BAYESIAN INFERENCE FOR REAL-TIME EPIDEMIC CURVE ESTIMATION IN DATA-SCARCE HEALTH SYSTEMS

In fragile health systems like Ghana’s, where over 60% of health facilities still rely on paper-based records and internet coverage in districts remains below 75%, real-time epidemic forecasting is both a challenge and a necessity. This study evaluates how Bayesian inference can generate accurate epidemic curves under conditions of incomplete, delayed, or imprecise data using a five-year (2020-2024) secondary dataset of 105 observations. The objective was to assess how Bayesian components-data input quality, prior specification, and posterior algorithms-affect real-time epidemic estimation amid data infrastructure constraints. Regression results revealed that while none of the variables individually reached statistical significance, Data Infrastructure Limitations had the strongest (β = -0.166) negative effect. Correlation analysis found only weak associations, with the highest coefficient being r = -0.113. Nonetheless, Bayesian upgrades improved forecast performance: case-record completeness rose from 60% to 84%, credible interval coverage increased by 16 points, mean absolute error in 24-hour case predictions dropped by 65%, and policy actions taken within 24 hours of alert quadrupled. These findings affirm Bayesian inference as a transformative tool for health intelligence in low-resource settings. The study recommends scaling internet and EHR infrastructure, institutionalizing Bayesian dashboards, and training analysts in probabilistic reasoning to enhance real-time outbreak management.