Bayesian Inference and Sensitivity Analysis of Dengue Transmission in Sudan - El Guma, Fathelrhman (International Journal of Statistics in Medical Research, 2025)
Background: Dengue fever is a significant public health concern in Sudan as well as tropical regions. Mathematical and statistical methodologies are crucial for comprehending its transmission dynamics and informing effective control tactics. Methods: We developed a two-population compartmental model to capture dengue transmission between humans (susceptible, infected, recovered and disease- induced mortality) and mosquito vectors (susceptible and infected). Using the next-generation matrix approach, we derive an explicit expression for the basic re- production number (R0). For the assessment of critical epidemiological parameters such as the mosquito biting rate, probability of human to vector transmission, recovery rate, and dengue-induced fatality rate, Bayesian inference was employed. To evaluate the robustness of these findings, a global sensitivity analysis was performed utilizing Latin hypercube sampling and partial rank correlation coefficients. Results: Posterior estimates indicated R0 1.25 (95% credible interval: 1.11– 1.40), with the model showing strong agreement with case report data (R2 = 0.93). Sensitivity analysis showed that the mosquito biting rate as well as the transmission probability were the main drivers of epidemic potential with recovery and dengue- induced mortality exhibiting inhibiting negative effects on transmission. Conclusions: The results suggest that transmissible vector factors are an important component for dengue transmission in East Sudan. The preferred method for the control of future outbreaks is expected to concentrate on mosquito bites/human vector transmission.