Country-level pandemic risk and preparedness classification based on COVID-19 data: A machine learning approach


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.

Publication DOI:
Divisions: College of Engineering & Physical Sciences
College of Engineering & Physical Sciences > Aston Institute of Urban Technology and the Environment (ASTUTE)
College of Engineering & Physical Sciences > Systems analytics research institute (SARI)
Additional Information: © 2020 Bird et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Uncontrolled Keywords: Research Article,Medicine and health sciences,Computer and information sciences,Physical sciences,Research and analysis methods,Biology and life sciences,Earth sciences,Social sciences,Pandemics,Clinical Laboratory Techniques,Humans,Support Vector Machine,Machine Learning,Classification,Disaster Planning,Betacoronavirus,Risk Assessment/methods,Models, Theoretical,Global Health,International Cooperation,Coronavirus Infections/diagnosis,Reagent Kits, Diagnostic/supply & distribution,Forecasting,Algorithms,Decision Trees,Pneumonia, Viral/diagnosis,Agricultural and Biological Sciences(all),General,Biochemistry, Genetics and Molecular Biology(all)
Publication ISSN: 1932-6203
Last Modified: 14 May 2024 07:18
Date Deposited: 02 Nov 2020 09:16
Full Text Link:
Related URLs: https://journal ... al.pone.0241332 (Publisher URL)
http://www.scop ... tnerID=8YFLogxK (Scopus URL)
PURE Output Type: Article
Published Date: 2020-10
Published Online Date: 2020-10-28
Accepted Date: 2020-10-13
Submitted Date: 2020-05-22
Authors: Bird, Jordan J. (ORCID Profile 0000-0002-9858-1231)
Barnes, Chloe M. (ORCID Profile 0000-0002-6782-1773)
Premebida, Cristiano
Ekárt, Anikó (ORCID Profile 0000-0001-6967-5397)
Faria, Diego R. (ORCID Profile 0000-0002-2771-1713)



Version: Published Version

License: Creative Commons Attribution

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