Real-time Estimation of Global CFR Ascribed to COVID-19 Confirmed Cases Applying Machine Learning Technique

  1. Monalisha Pattnaik
  2. Aryan Pattnaik

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Abstract

The COVID-19 is declared as a public health emergency of global concern by World Health Organisation (WHO) affecting a total of 201 countries across the globe during the period December 2019 to January 2021. As of January 25, 2021, it has caused a pandemic outbreak with more than 99 million confirmed cases and more than 2 million deaths worldwide. The crisp of this paper is to estimate the global risk in terms of CFR of the COVID-19 pandemic for seventy deeply affected countries. An optimal regression tree algorithm under machine learning technique is applied which identified four significant features like diabetes prevalence, total number of deaths in thousands, total number of confirmed cases in thousands, and hospital beds per 1000 out of fifteen input features. This real-time estimation will provide deep insights into the early detection of CFR for the countries under study.

CFR

as suggested by (Boldog et al., 2020, Chakraborty et al. 2019, Russell et al., 2020)

Diabetes Prevalence

proportion of a population who have diabetes in a given period of time.

Stringency Index

it provides a computable parameter to evaluate the effectiveness of the nationwide lock down in a particular country.

GDP Per Capita

it is a metric that breaks down a country’s economic output per person and is calculated by

Population Density

it is a measurement of population per unit area. It refers to the number of people living in an area per square kilometre.

HDI

it is a statistic composite index of life expectancy, education (literacy rate, gross enrolment ratio at different levels and net attendance ratio) and per capita income indicators which are used to rank countries into four tiers of human development.

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    SciScore for 10.1101/2021.12.27.21268463: (What is this?)

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

    Table 1: Rigor

    Ethicsnot detected.
    Sex as a biological variablenot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot 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: 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.

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    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a protocol registration statement.

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  2. Version published to 10.1101/2021.12.27.21268463v1 on medRxiv