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Recent Submissions

Rethinking the Human Resource (HR) Strategy in the Face of Systematic Failures in the Devolved Health Sector in Kenya
(International Journal of Advances in Management Research, 2022) Lopar, Samuel K.; Murimi,Michael; Kirande, Jairus; Alice Kombo,Alice; Kipkorir, Bett
This viewpoint paper is about rethinking the human resources (HR) strategy in the face of systematic failures in the devolved health sector in Kenya. The paper gives a background introduction of the health sector of Kenya as defined and established by the constitution of Kenya, explains the sharing of functions devolved in the health sector, and explains the history of devolution of the health sector. Under the identification and justification of the study, the paper highlights how specialized skills in health service provision are concentrated in urban centers and emphasizes a lack of inter-county transfer of services. The paper further explains the distribution of healthcare service provision, the current management of HR, and the statement of specific problems in Kenya; such problems include outcry from healthcare providers, which is manifested by frequent strikes across the country over issues to do with salaries, promotions, and career development. The viewpoint of the authors is that the seven building blocks of the health sector in Kenya are vital. The six building blocks can be handled by county governments while one block that deals with the management of HR of the health sector should be reformed, strengthened, and handled by the national government, hence the paper proposes the introduction of a health service commission to manage human resource components of the health sector. Finally, boost the Ministry of Health’s effective control of the healthcare workforce by advancing and integrating policies relating to health systems, services, and cross-sectorial collaboration to revive primary healthcare services and attain universal health coverage.
Supply Response of Kenyan Coffee in the World Market
(Kenyatta University, 2014-03-10) Njaramba, Stephen G.
The drastic drop in Kenya coffee supply in the last twenty years has severely affected the country's export revenues as well as the livelihoods of two million small scale producers and over six million people who directly or indirectly depend on coffee. In spite of the central role which coffee has played in the county's development, Coffee production has shown a steady decline over the last two decades. Coffee production declined from an all time high of about 130,000 metric tons in 1987/88 to a low of about 42,000 metric tons in the 2010 coffee calendar year In this study the objective was to estimate the response of Kenyan coffee which is supplied at the world market Coffee is an important crop to Kenya since it is a source of foreign exchange. It is also the main agricultural enterprise in some of the districts in the country and the major source of income to these districts. Therefore the research project sought to come up with the supply function of Kenyan coffee to the international market. Coffee supply in Kenya has continued to decline despite policy reforms in the coffee sector. The principal of cointegration and Error Correction Model were used to establish the effect of various variables to the supply of coffee to the international market. Despite the popular belief that falling international prices paid for coffee is the course of decline of supply from Kenya, this study found out that the international prices did not have significant effect on the supply of coffee from Kenya to the international market. Rather the supply is affected by cost of inputs both in the short and long run, the cost of moving coffee from the farm to the market, weather and the policies employed by the government. All the other variables were found to be insignificant at 5 percent level.
Application of SARIMAX Model to Forecast Weekly Irish Potato Retail Prices: A Case Study of Kitui County, Kenya
(Springer, 2024-11-12) Mutuku,Arthanus; Murage, Peter G.; Sewe, Stanley
The prices of Irish potatoes fluctuate with the seasons due to the influence of demand, supply, and macroeconomic variables such as inflation and interest rates. To effectively handle these variations, incorporating all the significant factors is crucial for Irish potato price forecasting. This study analyzed and forecasted weekly average Irish potato retail prices in major markets within Kitui County, Kenya via a seasonal autoregressive integrated moving average model incorporating inflation rates and interest rates as the exogenous variables. The study used time series data from the Kenya National Bureau of Statistics for weekly Irish potato prices, inflation rates, and interest rates from January 2014 to December 2022. We established that the SARIMAX model (1, 1, 2)(2, 1, 0)[52], with lagged inflation rates and lagged interest rates as the exogenous variable, provided the best fit for Irish potato prices. The reduced MAE, MSE, and RMSE proved that including external factors improved the forecasting accuracy of the SARIMAX model. The SARIMAX model in-sample predicted Irish potato prices were off the actual Irish potato prices by a mean price of Ksh.10.59, and better than the SARIMA model (1, 1, 2)(2, 1, 0)[52] predicted Irish potato prices by a mean price of Ksh.0.92.The SARIMAX model (1, 1, 1)(2, 1, 0)[52] was used to forecast future mean Irish potato retail prices for the next year.The insights drawn from this study would help farmers, consumers, and policymakers in this subsector make data-driven decisions in production investment and marketing, consumption and policymaking, respectively.
Time Series Analysis of Prostate Cancer Incidences in Meru County
(Sciencedomain International, 2024-12-29) Kamande, John K.; Okungu, Jacob O.; Murage, Peter G.
Cancer is a major health challenge. Globally, the estimated number of diagnosed cancer incidences is approximately 14.1 million people per year and a mortality rate of 8.2 million deaths per year. The primary objective was to develop robust predictive models to forecast prostate cancer incidences and identify significant trends and patterns that inform healthcare planning and interventions in Meru County Kenya using AutoRegressive Integrated Moving Average with exogeneous variable (ARIMAX) Models. The dataset used comprised historical records of prostate cancer incidences in Meru County. The data spaned from [Jan 2018] to [Nov 2023], providing a comprehensive overview of the trends over time. Additionally, exogenous variable age was included in the ARIMAX model to enhance the accuracy of the prostate cancer predictions. Data on the prevalence of prostate cancer was obtained from Meru Cancer Registry for 71 months. The ARIMAX model was fitted using the Box-Jenkins methodology which include four iterative steps that is model identification, parameter estimation, diagnostics and forecasting. The prostate cancer time series data was made stationary by differencing and log transformation. R programming (Version 4.3.3) software was used in the analysis. Further, given the highly sensitive nature of the forecast values, interpolated data from daily values to monthly values were used. The best models for the Prostate cancer incidences was ARIMAX (0,0,1). Majority of the Prostate cancer incidences were within the age group 70-79 years at 50.7%,ages 60-69 was 42.3% while 80-90 years was 7%. After log transformation and differencing of the prostate cancer time series data the Augmented Dickey Fuller test was performed and the p-value was (0.01) which was less than the significance level of (0.05), the null hypothesis was rejected that the prostate cancer time series had a unit root. Therefore, there was sufficient evidence to conclude that the time series was stationary. Ljung-Box test checked for the presence of autocorrelation at multiple lags and a high p-value = 0.719 greater than 0.05 indicated that there is no significant autocorrelation remaining in the residuals, thus the ARIMAX model was adequate. The MA(1) coefficient was -0.9, which indicated strong short-term negative autocorrelation. A positive value of 0.587 suggested that as the external variable increases by one unit, the log-transformed and differenced prostate cancer monthly cases (lnPCa Monthly cases d1) were expected to increase by 0.5871 units, holding all else constant. Results show that the ARIMAX(0,0,1) model slightly outperformed the ARIMA (0,0,1) model. This study successfully modeled the trends of prostate cancer incidences in Meru County using ARIMAX models. The findings indicated a rising trend in incidences, with the ARIMAX model providing the most accurate forecasts by incorporating the external variable age.
Threshold Determination for Maximum Product of Spacing Methodology with Ties for Extreme Events
(Machakos University, 2019-04) Murage, Peter G.; Mung’at, Joseph K.; Odero, Evelyne
Extreme events are defined as values of the event below or above a certain value called threshold. A well chosen threshold helps to identify the extreme levels. Several methods have been used to determine threshold so as to analyze and model extreme events. Maximum Product of spacing is one of these methods. However, there is a problem encountered while modeling data through this method in that the method breaks down when there is a tie in the exceedances. This study improved MPS method in order to determine an optimal threshold for extreme values in a data set containing ties, estimated the GPD parameters with the optimal threshold derived and then applied the method to determine the GPD parameters for a real market data that could be containing ties . The study applied a method to determine optimal threshold based on improved maximum product of spacing method and used Generalized Pareto Distribution (GPD) and Peak over threshold (POT) methods as the basis of identifying extreme. The peaks-over-threshold (POT) models are models for all large observations which exceed a high threshold. The POT models are generally considered to be the most useful for practical applications. The study used the method developed to deal with the ties to model the market volume data. This study will help the Statisticians in different sectors of our economy to model extreme events involving ties. To Statisticians, the structure of the extreme levels which exist in the tails of the ordinary distributions is very important in analyzing, predicting and forecasting the likelihood of an occurrence of extreme event.