Journal Articles
1.
Jordan J. Bird; Chloe M. Barnes; Cristiano Premebida; Anikó Ekárt; Diego R. Faria
Country-level Pandemic Risk and Preparedness Classification based on COVID-19 Data: A Machine Learning Approach Journal Article
In: PLOS ONE, vol. 15, no. 10, pp. 1–20, 2020.
Abstract | Links | BibTeX | Tags: AI and Health, COVID-19, Machine Learning, Pandemic Risk Analysis
@article{Bird2020Country-levelApproach,
title = {Country-level Pandemic Risk and Preparedness Classification based on COVID-19 Data: A Machine Learning Approach},
author = {Jordan J. Bird and Chloe M. Barnes and Cristiano Premebida and Anikó Ekárt and Diego R. Faria},
doi = {10.1371/journal.pone.0241332},
year = {2020},
date = {2020-10-10},
urldate = {2020-01-01},
journal = {PLOS ONE},
volume = {15},
number = {10},
pages = {1--20},
publisher = {Public Library of Science},
abstract = {In this work we present a three-stage Machine Learning strategy to country-level risk classification based on countries that are reporting COVID-19 information. A K% binning discretisation (K = 25) is used to create four risk groups of countries based on the risk of transmission (coronavirus cases per million population), risk of mortality (coronavirus deaths per million population), and risk of inability to test (coronavirus tests per million population). The four risk groups produced by K% binning are labelled as ‘low’, ‘medium-low’, ‘medium-high’, and ‘high’. Coronavirus-related data are then removed and the attributes for prediction of the three types of risk are given as the geopolitical and demographic data describing each country. Thus, the calculation of class label is based on coronavirus data but the input attributes are country-level information regardless of coronavirus data. The three four-class classification problems are then explored and benchmarked through leave-one-country-out cross validation to find the strongest model, producing a Stack of Gradient Boosting and Decision Tree algorithms for risk of transmission, a Stack of Support Vector Machine and Extra Trees for risk of mortality, and a Gradient Boosting algorithm for the risk of inability to test. It is noted that high risk for inability to test is often coupled with low risks for transmission and mortality, therefore the risk of inability to test should be interpreted first, before consideration is given to the predicted transmission and mortality risks. Finally, the approach is applied to more recent risk levels to data from September 2020 and weaker results are noted due to the growth of international collaboration detracting useful knowledge from country-level attributes which suggests that similar machine learning approaches are more useful prior to situations later unfolding.},
keywords = {AI and Health, COVID-19, Machine Learning, Pandemic Risk Analysis},
pubstate = {published},
tppubtype = {article}
}
In this work we present a three-stage Machine Learning strategy to country-level risk classification based on countries that are reporting COVID-19 information. A K% binning discretisation (K = 25) is used to create four risk groups of countries based on the risk of transmission (coronavirus cases per million population), risk of mortality (coronavirus deaths per million population), and risk of inability to test (coronavirus tests per million population). The four risk groups produced by K% binning are labelled as ‘low’, ‘medium-low’, ‘medium-high’, and ‘high’. Coronavirus-related data are then removed and the attributes for prediction of the three types of risk are given as the geopolitical and demographic data describing each country. Thus, the calculation of class label is based on coronavirus data but the input attributes are country-level information regardless of coronavirus data. The three four-class classification problems are then explored and benchmarked through leave-one-country-out cross validation to find the strongest model, producing a Stack of Gradient Boosting and Decision Tree algorithms for risk of transmission, a Stack of Support Vector Machine and Extra Trees for risk of mortality, and a Gradient Boosting algorithm for the risk of inability to test. It is noted that high risk for inability to test is often coupled with low risks for transmission and mortality, therefore the risk of inability to test should be interpreted first, before consideration is given to the predicted transmission and mortality risks. Finally, the approach is applied to more recent risk levels to data from September 2020 and weaker results are noted due to the growth of international collaboration detracting useful knowledge from country-level attributes which suggests that similar machine learning approaches are more useful prior to situations later unfolding.
