Is the end near? When the different countries will surmount COVID-19 pandemic: new approach applying physical, mathematical and game theory models

  1. J. G. García de Alcañíz
  2. V. López-Rodas
  3. E. Costas

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Abstract

In the year 2020 COVID-19 pandemic was a global issue that changed mankinds lifestyle. Since then, when we will control the pandemic and recover our normal life has become the paramount question to be answered, and it needs to be solved. One problem is that there are wealthy countries, with very good health care systems and scientific resources while others barely dedicate 100 US $ per citizen per year, rich countries could cooperate at different levels with poorer ones. In such a diverse context classic epidemiology models, excellent for predicting short term evolution of the pandemic at a local level are not as suitable for long term predictions at a global scale specially if the data they use are of questionable accuracy. Alternatively, big data and AI approaches have been tried. There is an option that can be more effective. Physics applies predictive models about the duration of an event based on analysing the dynamics of the time evolution of the event itself. These models can be used alongside with probabilistic and game theory models that consider different degrees of cooperation. By means of the physics Delta- t argument and a game theory model (cooperate versus defector) we calculate when different countries may control COVID-19 pandemic. In a non-cooperate model, those countries with more resources and best manage the pandemic will have it under control between May and September 2021, whereas those with no resources will suffer the pandemic until at least October 2023. On the other hand, a strong cooperative model will allow that the majority could control the COVID-19 pandemic between October 2021 and November 2022.

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    SciScore for 10.1101/2020.12.01.20242099: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    NIH rigor criteria are not applicable to paper type.

    Table 2: Resources

    No key resources detected.

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

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  2. Version published to 10.1101/2020.12.01.20242099v1 on medRxiv