Kaplan-Meier Type Survival Curves for COVID-19: A Health Data Based Decision-Making Tool

Area of Science:

• Epidemiology
• Biostatistics

Background:

• Global COVID-19 data collection is inconsistent, with varying definitions and reporting methods.
• Non-uniform data hinders accurate analysis of the pandemic's spread and impact across nations.

Purpose of the Study:

• To demonstrate the utility of Kaplan-Meier curves for analyzing COVID-19 dynamics using country-reported data.
• To present a robust model for characterizing epidemic evolution and inter-country differences.

Main Methods:

• Utilized cumulative data on infected, recovered, and deceased individuals from the first wave of COVID-19 (February-June).
• Developed a scheme based on Kaplan-Meier curve calculations to analyze disease dynamics.
• Compared curves from highly affected countries to identify distinguishing characteristics.

Main Results:

• Kaplan-Meier curves derived from non-uniform data provide valuable insights into disease progression.
• The model effectively illustrates differences in epidemic evolution between countries.
• Distinct patterns in Kaplan-Meier curves correlate with specific country-level disease characteristics.

Conclusions:

• Kaplan-Meier-type curves offer a practical approach to understanding and comparing epidemic dynamics.
• The developed model serves as a valuable tool for healthcare system management during public health crises.
• Standardized data analysis methods are crucial for effective global health monitoring.