Understanding evolution of COVID-19 driven mortality rate

  1. Ishika Bhaumik
  2. Suman Sinha-Ray
  3. Anshul Chaudhary
  4. Abhishek Srivastava
  5. Prashant Kodgire

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Abstract

Objective

COVID-19 has resulted in the death of almost 4 million people till date 1 . However, the mortality rate across countries seems to be vastly different irrespective of their respective socio-economic backgrounds. It is well known now that COVID-19 is an acute inflammatory infectious disease that gets complicated by type-I interferon response 2,3 . However, the precise reason for variations in COVID-19 related mortality rates is unknown. A detailed understanding behind the evolution of mortality rate around the globe is needed.

Methods

In this article, we show that a biological science guided machine learning-based approach can predict the evolution of mortality rates across countries. We collected the publicly available data of all the countries in the world with regard to the mortality rate and the relevant biological and socio-economical causes. The data was analyzed using a novel FFT driven machine learning algorithm.

Results

Our results demonstrate how COVID-19 related mortality rate is closely dependent on a multitude of socio-economic factors (population density, GDP per capita, global health index and population above 65 years of age), environmental (PM2.5 air pollution) and lifestyle aka food habits (meat consumption per capita, alcohol consumption per capita, dairy product consumption per capita and sugar consumption per capita). Interestingly, we found that individually these parameters show no visible trend that can be generalized with mortality.

Conclusions

We anticipate that our work will initiate conversations between health officials, policymakers and world leaders towards providing preventative measures against COVID-19 and future coronavirus-based diseases and endemics/ pandemics by taking a holistic view.

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  1. ScreenIT

    SciScore for 10.1101/2022.01.16.22269210: (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).

    Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

    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.

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    • 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.

    Results from scite Reference Check: We found no unreliable references.

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  2. Version published to 10.1101/2022.01.16.22269210v1 on medRxiv