IUJ Volume 5 No.1 2016

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Analysis of non-linear transmission of EBOLA virus disease and the impact of public health control interventions in hospital: the case of Guinea (West Africa) outbreak 2014.
(2016) Babangida, Bala Garba; Nazziwa, Aisha; Noor, Kasim; Adiukwu, Roseline Nwawure; Ngaloru, Stellamaris Ngozi; Mafuyai, Yaks Mabur; Obi, Edith Nkeiru; Onwunali, Magnus Chibueze; Obanny, Adolphus
ARTICLE ABSTRACT Epidemiological data on infection outbreaks are challenging to analyze, despite improved control interventions Ebola virus Disease (EVD) remains a serious risk in Guinea (West Africa) with 607 reported cases and 406 deaths recorded (66.8%) as of 20th August, 2014.In this study we use modified epidemiological modeling SEIR to analyze data from an Ebola outbreak in Guinea from 22nd march – 20th August,2014 We use Bayesian inference with non – linear transmission times incorporated into augmented data set as latent variables. Despite the lack of detailed data, most data sets record the time on symptom onset but transmission time is not observable. We inferred from such dataset records using structured Hidden Markov Models HMMS. Infectivity is determined before and after public health interventions for hospitalized cases. We estimate the number of secondary cases generated by an index case in the absence of control interventions (Ro). Our estimate of Ro is 1.57 (CI95 0.82-1.92) and the mean value of estimated detection rate is 0.75 (CI95 0.59 -0.93) with a coefficient of correlation between 𝛽𝛽 and v as – 0.23. We perform sensitivity analysis of the final epidemic size to the time of intervention, which ensures the uniqueness and the global stability of the positive endemic equilibrium state.
Application of Markov chain Modelto foreign debt management in Nigerian economy
(2016) Ngaloru, Stellamaris Ngozi; Abdulazeez, Halimat; Ebuk, Love E.; Adiukwu, Roseline N; Nafiu, Lukman Abiodun
ARTICLE ABSTRACT Since the last century, there have been marked changes in the approach to scientific enquiries and greater realisation that probability (or non-deterministic) models are more realistic than deterministic models in many situations. Observations taken at different time points rather than those taken at a fixed period of time began to engage the attention of probabilists. Many stochastic processes occurring in social sciences are studied now not only as a random phenomenon but also as one changing with time or space called Markov chain. This study considered an application of Markov chain model to predict future debt pattern as effective management of any nation’s debt is crucial to growth and development of the economy of that nation. We collected data on debts maintained by all the thirty-six states governments and Federal Capital Territory, Abuja in Nigeria for a period of six years and determined future debt trend based on the transition probabilities between various groups of transition states. Three noticeable transition states namely; rising, stable and dropping of debt trend were used. The findings revealed that 55% of states governments will have a rise in their debt profile, 6% will have a stable debt profile while 39% will have a drop in their debt profile. Therefore, it was recommended that the federal government should design appropriate financing strategies that guarantee a debt path matching loan with the ability to repay and put up a policy as a preventive action to reduce, in the medium term, the possibility of debt unsustainability by the various states governments in Nigeria.

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Browsing IUJ Volume 5 No.1 2016 by Author "Ngaloru, Stellamaris Ngozi"