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A quantitative framework for exploring exit strategies from the COVID-19 lockdown

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  • Fokas, A.S.
  • Cuevas-Maraver, J.
  • Kevrekidis, P.G.

Abstract

Following the highly restrictive measures adopted by many countries for combating the current pandemic, the number of individuals infected by SARS-CoV-2 and the associated number of deaths steadily decreased. This fact, together with the impossibility of maintaining the lockdown indefinitely, raises the crucial question of whether it is possible to design an exit strategy based on quantitative analysis. Guided by rigorous mathematical results, we show that this is indeed possible: we present a robust numerical algorithm which can compute the cumulative number of deaths that will occur as a result of increasing the number of contacts by a given multiple, using as input only the most reliable of all data available during the lockdown, namely the cumulative number of deaths.

Suggested Citation

  • Fokas, A.S. & Cuevas-Maraver, J. & Kevrekidis, P.G., 2020. "A quantitative framework for exploring exit strategies from the COVID-19 lockdown," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    • Handle: RePEc:eee:chsofr:v:140:y:2020:i:c:s0960077920306408
    DOI: 10.1016/j.chaos.2020.110244
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    References listed on IDEAS

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    1. Ali, Khalid K. & Cattani, Carlo & Gómez-Aguilar, J.F. & Baleanu, Dumitru & Osman, M.S., 2020. "Analytical and numerical study of the DNA dynamics arising in oscillator-chain of Peyrard-Bishop model," Chaos, Solitons & Fractals, Elsevier, vol. 139(C).
    2. Barmparis, G.D. & Tsironis, G.P., 2020. "Estimating the infection horizon of COVID-19 in eight countries with a data-driven approach," Chaos, Solitons & Fractals, Elsevier, vol. 135(C).
    3. Kaxiras, Efthimios & Neofotistos, Georgios & Angelaki, Eleni, 2020. "The first 100 days: Modeling the evolution of the COVID-19 pandemic," Chaos, Solitons & Fractals, Elsevier, vol. 138(C).
    4. Lu, D. & Osman, M.S. & Khater, M.M.A. & Attia, R.A.M. & Baleanu, D., 2020. "Analytical and numerical simulations for the kinetics of phase separation in iron (Fe–Cr–X (X=Mo,Cu)) based on ternary alloys," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 537(C).
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    Cited by:

    1. Katsikopoulos, Konstantinos V. & Şimşek, Özgür & Buckmann, Marcus & Gigerenzer, Gerd, 2022. "Transparent modeling of influenza incidence: Big data or a single data point from psychological theory?," International Journal of Forecasting, Elsevier, vol. 38(2), pages 613-619.
    2. Xinping Zhang & Yimeng Zhang & Yunchan Zhu, 2021. "COVID-19 Pandemic, Sustainability of Macroeconomy, and Choice of Monetary Policy Targets: A NK-DSGE Analysis Based on China," Sustainability, MDPI, vol. 13(6), pages 1-20, March.
    3. Gaeta, Giuseppe, 2022. "Mass vaccination in a roaring pandemic," Chaos, Solitons & Fractals, Elsevier, vol. 156(C).

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