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A predictive model and country risk assessment for COVID-19: An application of the Limited Failure Population concept

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  • Koutsellis, Themistoklis
  • Nikas, Alexandros

Abstract

This article provides predictions for the spread of the SARS-CoV-2 virus for a number of European countries and the United States of America, drawing from their different profiles, both socioeconomically and in terms of outbreak and response to the 2019–2020 coronavirus pandemic, from an engineering and data science perspective. Each country is separately analysed, due to their differences in populations density, cultural habits, health care systems, protective measures, etc. The probabilistic analysis is based on actual data, as provided by the World Health Organization (WHO), as of May 1, 2020. The deployed predictive model provides analytical expressions for the cumulative density function of COVID-19 curve and estimations of the proportion of infected subpopulation for each country. The latter is used to define a Risk Index, towards assessing the level of risk for a country to exhibit high rates of COVID-19 cases after a given interval of observation and given the plans of lifting lockdown measures.

Suggested Citation

  • Koutsellis, Themistoklis & Nikas, Alexandros, 2020. "A predictive model and country risk assessment for COVID-19: An application of the Limited Failure Population concept," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    • Handle: RePEc:eee:chsofr:v:140:y:2020:i:c:s0960077920306366
    DOI: 10.1016/j.chaos.2020.110240
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    1. Martelloni, Gabriele & Martelloni, Gianluca, 2020. "Modelling the downhill of the Sars-Cov-2 in Italy and a universal forecast of the epidemic in the world," Chaos, Solitons & Fractals, Elsevier, vol. 139(C).
    2. Christopher A. Sims, 1986. "Are forecasting models usable for policy analysis?," Quarterly Review, Federal Reserve Bank of Minneapolis, vol. 10(Win), pages 2-16.
    3. Ndaïrou, Faïçal & Area, Iván & Nieto, Juan J. & Torres, Delfim F.M., 2020. "Mathematical modeling of COVID-19 transmission dynamics with a case study of Wuhan," Chaos, Solitons & Fractals, Elsevier, vol. 135(C).
    4. Dagum, P. & Galper, A. & Horvitz, E. & Seiver, A., 1995. "Uncertain reasoning and forecasting," International Journal of Forecasting, Elsevier, vol. 11(1), pages 73-87, March.
    5. Fanelli, Duccio & Piazza, Francesco, 2020. "Analysis and forecast of COVID-19 spreading in China, Italy and France," Chaos, Solitons & Fractals, Elsevier, vol. 134(C).
    6. Santhosh, T.V. & Gopika, V. & Ghosh, A.K. & Fernandes, B.G., 2018. "An approach for reliability prediction of instrumentation & control cables by artificial neural networks and Weibull theory for probabilistic safety assessment of NPPs," Reliability Engineering and System Safety, Elsevier, vol. 170(C), pages 31-44.
    7. 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).
    8. Jean-Paul Chretien & Dylan George & Jeffrey Shaman & Rohit A Chitale & F Ellis McKenzie, 2014. "Influenza Forecasting in Human Populations: A Scoping Review," PLOS ONE, Public Library of Science, vol. 9(4), pages 1-8, April.
    9. Doukas, Haris & Nikas, Alexandros, 2020. "Decision support models in climate policy," European Journal of Operational Research, Elsevier, vol. 280(1), pages 1-24.
    10. Chakraborty, Tanujit & Ghosh, Indrajit, 2020. "Real-time forecasts and risk assessment of novel coronavirus (COVID-19) cases: A data-driven analysis," Chaos, Solitons & Fractals, Elsevier, vol. 135(C).
    11. Zhang, Xiaolei & Ma, Renjun & Wang, Lin, 2020. "Predicting turning point, duration and attack rate of COVID-19 outbreaks in major Western countries," Chaos, Solitons & Fractals, Elsevier, vol. 135(C).
    12. Haris Doukas & Alexandros Nikas & Mikel González-Eguino & Iñaki Arto & Annela Anger-Kraavi, 2018. "From Integrated to Integrative: Delivering on the Paris Agreement," Sustainability, MDPI, vol. 10(7), pages 1-10, July.
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