APPLICATION OF TIME SERIES FORECASTING MODELS IN PREDICTING HOSPITAL RESOURCE NEEDS DURING DISEASE OUTBREAKS

As outbreaks increasingly overwhelm fragile health systems, especially in resource-constrained nations, the ability to forecast hospital resource demand has become a critical public health priority. In Ghana, COVID-19 waves exposed severe mismatches between hospital needs and available resources-peaking ICU occupancy at 98% and oxygen deficits exceeding 12,000 liters daily. This study assessed the effectiveness of time series forecasting models-ARIMA, Prophet, and Exponential Smoothing-in predicting hospital resource needs across 105 monthly observations from 2020 to 2024. Using regression and correlation analyses, results revealed that Model Performance Metrics significantly influenced hospital resource demand (β = -0.435; p = 0.080), while overall model variance explained was 6.5% (R² = 0.065), with the highest correlation being r = -0.185. Descriptive analysis also showed a 35% improvement in forecast accuracy (MAE reduced from 12.0 to 7.8 beds) and a 45% decline in oxygen demand due to enhanced forecasting. These findings confirm the pivotal role of predictive modeling in outbreak preparedness. The study recommends institutionalizing hybrid forecasting algorithms, integrating fiscal constraints into modeling logic, and reinforcing data infrastructure to translate predictions into timely, life-saving resource deployments.