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

Author

Listed:
  • Jordan J Bird
  • Chloe M Barnes
  • Cristiano Premebida
  • Anikó Ekárt
  • Diego R Faria

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.

Suggested Citation

  • Jordan J Bird & Chloe M Barnes & Cristiano Premebida & Anikó Ekárt & Diego R Faria, 2020. "Country-level pandemic risk and preparedness classification based on COVID-19 data: A machine learning approach," PLOS ONE, Public Library of Science, vol. 15(10), pages 1-20, October.
  • Handle: RePEc:plo:pone00:0241332
    DOI: 10.1371/journal.pone.0241332
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    References listed on IDEAS

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    1. Zhiyuan Ma & Ping Wang & Zehui Gao & Ruobing Wang & Koroush Khalighi, 2018. "Ensemble of machine learning algorithms using the stacked generalization approach to estimate the warfarin dose," PLOS ONE, Public Library of Science, vol. 13(10), pages 1-12, October.
    2. Hortaçsu, Ali & Liu, Jiarui & Schwieg, Timothy, 2021. "Estimating the fraction of unreported infections in epidemics with a known epicenter: An application to COVID-19," Journal of Econometrics, Elsevier, vol. 220(1), pages 106-129.
    3. Ali Hortaçsu & Jiarui Liu & Timothy Schwieg, 2020. "Estimating the Fraction of Unreported Infections in Epidemics with a Known Epicenter: An Application to COVID-19," Working Papers 2020-37, Becker Friedman Institute for Research In Economics.
    4. Cleo Anastassopoulou & Lucia Russo & Athanasios Tsakris & Constantinos Siettos, 2020. "Data-based analysis, modelling and forecasting of the COVID-19 outbreak," PLOS ONE, Public Library of Science, vol. 15(3), pages 1-21, March.
    5. Fotios Petropoulos & Spyros Makridakis, 2020. "Forecasting the novel coronavirus COVID-19," PLOS ONE, Public Library of Science, vol. 15(3), pages 1-8, March.
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