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Author Spotlight: Advancements in Multiplex Detection of Respiratory Viruses
Published on: November 10, 2023
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Using hierarchical clustering analysis to evaluate COVID-19 pandemic preparedness and performance in 180 countries in
Banafsheh Sadeghi1, Rex C Y Cheung2, Meagan Hanbury1
1Health Bridge Analytics, Davis, California, USA.
BMJ Open
|November 10, 2021
Summary
Machine learning analysis of 2020 COVID-19 data revealed significant differences in country preparedness. Fatality rates, not just case numbers, highlight disparities in healthcare system resilience and resource allocation globally.
Area of Science:
- Epidemiology
- Machine Learning
- Public Health
Background:
- The COVID-19 pandemic highlighted global disparities in healthcare system preparedness and performance.
- Existing models for pandemic vulnerability prediction and standard epidemiological scoring techniques have limitations in forecasting country-level preparedness.
Purpose of the Study:
- To rank and score 180 countries based on COVID-19 cases and fatality data from 2020.
- To compare these rankings against existing pandemic vulnerability prediction models and standard epidemiological scoring methods.
Main Methods:
- Utilized retrospective daily COVID-19 data from 2020, divided into 24 half-month periods.
- Applied unsupervised machine learning, specifically hierarchical clustering, to group countries based on cumulative COVID-19 fatality per day and cases per million population per day.
- Calculated final country scores by averaging period scores for both cases and fatality.
Main Results:
- Ranked 180 countries by COVID-19 cases and fatality in 2020. Some countries, like the UAE and USA, had high case rates but better fatality scores (A or B), while Belgium and Sweden received 'F' in both. African nations, though not ranking 'F' for cases, showed 'F' scores for fatality.
- Developing countries were disproportionately represented in lower fatality rankings (D and F) compared to case rankings.
- Standard epidemiological measures showed moderate correlation, but prior prediction models failed to accurately forecast country preparedness.
Conclusions:
- COVID-19 fatality serves as a robust indicator of a country's resources and healthcare system resilience during a pandemic.
- The complex interplay of economic and sociopolitical factors influencing pandemic management requires advanced analytical approaches.
- Integrating computer science and machine learning methods can significantly enhance epidemiological modeling for complex global health challenges.
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