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An R-Based Landscape Validation of a Competing Risk Model
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Multidimensional Machine Learning Model to Calculate a COVID-19 Vulnerability Index.

Paula Andrea Rosero Perez1, Juan Sebastián Realpe Gonzalez1, Ricardo Salazar-Cabrera1

  • 1Research Group in Telematics Engineering, Telematics Department, Universidad del Cauca, Popayán 190002, Colombia.

Journal of Personalized Medicine
|July 29, 2023
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Summary
This summary is machine-generated.

A new multidimensional index improves COVID-19 risk assessment in Colombia by incorporating environmental and mobility data. This enhanced model, using machine learning, offers better predictions for public health strategies and other viral diseases.

Keywords:
COVID-19datasetmachine learningvulnerability index

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Area of Science:

  • Public Health
  • Data Science
  • Epidemiology

Background:

  • Colombia confirmed its first COVID-19 case on March 6, 2020, and by March 13, 2023, had over 6.3 million cases.
  • Existing COVID-19 vulnerability indices in Colombia, like DANE's, overlooked crucial environmental and mobility factors.
  • Previous assessments did not fully capture the complex risk landscape influencing COVID-19 spread.

Purpose of the Study:

  • To develop a multidimensional COVID-19 vulnerability index incorporating diverse data types.
  • To compare the predictive accuracy of this new index against existing models.
  • To enhance decision-making for public health interventions in Colombia.

Main Methods:

  • Utilized the Cross-Industry Standard Process for Data Mining (CRISP-DM) methodology for data handling and modeling.
  • Integrated variables including unemployment, GDP, mobility, vaccination data, and climate information.
  • Employed machine learning models, including Extra Trees Regressor, to predict COVID-19 incidence.

Main Results:

  • The developed multidimensional index demonstrated superior performance in predicting COVID-19 incidence.
  • The Extra Trees Regressor algorithm achieved an R-squared value of 0.829, indicating high predictive accuracy.
  • The study identified key factors contributing to COVID-19 vulnerability beyond demographics and health status.

Conclusions:

  • The multidimensional index provides a more comprehensive understanding of COVID-19 risk factors.
  • This approach can significantly support public health decision-making and resource allocation.
  • The methodology is adaptable for assessing risks associated with other infectious diseases, such as dengue.