Groundbreaking predictions about COVID-19 pandemic duration, number of infected and dead: A novel mathematical approach never used in epidemiology

  1. Javier Garcia Garcia de Alcaniz
  2. Victoria Lopez-Rodas
  3. Eduardo Costas

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Abstract

Hundreds of predictions about the duration of the pandemic and the number of infected and dead have been carried out using traditional epidemiological tools (i.e. SIR, SIRD models, etc.) or new procedures of big-data analysis. However, the extraordinary complexity of the disease and the lack of knowledge about the pandemic (i.e. R value, mortality rate, etc.) create uncertainty about the accuracy of these estimates. However, several elegant mathematical approaches, based on physics and probability principles, like the Delta-t argument, Lindy's Law or the Doomsday principle-Carter's catastrophe, which have been successfully applied by scientists to unravel complex phenomena characterized by their great uncertainty (i.e. Human race's longevity; How many more humans will be born before extinction) allow predicting parameters of the Covid-19 pandemic. These models predict that the COVID-19 pandemic will hit us until at least September-October 2021, but will likely last until January-September 2022, causing a minimum of 36,000,000 infected and most likely 60,000,000, as well as 1,400,000 dead at best and most likely 2,333,000.

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    SciScore for 10.1101/2020.08.05.20168781: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board Statementnot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    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).

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    However, since Gödel’s incompleteness theorems40 demonstrating the inherent limitations of every formal axiomatic system capable of modelling basic arithmetic, it was evident that science would have certain limitations to achieve accuracy in Newtonian prediction41. The geological instant in which we are living is characterized by increasing global extinction rates, homogenization of biotas, proliferation of opportunistic species and pest-and-weed ecology, all of which favor the occurrence of unpredictable emergent novelties (reviewed by Myers and Knoll 2001 and Woodruff 2001)42,43. COVID-19 pandemic can be a very good example of an unpredictable emergent novelty, its arrival was particularly difficult to predict. For example, for the turn of the century, Oxford University Press gathered in a book what 30 of the brightest minds of the time thought the future would bring. None predicted a pandemic would devastate the world44. SARS-CoV-2 took us by surprise and we underestimated it, but once its real and true magnitude was clear hundreds of scientists started modeling to predict both duration in time and number of infected and dead. Epidemiology has an important theoretical body and, up to date, different predictive models have been used45–55. Some problems arise with these COVID-19 epidemiology models because they make complex assumptions due to lack of information about key aspects like number of cases, transmission rates, contact parameters, immunity; how they display uncerta...

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

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  2. Version published to 10.1101/2020.08.05.20168781v1 on medRxiv