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Poorly known aspects of flattening the curve of COVID-19

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  • Debecker, Alain
  • Modis, Theodore

Abstract

A negative correlation between the final ceiling of the logistic curve and its slope, established long time ago via a simulation study, motivated this closer look at flattening the curve of COVID-19. The diffusion of the virus is analyzed with S-shaped logistic-curve fits on the 25 countries most affected in which the curve was more than 95% completed at the time of the writing (mid-May 2020.) A negative correlation observed between the final number of infections and the slope of the logistic curve corroborates the result obtained long time ago via an extensive simulation study. There is both theoretical arguments and experimental evidence for the existence of such correlations. The flattening of the curve results in a retardation of the curve's midpoint, which entails an increase in the final number of infections. It is possible that more lives are lost at the end by this process. Our analysis also permits evaluation of the various governments’ interventions in terms of rapidity of response, efficiency of the actions taken (the amount of flattening achieved), and the number of days by which the curve was delayed. Not surprisingly, early decisive response—such as countrywide lockdown—proves to be the optimum strategy among the countries studied.

Suggested Citation

  • Debecker, Alain & Modis, Theodore, 2021. "Poorly known aspects of flattening the curve of COVID-19," Technological Forecasting and Social Change, Elsevier, vol. 163(C).
    • Handle: RePEc:eee:tefoso:v:163:y:2021:i:c:s0040162520312580
    DOI: 10.1016/j.techfore.2020.120432
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    References listed on IDEAS

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    1. Yuya Katafuchi & Kenichi Kurita & Shunsuke Managi, 2021. "COVID-19 with Stigma: Theory and Evidence from Mobility Data," Economics of Disasters and Climate Change, Springer, vol. 5(1), pages 71-95, April.
    2. Editorial, 2020. "Covid-19 and Climate Change," Journal, Review of Agrarian Studies, vol. 10(1), pages 5-6, January-J.
    3. Yoo, Sunbin & Managi, Shunsuke, 2020. "Global mortality benefits of COVID-19 action," Technological Forecasting and Social Change, Elsevier, vol. 160(C).
    4. Modis, Theodore, 1994. "Determination of the Uncertainties in S-Curve Logistic Fits," OSF Preprints n53pd, Center for Open Science.
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    Cited by:

    1. Modis, Theodore, 2022. "Strengths and weaknesses of the logistic function used in forecasting," OSF Preprints mrwu3, Center for Open Science.
    2. Petropoulos, Fotios & Apiletti, Daniele & Assimakopoulos, Vassilios & Babai, Mohamed Zied & Barrow, Devon K. & Ben Taieb, Souhaib & Bergmeir, Christoph & Bessa, Ricardo J. & Bijak, Jakub & Boylan, Joh, 2022. "Forecasting: theory and practice," International Journal of Forecasting, Elsevier, vol. 38(3), pages 705-871.
      • Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
    3. Miranda, L.C.M. & Devezas, Tessaleno, 2022. "On the global time evolution of the Covid-19 pandemic: Logistic modeling," Technological Forecasting and Social Change, Elsevier, vol. 175(C).
    4. Petra Pártlová & Kristína Korená & Jan Váchal, 2022. "Projecting Sustainable Systems of Economy by Means of Ecological Optimization," Energies, MDPI, vol. 15(22), pages 1-13, November.
    5. Qi Wu & Shouheng Sun, 2022. "Energy and Environmental Impact of the Promotion of Battery Electric Vehicles in the Context of Banning Gasoline Vehicle Sales," Energies, MDPI, vol. 15(22), pages 1-18, November.
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    8. Kajikawa, Yuya & Mejia, Cristian & Wu, Mengjia & Zhang, Yi, 2022. "Academic landscape of Technological Forecasting and Social Change through citation network and topic analyses," Technological Forecasting and Social Change, Elsevier, vol. 182(C).
    9. Bullini Orlandi, Ludovico & Zardini, Alessandro & Rossignoli, Cecilia & Ricciardi, Francesca, 2022. "To do or not to do? Technological and social factors affecting vaccine coverage," Technological Forecasting and Social Change, Elsevier, vol. 174(C).
    10. Salvador Ávila Filho & Júlia Spínola Ávila & Beata Mrugalska & Naiara Fonseca de Souza & Ana Paula Meira Gomes de Carvalho & Lhaís Rodrigues Gonçalves, 2021. "Decision Making in Health Management during Crisis: A Case Study Based on Epidemiological Curves of China and Italy against COVID-19," IJERPH, MDPI, vol. 18(15), pages 1-14, July.
    11. Mat Daud, Auni Aslah, 2021. "Comment on “Poorly known aspects of flattening the curve of COVID-19″," Technological Forecasting and Social Change, Elsevier, vol. 167(C).

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