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Recent Submissions

Comparative Analysis Of Predictive Performance Of Holt-Winters And Facebook Prophet On Kenyan Covid-19 Data
(American Journal of Applied Statistics and Economics, 2026-02-05) Langat Erick; Reuben Cheruiyot Lang’at; Joseph Cheruiyot
Comparative time-series forecasting is essential for identifying and predicting the trajectories of infections such as COVID-19. This study offers a thorough comparison of two time series forecasting methodologies: The Holt-Winters (HW) exponential smoothing technique and Meta’s (Facebook’s) Prophet model, as applied to COVID-19 case data. We assessed each model’s capacity to capture trend dynamics, seasonal variations, and sudden structural shifts linked to pandemic waves and policy measures, using publicly accessible epidemiological time-series data. The Holt-Winters model, which focuses on level, trend, and seasonality components, offers a clear foundation for short-term forecasting but has shortcomings when faced with irregular shocks and non-linear patterns. Conversely, Prophet’s decomposable additive structure, which includes automatic changepoint detection and variable seasonality, exhibits superior flexibility to sudden changes in the transmission patterns. Forecast accuracy was evaluated using conventional error metrics (Root Mean Square Error; RMSE, Mean Absolute Error; MAE, R-Squared; R2), indicating that Holt-Winters (HW) typically surpassed Facebook Prophet in both daily and cumulative confirmed cases of COVID-19. The comparative analysis emphasizes the significance of model selection according to the epidemic environment and illustrates the advantages of conventional time-series techniques for reliable public health forecasting. This study provides methodological insights for academics and decision-makers in pursuit of efficient methods for monitoring and forecasting the dynamics of infectious diseases.
Promoting Agriculture through Data Analytics: Pathways to Strengthen Food Security.
(American Journal of Applied Statistics and Economics (AJASE), 2026) Reuben Cheruiyot Lang’at
Globally, agriculture faces escalating pressures from climate change, population growth, de-clining soil fertility, and market volatility. As food insecurity intensifies, especially in Sub-Sa-haran Africa, data analytics has emerged as a transformative tool for improving agricultural decision-making, productivity, and resilience. This paper examines the role of data analytics in enhancing crop forecasting, optimizing resource use, improving extension services, and designing evidence-based policies to ensure sustainable food systems. Drawing from empir-ical studies and international development reports, the paper argues that data-driven agricul-ture provides an effective pathway for addressing chronic food insecurity while supporting national development strategies. This study presents four illustrative, reproducible analyses using built-in R datasets that highlight common analytical approaches such as descriptive statistics, regression analysis, and analysis of variance, and how their outputs can inform agricultural decisions. The paper concludes with policy recommendations for integrating an-alytics capacity into agricultural institutions, particularly in low and middle-income countries striving to modernize their food systems.
Nonparametric Density Estimation in Survey Sampling
(Journal of Mathematics and Statistics Studies, 2026-02-16) Reuben Cheruiyot Lang’at
Nonparametric methods for estimating probability densities are popular because they provide flexible tools for exploratory analysis, model checking, and inference when little is known about the underlying distributional form. In the context of sample surveys where data arise from complex designs involving stratification, clustering, and unequal inclusion probabilities, naive application of standard nonparametric estimators can, however, produce biased and inconsistent results. This paper reviews foundations of nonparametric density estimation and use of kernel and local polynomial methods and discusses their adaptation to design-based and model-based survey frameworks. Practical implementation issues involving bandwidth selection, boundary correction, and computational considerations are made. Throughout, emphasis is placed on methods that respect survey design information, and on trade-offs between design-based validity and model-based efficiency. The paper concludes with recommendations for practice and directions for future research.
Determinants of Covid-19 vaccine uptake among the elderly aged 58 years and above in Kericho County, Kenya: Institution based cross sectional survey
(PLOS Global Public Health, 2023-09) Calvince Otieno Anino; Immaculate Wandera; Zachary Ondicho Masimba; Collins Kipkosgei Kirui; Carjetine Syallow Makero; Phanice Kerubo Omari; Philip Sanga
Hesitancy to Covid-19 vaccine is a global challenge despite the compelling evidence of the value of vaccine in preventing disease and saving lives. It is suggested that context-specific strategies can enhance acceptability and decrease hesitancy to Covid-19 vaccine. Hence, the study determined uptake and determinants of Covid-19 vaccine following a sustained voluntary vaccination drive by Kenyan government. We conducted institution based crosssectional survey of 1244 elderly persons aged 58 to 98 years in the months of January, February and March, 2022. A multinomial logistic regression analysis was used to investigate determinants of Covid 19 vaccine uptake. The predictor variables included socioeconomic and demographic characteristics, convenience and ease of access of the vaccine, collective responsibility, complacency and the three dimensions of confidence; trust in safety, trust in decision makers and delivery system. The findings are reported as the adjusted odd ratio (AOR) at 95% confidence interval (CI). Significant level was considered at p <0.05. The results from the multinomial logistic regression analysis indicated that advanced age and presence of chronic disease were associated with increased odds of doubt on Covid 19 vaccine, while long distance from vaccination centers was associated with increased odds of delay in vaccination. Overall, the findings of this study provided valuable insights into the factors influencing vaccine hesitancy among the elderly population in Kenya and will inform the development of targeted interventions to increase vaccine acceptance and uptake in this population.
Exploring Contemporary Issues of Adolescent Pregnancies in Kenya: Further Analysis Of 2014 And 2020 Kenya Demographic and Health Survey Datasets Files
(International Journal of Multidisciplinary Research & Innovation, 2024) Calvince Anino; Joel Wanzala; Fredrik Wanyama; Collins Kirui
Background Adolescent pregnancy is a pressing issue with significant social and health consequences for both mothers and children, particularly in developing countries with limited access to quality healthcare. Despite policy and program interventions, adolescent pregnancies continue to have adverse outcomes. The examined trends and contemporary issues related to adolescent pregnancies in Kenya using data from the 2014 and 2020 Kenya Demographic and Health Survey datasets files. Methods Kenya Demographic and Health Survey datasets for 2014 and 2020 were used. Descriptive statistics were used to analyze trends in adolescent pregnancies, while binary logistic regression analysis was used to identify factors associated with adolescent pregnancies. Results The study found a significant decrease in the prevalence of adolescent pregnancies from 18.8% in 2014 to 12.2% in 2020 (p 0.001), with a higher prevalence in rural areas. Over 70% of the pregnancies were unintended, and sexual initiation before the age of 15 was associated with a higher risk of pregnancy. Wealth status, education, access to healthcare, and household size were significantly associated with adolescent pregnancy. Conclusion The study observed a concerning rise in adolescent pregnancies, primarily affecting girls aged 15-19. This was driven by the interplay of socio-demographic, economic, and cultural factors, which greatly impacted rural and disadvantaged communities.