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Undersampling in action and at scale: application to the COVID-19 pandemic
Andreas Langousis1, Alin Andrei Carsteanu2
1Department of Civil Engineering, University of Patras, Patras, Greece.
Stochastic Environmental Research and Risk Assessment : Research Journal
|August 25, 2020
Summary
This study addresses statistical interpretation challenges in COVID-19 data. It proposes re-scaling reliable statistics like deaths to mitigate undersampling issues for better decision-making.
Area of Science:
- Epidemiology
- Biostatistics
Background:
- Accurate statistical interpretation of COVID-19 data is crucial for public health decision-making.
- Undersampling can significantly skew epidemic analysis and impact policy effectiveness.
Purpose of the Study:
- To analyze potential caveats in statistical interpretation of COVID-19 data.
- To propose a method for mitigating undersampling in epidemic statistics.
Main Methods:
- Statistical analysis of collected data.
- Re-scaling of reliable statistics (e.g., deaths) to adjust for undersampling.
- Comparison of epidemic properties like mortality rates between countries with similar healthcare systems.
Main Results:
- Identified significant caveats in the statistical interpretation of COVID-19 data.
- Proposed a re-scaling method to improve the reliability of epidemic statistics.
- Demonstrated potential for more accurate comparisons of mortality rates.
Conclusions:
- The proposed statistical re-scaling method offers a way to mitigate undersampling in COVID-19 data.
- Improved statistical interpretation can lead to more informed public health decisions during pandemics.
- Comparable mortality data can be achieved by considering healthcare system similarities.
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