Schistosomiasis commonly known as bilharzia is regarded by W.H.O as a neglected tropical disease. It affects the intestines and the urinary system preferentially, but can harm other systems in the body. The disease is a health concern among majority of the population in Mwea irrigation scheme in Kenya and indeed other tropical countries. This paper documents a deterministic analysis of the effectiveness of non-clinical approaches in the control of transmission of schistosomiasis in the region. A SIR based mathematical model that incorporates media campaigns as a control strategy of reducing transmission of the disease is used. The model considers behavior patterns of hosts as the main process of transmission of the disease. The dynamics of these processes is expressed in terms of ordinary differential equations deduced from the human behavior patterns that contribute to the spread of the disease. The reproduction number R0 and equilibrium points both DFE and EE are obtained. The stabilities of these equilibrium points are analyzed in reference to the reproduction number (R0). Secondary data is used in the mathematical model developed and in the prediction of the dynamics estimated in the model for a period of five years. Numerical simulation was carried out and results represented graphically. The results of the simulation show that the infection decreased from 75108 to about 35000 and the susceptible from 325142 to 50000 respectively in a period of five years. From the analysis, the DFE point is asymptotically stable when R_0<1.Sensitivity analysis of parameters was carried out using partial differentiation. The results show that the sensitivity index of most parameters are inversely proportional to R0 which will reduce schistosomiasis infection. From the results, incorporation of media campaigns as a control strategy significantly reduces transmission of the disease. The results will be useful to MOH to enhance media campaigns to prevent spread of schistosomiasis in Mwea Irrigation scheme and other endemic areas.
As data computing and big data driven analytics become more prevalent in a number of spatial industries, there is increasing need to quantify and communicate uncertainty with those data and resulting spatial analytical products. This has direct implication in oil & gas exploration and development where big data and data analytics continue to expand uses and applications of spatial and spatio-temporal data in the industry without providing for effective communication of spatial uncertainty. The result is that communications and inferences made using spatial data visuals lack crucial information about uncertainty and thus present a barrier to accurate and efficient decision making. With increasing cost awareness in oil & gas exploration and development, there is urgent need for methods and tools that help to objectively define and integrate uncertainty into business decisions. To address this need, the Variable Grid Method (VGM) has been developed for simultaneous communication of both spatial patterns and trends and the uncertainty associated with data or their analyses. The VGM utilizes varying grid cell sizes to visually communicate and constrain the uncertainty, creating an integrated layer that can be used to visualize uncertainty associated with spatial, spatio-temporal data or data-driven products. In this paper, we detail the VGM approach and demonstrate the utility of the VGM to intuitively quantify and provide cost-effective information about the relationship between uncertainty and spatial data. This allows trends of interest to be objectively investigated and target uncertainty criteria defined to drive optimal investment in improved subsurface definition. Examples are presented to show how the VGM can thus be used for efficient decision making in multiple applications including geological risk evaluation, as well as to optimize data acquisition in exploration and development. Today, uncertainty, if it is provided at all, is generally communicated using multiple independent visuals, aggregated in final displays, or omitted altogether. The VGM provides a robust method for quantifying and representing uncertainty in spatial data analyses, offering key information about the analysis, but also associated risks, both of which are vital for making prudent business decisions in oil & gas exploration and development.
According to world health organization [WHO] medical records, malaria has a global fatality of 200 million people annually. Most of the victims are mainly children and expectant women. In this work a deterministic model has been developed to show the transmission of malaria. The model consists of ordinary differential equations (ODEs) which describe how malaria spreads. Existence of equilibrium points was analyzed and the key to the analysis was by defining the basic Reproduction number (R0). Numerical simulations was performed by MatLab solver.
Predator-Prey relationship is a very fundamental aspect of every ecosystem. The associations that exist between different organisms in the ecosystem influence the survival of organisms and the functioning of the ecosystem as a whole. Lake Turkana is an understudied, permanent desert, fresh water lake located in north western, Kenya. Some of the species found in the lake include the Kingfishers and the Palegic fish. In this study the predator-prey relationship between the Kingfishers and the Pelagic fish (Pyrenean minnow) in the Lake Turkana has been analyzed qualitatively. A model of first order differential equations is proposed and studied after making certain simplifying assumptions. The analytical result showed that the system has two equilibriums, namely the trivial equilibrium and the endemic equilibrium which are stable. Numerical simulation was performed and analysis done to show the relationship between the two species. Results obtained from the simulation compares well with data obtained from Turkana Fisheries Company. The changes in the predator-prey species in the lake shows that predation has the balance of nature that exists in this ecosystem .
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.