Japan (2020). https://www.mhlw.go.jp/stf/seisakunitsuite/bunya/newpage_00032.html. Accessed 21 April 2020

  26. KCDC (2020) The updates on COVID-19 in Korea. https://www.cdc.go.kr. Accessed 14 June 2020

  27. Taiwan Centers for Disease Control (2020). https://www.cdc.gov.tw/En. Accessed 20 June 2020

  28. Amtliches Dashboard COVID19 öffentlich zugängliche Informationen (2020). https://info.gesundheitsministerium.at/dashboard_Hosp.html?l=de. Accessed 20 June 2020

  29. RKI (2020) Aktueller Lage-/Situationsbericht des RKI zu COVID-19. https://www.rki.de/DE/Content/InfAZ/N/Neuartiges_Coronavirus/Situationsberichte/Gesamt.html. Accessed 22 June 2020

  30. Government of Iceland (2020) Large scale testing of general population in Iceland underway. https://www.government.is/news/article/2020/03/15/Large-scale-testing-of-general-population-in-Iceland-underway. Accessed 5 April 2020

  31. Italian doctors doubt testing is Italy's route out of coronavirus lockdown (2020). The Local.it. https://www.thelocal.it/20200415/will-mass-testing-help-italy-out-of-coronavirus-lockdown. Accessed 15 April 2020

  32. DGS (2020) Direção-Geral da Saúde. https://covid19.min-saude.pt/ponto-de-situacao-atual-em-portugal. Accessed 16 April 2020

  33. Stopcoronavirus (2020). https://xn--80aesfpebagmfblc0a.xn--p1ai. Accessed 19 April 2020

  34. UK PHE (2020) Number of coronavirus (COVID-19) cases and risk in the UK. https://www.gov.uk/guidance/coronavirus-covid-19-information-for-the-public. Accessed 21 April 2020

  35. VanMeter KC, Hubert RJ (2015) Microbiology for the healthcare professional. St. Louis: Mosby, USA

    Google Scholar 

  36. Jung SM, Akhmetzhanov AR, Hayashi K et al (2020) Real-time estimation of the risk of death from novel coronavirus (COVID-19) infection: inference using exported cases. J Clin Med 9(2):523. https://doi.org/10.3390/jcm9020523

    Article  PubMed Central  Google Scholar 

  37. Al-qaness MAA, Ewees AA, Fan H et al (2020) Optimization method for forecasting confirmed cases of COVID-19 in China. J Clin Med 9(3):674. https://doi.org/10.3390/jcm9030674

    Article  CAS  PubMed Central  Google Scholar 

  38. Sasaki K (2020) COVID-19 dynamics with SIR model. The First Cry of Atom. https://www.lewuathe.com/covid-19-dynamics-with-sir-model.html. Accessed 15 March 2020

  39. Jiang M, Li Y, Han M, et al. (2020, April) Recurrent PCR positivity after hospital discharge of people with coronavirus disease 2019 (COVID-19). J Infect 11:32289343. doi: https://doi.org/10.1016/j.jinf.2020.03.024

  40. Lan L, Xu D, Ye G, et al. (2020, February) Positive RT-PCR test results in patients recovered from COVID-19. JAMA 27:https://doi.org/10.1001/jama.2020.2783

  41. Nicola M, O’Neill N, Sohrabi C, et al. (2020, April) Evidence based management guideline for the COVID-19 pandemicreview article. Int J Surg 11:7151371. doi: https://doi.org/10.1016/j.ijsu.2020.04.001

  42. Hou Z, Gou Q (1988) Homogeneous denumerable markov processes. Springer, The Netherlands, Amsterdam

    Google Scholar 

  43. Lakshmikantham V, Matrosov VM, Sivasundaram S (1991) Vector Lyapunov functions and stability analysis of nonlinear systems. Springer, The Netherlands, Dordrecht

    Book  Google Scholar 

  44. Lakshmikantham V, Leela S, Martynyuk AA (2015) Stability analysis of nonlinear systems. Springer, The Netherlands, Dordrecht

    Book  Google Scholar 

  45. Stewart WJ (ed) (1995) Computations with Markov Chains. Springer, USA, Boston

    Google Scholar 

  46. Kuniya T (2020) Prediction of the epidemic peak of coronavirus disease in Japan, 2020. J Clin Med 9(3):789. https://doi.org/10.3390/jcm9030789

    Article  CAS  PubMed Central  Google Scholar 

  47. Sanche S, Lin YT, Xu C, et al. The novel coronavirus, 2019-nCoV, is highly contagious and more infectious than initially estimated [Preprint]. https://www.medrxiv.org/content/https://doi.org/10.1101/2020.02.07.20021154v1. doi: 0.1101/2020.02.07.20021154

  48. Chen N, Zhou M, Dong X, et al. (2020) Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: a descriptive study. The Lancet. doi: https://doi.org/10.1016/S0140-6736(20)30211-7

  49. Pirouz B, Haghshenas Sina S, Haghshenas Sami S et al (2020) Investigating a serious challenge in the sustainable development process: analysis of confirmed cases of COVID-19 (new type of coronavirus) through a binary classification using artificial intelligence and regression analysis. Sustainability 12(6):2427. https://doi.org/10.3390/su12062427

    Article  CAS  Google Scholar 

  50. Sharov KS (2020) COVID-19 pandemic: a survival challenge to humanity unseen thus far or a déjà vu experience? Beacon J Stud Ideol Ment Dimens 3(1):011040018. https://hdl.handle.net/20.500.12656/thebeacon.3.011040018

  51. Sanov IN (1957) On the probability of large deviations of random variables. Math Bull 42(84):11–44. http://www.mathnet.ru/links/0372f88006a6fcf2b255e73cb86d6317/sm5043.pdf. Accessed 12 March 2020

  52. Borovkov AA, Mogulsky AA (2020) On the principles of large deviations in metric spaces. Sib Math J 51(6):1251–1269. https://www.emis.de/journals/SMZ/2010/06/1251.pdf. Accessed 20 March 2020

  53. Ethier NS, Kurtz TG (2005) Markov processes. In Wiley Series in Probability and Mathematical Statistics. New York: John Wiley & Sons Inc.

    Google Scholar 

  54. Gasnikov AV (2019) Lectures on random processes. MFTI, Moscow

    Google Scholar 

  55. Lalmuanawma S, Hussain J, Chhakchhuak L (2020) Applications of machine learning and artificial intelligence for Covid-19 (SARS-CoV-2) pandemic: a review. Chaos Soliton Fract 139:110059. https://doi.org/10.1016/j.chaos.2020.110059

    Article  Google Scholar 

  56. Çakan S (2020) Dynamic analysis of a mathematical model with health care capacity for COVID-19 pandemic. Chaos Soliton Fract 139:110033. https://doi.org/10.1016/j.chaos.2020.110033

    Article  Google Scholar 

  57. Doungmo Goufo EF, Khan Y, Chaudhry QA (2020) HIV and shifting epicenters for COVID-19, an alert for some countries. Chaos Soliton Fract 139:110030. https://doi.org/10.1016/j.chaos.2020.110030

    Article  Google Scholar 

  58. Swapnarekha H, Behera SH, Nayak J, Naik B (2020) Role of intelligent computing in COVID-19 prognosis: a state-of-the-art review. Chaos Soliton Fract 138:109947. https://doi.org/10.1016/j.chaos.2020.109947

    Article  CAS  Google Scholar 

  59. Sassin W (2019) Deja Vue? Beacon J Stud Ideol Ment Dimens 2019;2(2):020210216. https://hdl.handle.net/20.500.12656/thebeacon.2.020210216

  60. Sassin W (2019) Er-Schöpfung der Schöpfung, oder Eine neue Kulturstufe in der Entwicklung des homo. Beacon J Stud Ideol Ment Dimens 2(2):020510203. https://hdl.handle.net/20.500.12656/thebeacon.2.020510203

  61. Lysenko LA (2019) Back to anthropology: what does it mean to development studies? Beacon J Stud Ideol Ment Dimens 2(2):020000000. https://hdl.handle.net/20.500.12656/thebeacon.2.020000000

  62. Donskikh OA (2019) Horror Zivilisationis, oder Horror der Subjektivität. Beacon J Stud Ideol Ment Dimens 2(2):020110205. https://hdl.handle.net/20.500.12656/thebeacon.2.020110205

  63. Gnes A (2019) Festival culture as a means of preserving vital differences in the ideologically equalised world. Beacon J Stud Ideol Ment Dimens 2(2):020310005. https://hdl.handle.net/20.500.12656/thebeacon.2.020310005

  64. Sassin W, Donskikh OA, Gnes A, et al. (2018) Evolutionary environments. Homo Sapiensan Endangered Species? Innsbruck: Studia Universitätsverlag

    Google Scholar 

  65. Sassin W (2018) Die Transformation des sozialen Bewusstseins. Beacon J Stud Ideol Ment Dimens 1(1):010210201. https://hdl.handle.net/20.500.12656/thebeacon.1.010210201

  66. Sassin W (2020) Die Grenzen der Ökonomie: GlobalisierungVom Füllhorn zum Giftbecher? Eur Crossrd 1(1):010410216. https://hdl.handle.net/20.500.12656/eurcrossrd.1.010410216

  67. Sassin W (2018) Zu den Grenzen menschlicher Erkenntnis. Beacon J Stud Ideol Ment Dimens 1(1):010310202. https://hdl.handle.net/20.500.12656/thebeacon.1.010310202

  68. Sharov KS (2020, May) Adaptation to SARS-CoV-2 under stress: role of distorted information. Eur J Clin Invest 31. doi: https://doi.org/10.1111/eci.13294

  69. Sharov KS (2020) SARS-CoV-2-related pneumonia cases in pneumonia picture in Russia in March-May 2020: secondary bacterial pneumonia and viral co-infections. J Glob Health 10(2):020504 [in press]. doi: https://doi.org/10.7189/jogh.10.020504

  70. Colson P, La Scola B, Esteves-Vieira V et al (2020) Letter to the editor: plenty of coronaviruses but no SARS-CoV-2. Euro Surveill 25(8):2000171. https://doi.org/10.2807/1560-7917.ES.2020.25.8.2000171

    Article  PubMed Central  Google Scholar 

  71. Giordano G, Blanchini F, Bruno R, et al. (2020, April) Modelling the COVID-19 epidemic and implementation of population-wide interventions in Italy. Nat Med 22:1–6. doi: https://doi.org/10.1038/s41591-020-0883-7

  72. Luo Y, Mao L, Yuan X, et al. (2020, July) Prediction model based on the combination of cytokines and lymphocyte subsets for prognosis of SARS-CoV-2 infection. J Clin Immunol. 13:1–10. doi: https://doi.org/10.1007/s10875-020-00821-7

  73. Shi S, Tanaka S, Ueno R et al (2020) Travel restrictions and SARS-CoV-2 transmission: an effective distance approach to estimate impact. Bull World Health Organ 98(8):518–529. https://doi.org/10.2471/BLT.20.255679

    Article  PubMed  PubMed Central  Google Scholar 

  74. Wang S, Pan Y, Wang Q, et al. (2020, August) Modeling the viral dynamics of SARS-CoV-2 infection. Math Biosci. 6;328:108438. doi: https://doi.org/10.1016/j.mbs.2020.108438

  75. Sharov KS (2020, July) Trends in adaptation of fifteen European countries population to SARS-CoV-2 in March-May 2020: Can Taiwanese experience be adopted? J Formos Med Assoc 31. doi: https://doi.org/10.1016/j.jfma.2020.07.038

  76. Sharov KS (2020, July) Adaptation of Russian population to SARS-CoV-2: asymptomatic course, comorbidities, mortality, and other respiratory virusesa reply to fear versus data. Int J Antimicrob Agents 10:106093. pii: S0924857920302697. doi: https://doi.org/10.1016/j.ijantimicag.2020.106093

  77. Boccaletti S, Ditto W, Mindlin G, Atangana A (2020) Modeling and forecasting of epidemic spreading: the case of Covid-19 and beyond. Chaos Solitons Fractals 135:109794. https://doi.org/10.1016/j.chaos.2020.109794

    Article  PubMed  PubMed Central  Google Scholar 

  78. Frederiksen LSF, Zhang Y, Foged C, Thakur A (2020) The long road toward COVID-19 herd immunity: vaccine platform technologies and mass immunization strategies. Front Immunol 11:1817. https://doi.org/10.3389/fimmu.2020.01817

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  79. Dobrovolny HM (2020) Modeling the role of asymptomatics in infection spread with application to SARS-CoV-2. PLoS ONE 15(8):e0236976. https://doi.org/10.1371/journal.pone.0236976

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  80. Sharov KS (2020) Creating and applying SIR modified compartmental model for calculation of COVID-19 lockdown efficiency. Chaos Solitons Fractals 141:110295. https://doi.org/10.1016/j.chaos.2020.110295

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

The authors would like to thank the following persons who helped them to collect statistical data on COVID-19 tests in different countries: Catharijne van de Berg (The Netherlands), Clara Søndergaard (Denmark); Alma Kjær (Denmark); Luise Strøm (Denmark); Marie Kristiansen (Norway); Sille Lassila (Finland); Matilde Da Costa (Portugal); Ines Rodrigues (Portugal); Aðalborg Ragnarsdóttir (Iceland); Agata Sturrisdóttir (Iceland); Kristina Eriksson (Sweden); Hanna Sjöberg (Sweden); Monica Luzzi (Italy); Giuseppe Franchi (Italy); Carlo Mancini (Italy); Lola Sánchez (Spain); Ana Martínez (Spain); Sofia Ramirez (Spain); Daniel von Braun (Switzerland); Alicja Kamiński (Poland); Lady Cecily Grey (UK); Poppy Moore (UK).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Konstantin S. Sharov .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2021 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Sharov, K.S., Legach, F.I. (2021). How Effective Were and Are Lockdowns?. In: Legach, F.a.E.I., Sharov, K.S. (eds) SARS-CoV-2 and Coronacrisis. Springer, Singapore. https://doi.org/10.1007/978-981-16-2605-0_6

Download citation

  • DOI: https://doi.org/10.1007/978-981-16-2605-0_6

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-16-2604-3

  • Online ISBN: 978-981-16-2605-0

  • eBook Packages: Social SciencesSocial Sciences (R0)

Publish with us

Policies and ethics