COMPARATIVE STUDY OF DETERMINISTIC AND STOCHASTIC MODELS IN MALARIA TRANSMISSION DYNAMICS IN SUB-SAHARAN AFRICA

As malaria continues to burden Sub-Saharan Africa-with Ghana reporting over 10 million suspected cases between 2020 and 2022-accurate predictive modeling is essential for controlling transmission. This study compared deterministic and stochastic models to evaluate which better supports malaria control outcomes across Ghana’s ecological zones from 2020 to 2024. A total of 105 monthly observations were analyzed using secondary data from the Ghana Health Service, WHO, and IIASA, applying descriptive statistics, Pearson correlation, and multiple regression analysis. Key findings show that deterministic models exhibited high parameter sensitivity (mean = 0.78) and moderate predictive stability (mean PSI = 0.63), while stochastic models achieved superior forecast accuracy (mean = 89.7%), higher convergence robustness, and captured transition-rate fluctuations better. However, regression results revealed minimal explanatory power (R² = 0.007), and correlation coefficients with control outcomes were weak (highest r = 0.036). Despite statistical limitations, stochastic-informed interventions reduced incidence by up to 40.8%, while policy lag times dropped to as low as 2.1 days in well-integrated regions. The study concludes that while no single model type suffices, hybrid modeling-combining deterministic structure with stochastic flexibility-is crucial. It recommends expanding climate-integrated, real-time decision tools and capacity-building for model adoption in resource-variable zones.