Juma Kasozi

Trends offer direction and momentum. However, trends in mortality are affected by trend breaks, which are a consequence of mortality shocks. Additionally, insufficient historical data challenge the credibility of the forecasted trends, which are useful for actuaries in pricing, reserving, and valuing life insurance products. To address these challenges, the study aims to determine and incorporate trend breaks among individual causes of death and coherently forecast them by applying the bottom-up hierarchical forecasting approach for life insurance models. The models used are categorized as base (linear model), auto-statistical (Arima, Exponential-Smoothing, and Prophet), and auto-machine learning. The data from the World Health Organization consisted of annualized mortality quantities by cause, gender, age, and period for Kenya. Results based on the mean absolute percentage error criteria across the causes of death showed that all the models apart from the base model showed significant improvement after accounting for the trend breaks with the best being the auto machine learning approach leading with seven causes of death. Updating forecasts based on the computed trend breakpoints that varied between 2007 to 2011 generally improved forecast accuracy. These results suggest that forecasting errors may be reduced after accounting for trend breaks and model specifications. Furthermore, this implies that insufficient data do not necessarily produce deficient forecasts. The study’s contribution involved applying approaches that enhance the accuracy of forecasting models to prevent adverse effects of mortality shocks in actuarial modeling.

In this paper, we are interested in finding an optimal control support design for controlling vibrations due to pedestrian-bridge interactions. Therefore, we derive the topological derivatives of the proposed functionals using the averaged adjoint approach. A numerical algorithm initialized by these sensitivities is used as a solution strategy. The algorithm is tested numerically for two different cases of initial conditions.

This study uses the Hamiltonian Monte Carlo (HMC) and Gibbs methods to predict of Hypertension, Tobacco use, Overweight and Obesity. The methods were applied on data from World Health Organization report 2015. Corresponding stochastic matrices were generated and utilized in the prediction and comparison of the two sampling methods. Using Python and the NumPy library, the state vectors for these conditions were predicted, with the results showing slight variations between the two methods. In the long term, both methods converged to stable probability distributions, indicating steady-state values for each condition. HMC predicted long-term prevalence of 17.48% for hypertension, 17.5% for tobacco use, 17.56% of Overweight, 14.36% of Obesity and 33.1% for "Other subjects, while Gibbs predicted 17.82%, 26.27%, 17.13%, 4.80% and 33.98%, respectively. Additionally, Gibbs was found to outperform HMC in terms of error metrics, with lower values for Mean Squared Error (MSE), Mean Absolute Error (MAE), indicating better prediction accuracy. This analysis aids practitioners in selecting the most suitable method based on specific problem characteristics and dataset complexities.

Universal Health Coverage (UHC) is a global objective aimed at providing equitable access to essential and cost-effective healthcare services, irrespective of individuals’ financial circumstances. Despite efforts to promote UHC through health insurance programs, the uptake in Kenya remains low. This study aimed to explore the factors influencing health insurance uptake and offer insights for effective policy development and outreach programs. The study utilized machine learning techniques on data from the 2021 FinAccess Survey. Among the models examined, the Random Forest model demonstrated the highest performance with notable metrics, including a high Kappa score of 0.9273, Recall score of 0.9640, F1 score of 0.9636, and Accuracy of 0.9636. The study identified several crucial predictors of health insurance uptake, ranked in ascending order of importance by the optimal model, including poverty vulnerability, social security usage, income, education, and marital status. The results suggest that affordability is a significant barrier to health insurance uptake. The study highlights the need to address affordability challenges and implement targeted interventions to improve health insurance uptake in Kenya, thereby advancing progress towards achieving Universal Health Coverage (UHC) and ensuring universal access to quality healthcare services.