Improving case fatality ratio estimates in ongoing pandemics through case-to-death time distribution analysis.

Area of Science:

• Epidemiology
• Biostatistics
• Public Health

Background:

• Case fatality ratio (CFR) is crucial for assessing novel pathogen severity.
• The direct CFR estimation method (deaths/cases) is simplistic and prone to bias due to time lags.
• Accurate real-time CFR estimation is vital for outbreak response.

Purpose of the Study:

• To introduce a novel distributed-delay method for more accurate CFR estimation.
• To address the limitations of existing methods in accounting for case-to-death time lags.
• To provide a robust tool for real-time CFR monitoring during outbreaks.

Main Methods:

• Developed a distributed-delay method using aggregate time-series case and death data.
• Incorporated flexible case-to-death time distributions without assuming parameter values.
• Utilized a fitting approach to forecast fatalities based on time distributions.

Main Results:

• The distributed-delay method consistently recovered true CFR earlier than the direct method in simulations.
• The method outperformed Baud's and Generalized Baud's methods.
• Empirical COVID-19 data from 34 countries supported the findings.
• A negative association was found between eventual CFR and expected case-to-death time.

Conclusions:

• The distributed-delay method offers a more accurate approach to real-time CFR estimation.
• Accounting for time lags is critical for reliable CFR during outbreaks.
• Refining this method can enhance real-time outbreak monitoring and response.