Analysis of feature influence on Covid-19 Death Rate Per Country Using a Novel Orthogonalization Technique

  1. Gaston Gonnet
  2. John Stewart
  3. Joseph Lafleur
  4. Stephen Keith
  5. Mark McLellan
  6. David Jiang-Gorsline
  7. Tim Snider

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Abstract

We have developed a new technique of Feature Importance, a topic of machine learning, to analyze the possible causes of the Covid-19 pandemic based on country data. This new approach works well even when there are many more features than countries and is not affected by high correlation of features. It is inspired by the Gram-Schmidt orthogonalization procedure from linear algebra. We study the number of deaths, which is more reliable than the number of cases at the onset of the pandemic, during Apr/May 2020. This is while countries started taking measures, so more light will be shed on the root causes of the pandemic rather than on its handling.

The analysis is done against a comprehensive list of roughly 3,200 features. We find that globalization is the main contributing cause, followed by calcium intake, economic factors, environmental factors, preventative measures, and others. This analysis was done for 20 different dates and shows that some factors, like calcium, phase in or out over time. We also compute row explainability, i.e. for every country, how much each feature explains the death rate. Finally we also study a series of conditions, e.g. comorbidities, immunization, etc. which have been proposed to explain the pandemic and place them in their proper context. While there are many caveats to this analysis, we believe it sheds light on the possible causes of the Covid-19 pandemic.

One-Sentence Summary

We use a novel feature importance technique to find that globalization, followed by calcium intake, economic factors, environmental factors, and some aspects of societal quality are the main country-level data that explain early Covid-19 death rates.

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  1. Version published to 10.1101/2021.07.02.21259929v3 on medRxiv
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    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.

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

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  3. Version published to 10.1101/2021.07.02.21259929v2 on medRxiv
  4. Version published to 10.1101/2021.07.02.21259929v1 on medRxiv