Early and accurate estimation of the case fatality rate (CFR) during emerging infectious disease outbreaks remains a critical challenge in epidemiological surveillance. Traditional CFR estimation methods often exhibit substantial biases due to reporting delays, testing policies, and censored outcomes. We present a novel probabilistic framework for CFR estimation using population-level data that addresses these methodological challenges. Our approach introduces an Expected Case Resolution (ECR) estimator that leverages the temporal dynamics of case resolution through a parametric survival analysis framework. The method employs minimal parameters while providing bounded confidence intervals for CFR estimates, requiring only aggregate daily case and mortality counts rather than individual patient-level data. We validate our methodology using data from 185 countries during the SARS-CoV-2 pandemic, demonstrating improved early-stage estimation accuracy compared to conventional approaches. The framework proves particularly valuable for jurisdictions lacking comprehensive patient-tracking infrastructure, offering a robust alternative to existing methods that rely on detailed individual case histories. Our results indicate that the ECR estimator achieves superior convergence to true CFR values during the critical early phases of outbreak progression, while maintaining computational efficiency through a streamlined parameter estimation process. This methodology provides public health authorities with a more reliable tool for early outbreak severity assessment, facilitating evidence-based intervention strategies.
Keywords: case fatality rate, epidemiological modeling, COVID-19, survival analysis, disease surveillance, statistical estimation
Early_Estimation_of_the_Case_Fatality_Rate.pdf