ORIGINAL RESEARCH article
Front. Public Health
Sec. Infectious Diseases: Epidemiology and Prevention
Volume 13 - 2025 | doi: 10.3389/fpubh.2025.1589461
This article is part of the Research TopicSARS-CoV-2: Virology, Epidemiology, Diagnosis, Pathogenesis and Control, Volume IIView all 11 articles
COVID-19 global risk evaluation: rankings, reducing surveillance bias, and infodemic
Provisionally accepted
- 1AGH University of Science and Technology, Kraków, Poland
- 2University of Silesia in Katowice, Katowice, Silesian Voivodeship, Poland
- 3Tufts University, Medford, Massachusetts, United States
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This study examines how public health institutions estimate regional COVID-19 burdens, pursuing two primary objectives: (1) to analyze the methodologies employed for regional risk assessment, and (2) to perform spatial and Spearman rank correlation analyses of risk metrics that incorporate testing data across 101 countries. Classification methods used to assess COVID-19 risk often treat testing as a secondary, qualitative factor, overlooking its value as a quantitative input. Integrating testing data with case counts can improve the accuracy of regional infection probability estimates. Spatial analysis revealed that probabilistic metrics-such as the local probability of infection-showed stronger spatial synchronization of epidemic patterns compared to observed-to-expected case ratios. The death-to-population ratio displayed the strongest positive correlation with the observed-toexpected cases ratio. Conversely, the case fatality rate exhibited only a weak positive correlation with probabilistic metrics, though these correlations were not consistently statistically significant. The findings underscore the potential of probabilistic metrics, such as the local probability of infection, in predicting COVID-19 risk. Further research is warranted to explore the predictive capacity of probabilistic metrics concerning death-related outcomes.
Keywords: Local positivity, global positivity, Spearman correlation, ranking, Surveillance bias
Received: 07 Mar 2025; Accepted: 10 Jul 2025.
Copyright: © 2025 Michalak, Węglińska, Kulawik, Cordes, Leśniak and Lupa. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Michal Michalak, AGH University of Science and Technology, Kraków, Poland
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