Version 1
: Received: 1 June 2021 / Approved: 2 June 2021 / Online: 2 June 2021 (14:54:10 CEST)
How to cite:
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. Preprints2021, 2021060083
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. Preprints 2021, 2021060083
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. Preprints2021, 2021060083
APA Style
Janko, V., Slapničar, G., Dovgan, E., Reščič, N., Kolenik, T., Gjoreski, M., Smerkol, M., Gams, M., & Luštrek, M. (2021). Machine Learning for Analyzing Non-countermeasure Factors Affecting Early Spread of COVID-19. Preprints. https://doi.org/
Chicago/Turabian Style
Janko, V., Matjaž Gams and Mitja Luštrek. 2021 "Machine Learning for Analyzing Non-countermeasure Factors Affecting Early Spread of COVID-19" Preprints. https://doi.org/
Abstract
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 have 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.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
