Drought is a pre-existing threat to agriculture, water, and socio-economic stability of Sri Lanka. The research evaluates the spatiotemporal variability of droughts with reference to five geographically distinct areas characterized by different climates, such as China Bay, Colombo, Nuwara Eliya, Puttalam, and Kurunegala using the Standardized Precipitation Index (SPI) at several time scales (1 to 24 months) between the years 2010 and 2023. The monthly precipitation data were modeled using Gamma, Exponential, and Normal distributions. The most appropriate model for each region was identified based on the Akaike Information Criterion (AIC). Findings indicate that the regions in the Dry Zone especially China Bay and Puttalam are most affected and high by frequent severe short-term drought whereas Kurunegala has the most vulnerability to long-term drought. The paper also discovers that the choice of probability distribution is important for deriving SPI at various time scales. These results are an indication of the regional differences in the drought risk and the necessity of the customized early warning systems, agricultural plans, and strategies of water management. This multiscale, probabilistic method can provide a very solid design of enhancing drought resilience in Sri Lanka.
The Weighted Cosine Exponential Distribution (WCED) is proposed as a novel lifetime model, incorporating a trigonometric weighting mechanism into the exponential distribution to enhance flexibility and capture complex hazard behaviors, particularly the inverted bathtub shape common in reliability and survival analysis. Fundamental statistical properties are derived. A simulation study evaluates five estimation methods (MLE, MME, OLS, WLS, CVM), showing improved accuracy with larger samples. Applied to three cancer survival datasets, WCED demonstrates superior fit over existing weighted distributions, evidenced by lower information criteria (AIC, AICC, HQIC) and higher statistical significance. The model offers a parsimonious and adaptable tool for analyzing lifetime data with nonmonotonic failure patterns, proving valuable for reliability engineering and biomedical applications.
An important tool in the Genomic approach is the widely used Microarray interventions to effectively and accurately predict survival time and risk analysis of patients. This study is aimed to present and examine the risk impact of five cancer gene sequences with different high-dimensional microarray data sets, through survival analysis. GSE10300; GSE14333; GSE16446; GSE17618 and GSE20685 data sets with 16,183; 54,712; 54,739; 54,726 and 54,739 gene counts from sample sizes 44, 226, 107, 44 and 327 respectively were selected from the NCBI database to identify their risk impact. Then the classical Cox Proportional Hazard (CoxPH), Bayesian Adaptive Spline Surface (BASS) and Bayesian Additive Regression Tree (BART) models were employed to analyze each of the five data sets. Each model’s prediction performance was measured using the Root Mean Squared Error (RSME). The fol-lowing, R packages ‘survive’ for CoxPH; ‘BASS’ for BASS and ‘bartMachine’ for BART were utilized in obtaining the results. Finally, influential variable plots were shown to indicate the most important main effects and interactions for the five microarray datasets. Varying median hazard values and traces of yellow boxes across the diagonals of the heatmap diagrams indicate multi-collinearity between genes, implying misleading results by the CoxPH model, while leveraging superiority of BART model over the BASS and CoxPH models. The RSME plots successfully identify some influential genes among thou-sands of genes across the five real-life microarray datasets analyzed: CoxPH model shows a moderate level of predictive accuracy while the BASS model exhibits both strengths and weaknesses depending on the number of predictors used, significantly higher than CoxPH. The BART model, on the other hand, with the lowest RSME, consistently demonstrates its superior, stable and consistent predictive performance over CoxPH and BASS models. This study gives a more distinct and nuanced understanding of genes interactions and influence, as well as provide improved prognostic tools and personalized treatment strategies for cancer patients’ risks and survival.
Agriculture is a vital sector in Sri Lanka, yet it has been increasingly challenged by environmental disturbances and policy shifts. This study analyzes the impact of two significant disruptions—the 2017 drought and the 2021 fertilizer ban—on paddy yield trends. To address the complexities of these influences, Bayesian spline regression was employed to model non-linear and seasonal yield patterns, and interrupted time series regression was used to capture abrupt changes in response to external shocks. Uncertainty and the causal effects of these interventions were quantified through posterior distributions, estimated using the brms package and the NUTS algorithm. The results offer nuanced insights into how environmental and policy factors shape agricultural productivity, with implications for researchers and policymakers concerned with sustainable agriculture and climate adaptation. Overall, this study underscores the value of Bayesian modeling in assessing multifaceted, real-world issues that span agriculture, environment, and policy.
This study investigates the impact of major national crises on life expectancy trends in Sri Lanka from 2000 to 2023 using a Bayesian generalized additive modeling (GAM) framework combined with interrupted time series analysis. The model incorporates key socio-economic and environmental covariates—including infant mortality and GDP per capita—along with event indicators to capture the immediate and post-event effects of significant crises: the 2004 Indian Ocean tsunami, the prolonged civil conflict culminating in 2009, and the COVID-19 pandemic. Model evaluation based on the Leave-One-Out Information Criterion (LOOIC) identified a spline dimension of 8 as optimal for capturing nonlinear temporal trends. Results indicate significant immediate negative impacts on life expectancy due to the 2004 tsunami and the 2009 war. The nonlinear temporal trend, captured by the spline term, showed a strong positive effect indicating overall improvements over time. Estimated effects for infant mortality and GDP per capita were inconclusive, with wide credible intervals overlapping zero, suggesting minimal or uncertain direct associations after accounting for crises and temporal trends. Posterior predictive checks confirmed the model’s strong predictive accuracy. Forecasting for 2024 was performed using a second-order Taylor series expansion on posterior fitted draws, providing a robust and computationally efficient approach to project life expectancy beyond observed data while accounting for nonlinear trends and uncertainty. The forecast indicates a continued upward trend, though with increasing uncertainty. These findings highlight the resilience of the Sri Lankan health system and underscore the importance of targeted interventions during crises to sustain population health improvements. The proposed modeling framework provides policymakers a valuable tool for ongoing health monitoring, crisis impact assessment, and strategic planning in Sri Lanka and similar settings.
