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Article

Machine Learning for Analyzing Non-Countermeasure Factors Affecting Early Spread of COVID-19

Jožef Stefan Institute, 1000 Ljubljana, Slovenia
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Academic Editor: Paul B. Tchounwou
Int. J. Environ. Res. Public Health 2021, 18(13), 6750; https://doi.org/10.3390/ijerph18136750
Received: 1 June 2021 / Revised: 20 June 2021 / Accepted: 21 June 2021 / Published: 23 June 2021
The COVID-19 pandemic affected the whole world, but not all countries were impacted equally. This opens the question of what factors can explain the initial faster spread in some countries compared to others. Many such factors are overshadowed by the effect of the countermeasures, so we studied the early phases of the infection when countermeasures had not yet taken place. We collected the most diverse dataset of potentially relevant factors and infection metrics to date for this task. Using it, we show the importance of different factors and factor categories as determined by both statistical methods and machine learning (ML) feature selection (FS) approaches. Factors related to culture (e.g., individualism, openness), development, and travel proved the most important. A more thorough factor analysis was then made using a novel rule discovery algorithm. We also show how interconnected these factors are and caution against relying on ML analysis in isolation. Importantly, we explore potential pitfalls found in the methodology of similar work and demonstrate their impact on COVID-19 data analysis. Our best models using the decision tree classifier can predict the infection class with roughly 80% accuracy. View Full-Text
Keywords: COVID-19; machine learning; feature significance; feature correlation; risk factors COVID-19; machine learning; feature significance; feature correlation; risk factors
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MDPI and ACS Style

Janko, V.; Slapničar, G.; Dovgan, E.; Reščič, N.; Kolenik, T.; Gjoreski, M.; Smerkol, M.; Gams, M.; Luštrek, M. Machine Learning for Analyzing Non-Countermeasure Factors Affecting Early Spread of COVID-19. Int. J. Environ. Res. Public Health 2021, 18, 6750. https://doi.org/10.3390/ijerph18136750

AMA Style

Janko V, Slapničar G, Dovgan E, Reščič N, Kolenik T, Gjoreski M, Smerkol M, Gams M, Luštrek M. Machine Learning for Analyzing Non-Countermeasure Factors Affecting Early Spread of COVID-19. International Journal of Environmental Research and Public Health. 2021; 18(13):6750. https://doi.org/10.3390/ijerph18136750

Chicago/Turabian Style

Janko, Vito, Gašper Slapničar, Erik Dovgan, Nina Reščič, Tine Kolenik, Martin Gjoreski, Maj Smerkol, Matjaž Gams, and Mitja Luštrek. 2021. "Machine Learning for Analyzing Non-Countermeasure Factors Affecting Early Spread of COVID-19" International Journal of Environmental Research and Public Health 18, no. 13: 6750. https://doi.org/10.3390/ijerph18136750

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