Browsing by Author "Wakhungu, Jacob"

Now showing 1 - 2 of 2

Causes and trends of public transport motorcycle accidents in Bungoma county, Kenya
(2016) Singoro, Brian Wanyama; Wakhungu, Jacob; Obiri, John; Were
There has been a drastic increase in the use of motorcycles as a means of transport worldwide due to various reasons. In Kenya, the increased use of motorcycles has been seen over the last decade. This increase has brought forth many challenges, including motorcycle accidents on disproportionate scale comparative to the world statistics. Indeed motorcycle accidents constitute a major cause of death and injuries to thousands of people every year. In spite of this, motorcycle accidents remain a neglected problem in Kenya. This study sought to determine the causes and trends of motorcycle accidents in Bungoma County. The study population comprised 400 people from households of motorcycle riders involved in accidents and those not involved. Key informants in the motorcycle transport industry were interviewed. The study adopted a cross-sectional survey design to establish the causes, incidences/trends, and vulnerability of motorcycle accidents. Descriptive and inferential statistics were used in the analysis of data. The study was anchored on both the crunch model and the wish to die and domino theory. The study found that human error is the leading cause of motorcycle accidents. This is imparted on by poor regulatory and enforcement regimes. Structured and comprehensive training of riders on traffic code and regulations will most likely reduce accidents and associated economic losses. Collective action measures such as motorcycle Saccos for voluntary enforcement and pooling of resources, to aid riders in case of injuries and death, should be explored and pursued. The study provides information and insights on disaster risk reduction for policy formulation on motorcycle accident mitigation. From the results, the proposed strategies that can be employed to curb motorcycle accidents in the order of magnitude are: training of motorcycle riders; observing speed limits; improved roads; not driving while under the influence of drugs/ alcohol; not carrying more than one passenger; improved enforcement by police; proper motorcycle maintenance; wearing protective clothes/ helmets/ boots; wearing reflective jacket; and not driving while tired.
Dynamic risk model for Rift Valley fever outbreaks in Kenya based on climate and disease outbreak data
(PAGEpress-Geospatial Health, 2015-10-18) Gikungu, David; Wakhungu, Jacob; Siamba, D.N; Neyole, Edward
Rift Valley fever (RVF) is a mosquito-borne viral zoonotic disease that occurs throughout sub-Saharan Africa, Egypt and the Arabian Peninsula, with heavy impact in affected countries. Outbreaks are episodic and related to climate variability, especially rainfall and flooding. Despite great strides towards better prediction of RVF epidemics, there is still no observed climate data-based warning system with sufficient lead time for appropriate response and mitigation. We present a dynamic risk model based on historical RVF outbreaks and observed meteorological data. The model uses 30-year data on rainfall, temperature, relative humidity, normalised difference vegetation index and sea surface temperature data as predictors. Our research on RVF focused on Garissa, Murang’a and Kwale counties in Kenya using a research design based on a correlational, experimental, and evaluational approach. The weather data were obtained from the Kenya Meteorological Department while the RVF data were acquired from International Livestock Research Institute, and the Department of Veterinary Services. Performance of the model was evaluated by using the first 70% of the data for calibration and the remaining 30% for validation. The assessed components of the model accurately predicted already observed RVF events. The Brier score for each of the models (ranging from 0.007 to 0.022) indicated high skill. The coefficient of determination (R2) was higher in Garissa (0.66) than in Murang’a (0.21) and Kwale (0.16). The discrepancy was attributed to data distribution differences and varying ecosystems. The model outputs should complement existing early warning systems to detect risk factors that predispose for RVF outbreaks.