TheDispensersforSafeWaterprogramunderEvidenceActionpromotespoint of collection water treatment through the installation of chlorine dispenser gadgets in rural parts of Kenya. Although the initiative has improved access to safe drinking water, monitoring household adoption remained a challenge during the COVID-19 pandemic, which limited field-based data collection and led to increased dependence on phone surveys. In addition, technology adoption data are often imbalanced, which poses difficulties for traditional classification methods. This study aimed to develop and implement a stacking ensemble classifier to model the adoption of chlorine dispensers among households in western Kenya. Data were collected from 27,457 households. The analysis used structured household, promoter and spot check survey data. The key variables included chlorine availability, user knowledge, household demographics, and engagement with promoters. RF, ANN, and NB models were trained and evaluated individually, then combined using a stacked ensemble approach. The ensemble model outperformed all base learners, achieving the highest accuracy (69.1%) and AUC (0.6959). The variable importance analysis revealed that the presence of chlorine and the knowledge of the user were the strongest predictors of adoption. In conclusion, ensemble learning provides a reliable method for modeling behavioral adoption in public health interventions. The findings offer practical insights for programs and demonstrate the potential of machine learning in improving, targeting and monitoring of safe water initiatives in low-resource settings.

Kenya has in recent years experienced a surge in extreme rainfall events, which continue to pose serious risks to human life, infrastructure, and the wider economy. This study aimed to develop a non-stationary Generalized Pareto Distribution(GPD). Its performance was compared with the classical stationary Peaks-Over-Threshold (POT) model to improve the modeling and prediction of extreme rainfall events. Accurately modeling these extremes is therefore essential to support sound risk assessment and informed decision-making. An Extreme Value Theory(EVT)–based threshold exceedance framework was used to model rainfall extremes, contrasting stationary and non-stationary Generalized Pareto formulations. Under the non-stationary framework, key environmental covariates were incorporated. These included Sea Surface Temperature (SST), Atmospheric Moisture Content (AMC), and Impervious Surface Percentage (ISP). These covariates account for ocean-driven climate variability, atmospheric moisture availability, and land-surface modifications influencing local atmospheric moisture, respectively. The findings indicate that the most suitable model was one in which the shape parameter varied with SST and the scale parameter with AMC. This model yielded lower information criterion values, reflecting a superior fit. This preference was further supported by a likelihood-ratio test, which indicated significance at p < 0.05. The model enabled the estimation of return levels and the assessment of rainfall-related risks in Kenya. By accounting for climatic and land-surface drivers, the study offers a stronger basis for risk assessment and delivers guidance for urban planning, policymaking, and strategies for disaster preparedness.

This paper seeks to use the Moving Block Bootstrap method to determine the confidence intervals for a change point in conditional variance function of data exhibiting conditional heteroscedasticity and heterogeneity. Confidence intervals for a change point normally provide or give a range within which the true change point location is likely to lie. This is usually based on a specified confidence level. This helps to in turn determine whether the change point is statistically significant especially after determining the critical values for the distribution of the change point test statistic. Confidence intervals are also called interval estimates as opposed to point estimates which provide a single estimate for a parameter.

Wealth inequality remains a significant challenge in Kenya, exacerbated by the limitations of traditional wealth measurement methods. This study develops and evaluates an ensemble wealth index model combining Random Forest (RF) and Multilayer Perceptron (MLP) algorithms to improve prediction accuracy using socio-economic data from the 2019 Kenya Population and Housing Census (KPHC). The models were assessed using performance metrics such as accuracy, precision, sensitivity, specificity and ROC-AUC. The Random Forest model, configured with 500 trees and a split variable count of 2, achieved 34.3% accuracy, 58.54% balanced accuracy, 65.98% out-of-bag error and performed best on the ”Poorest” class with a 13.7% class error. It further recorded 41.13% precision, 34.27% recall, 83.17% specificity, and a 67.31% AUC. The MLP model, using sigmoid activation in hidden layers and softmax in the output layer, achieved 33.4% accuracy, 57.7% balanced accuracy, 30.6% precision, 32.54% recall, 82.86% specificity and AUC of 69.12%. The RF-MLP ensemble model outperformed the individual models with a 34.4% accuracy, 37.42% precision, 34.05% recall, 83.2% specificity and AUC of 68.55%. Despite modest overall accuracies, the ensemble model showed enhanced balanced accuracy and specificity, particularly in extreme wealth categories. However, classification of middle and poor wealth levels remains challenging due to feature overlap and class imbalance.

The spotted stem borer ( Chilo partellus ) is one of the most destructive pests of maize in sub‐Saharan Africa, causing substantial yield losses and threatening food security among smallholder farming communities. This study investigated the long‐term relationships between climatic variability and C. partellus incidence across contrasting organic and conventional farming systems in two agroecological zones. Biweekly monitoring observations of C. partellus incidence were summarized quarterly for temporal analyses across four long‐term farming systems at Chuka and Thika: conventional high‐, organic high‐, conventional low‐, and organic low‐input. The resulting analytical series represented 32 and 28 quarterly positions at Chuka and Thika, respectively. Climatic variables (temperature, rainfall, and relative humidity) were obtained from on‐site weather stations. Spearman’s rank correlation, cross‐correlation, ARIMA/SARIMA time‐series models with exogenous climatic predictors (ARIMAX/SARIMAX), and Bayesian generalized linear mixed models were used to evaluate climatic associations and farming systems effects. Temperature emerged as the climatic variable most consistently associated with C. partellus incidence, showing significant negative relationships across all farming systems at Chuka and within the organic farming systems at Thika, whereas rainfall and relative humidity exhibited comparatively weak associations. Cross‐correlation analyses indicated predominantly contemporaneous and short lag associations (0–2 quarters) between climatic variables and pest incidence. Time‐series models were evaluated using descriptive measures of in‐sample agreement between observed and fitted values. Across farming systems, R ² values ranged from 0.039 to 0.650, indicating substantial variation in the degree to which the fitted models represented the observed temporal patterns. These measures describe in‐sample model fit and should not be interpreted as evidence of out‐of‐sample predictive or forecasting performance. Bayesian analyses further indicated that C. partellus incidence was higher in the organic than in the conventional high‐input systems after accounting for climatic variability, whereas the 95% credible intervals for farming system effects in the low‐input systems overlapped zero. Overall, these findings suggest that climatic variability and farming system management are associated with long‐term variation in C. partellus incidence. The findings may inform the development of climate‐informed pest‐monitoring and integrated pest management strategies, while independently validated models would be required before application for pest forecasting under sustainable maize production in East Africa.