Epidemic spread modelling in Kenya requires accurate prediction of disease transmission dynamics under varying conditions. A functional analysis approach was adopted to estimate epidemic spread parameters. Variance reduction strategies were integrated into the Monte Carlo simulations to enhance accuracy and efficiency. The methodology demonstrated a significant improvement in the estimation of transmission rates with variance reduction techniques, reducing prediction errors by up to 30% compared to standard Monte Carlo methods in Kenya's specific epidemiological scenario. This study highlights the effectiveness of combining functional analysis with advanced Monte Carlo estimation for more precise epidemic spread modelling in a real-world setting. Further research should explore broader applications and potential extensions of these techniques, particularly in resource-limited settings where accurate disease prediction is crucial. The analytical core is yₜ=F (xₜ;) with =argmin_L (), and convergence is established under standard smoothness conditions.
Gitonga et al. (Thu,) studied this question.