COVID-19 Prognostic Models: A Pro-con Debate for Machine Learning vs. Traditional Statistics

Area of Science:

• * Data Science and Computational Biology
• * Public Health and Epidemiology
• * Medical Informatics

Background:

• * The COVID-19 pandemic necessitated rapid innovation in healthcare, driven by multidisciplinary collaboration.
• * Data science, including accessible tools and open datasets, has become crucial for modeling viral impact.
• * Previous reviews identified two main approaches: classical statistics and machine learning.

Purpose of the Study:

• * To review and compare the strengths and weaknesses of classical statistical and machine learning methods.
• * To evaluate the application of these methods in predicting COVID-19 outcomes.
• * To inform future research directions in data-driven pandemic response.

Main Methods:

• * Systematic review of existing literature on statistical and machine learning models for COVID-19.
• * Comparative analysis of methodologies used for diagnostic, prognostic, and epidemiological modeling.
• * Discussion of the relative merits of classical statistics versus machine learning in this context.

Main Results:

• * Both classical statistics and machine learning have been applied to model COVID-19.
• * Each approach offers distinct advantages and disadvantages for predicting patient and population outcomes.
• * The choice of method depends on the specific predictive task and data availability.

Conclusions:

• * Data-driven modeling is essential for managing the COVID-19 pandemic.
• * A nuanced understanding of statistical and machine learning techniques is required for effective prediction.
• * Future research should focus on optimizing these methods for improved healthcare outcomes.