Parameter identification for the Gumel-Mickens HIV transmission model under missing observable data
SOURCE: Global Journal of Pure and Applied Mathematics
OUTPUT TYPE: Journal Article
PUBLICATION YEAR: 2017
TITLE AUTHOR(S): M.Ngungu, C.R.Kikawa, A.C.Mkolesia, M.Y.Shatalov
KEYWORDS: DISEASES, ECOLOGY, EPIDEMIOLOGY
DEPARTMENT: Developmental, Capable and Ethical State (DCES)
Print: HSRC Library: shelf number 10165
HANDLE: 20.500.11910/11661
URI: http://hdl.handle.net/20.500.11910/11661
OUTPUT TYPE: Journal Article
PUBLICATION YEAR: 2017
TITLE AUTHOR(S): M.Ngungu, C.R.Kikawa, A.C.Mkolesia, M.Y.Shatalov
KEYWORDS: DISEASES, ECOLOGY, EPIDEMIOLOGY
DEPARTMENT: Developmental, Capable and Ethical State (DCES)
Print: HSRC Library: shelf number 10165
HANDLE: 20.500.11910/11661
URI: http://hdl.handle.net/20.500.11910/11661
If you would like to obtain a copy of this Research Output, please contact Hanlie Baudin at researchoutputs@hsrc.ac.za.
Abstract
In this paper, based on the model proposed by Gumel, Moghadas and Mickens, which monitors the impact of live attenuated HIV vaccines, a new method for restoring the unknown data is proposed. Missing data in both epidemiological and ecological surveys and experiments can be due to a number of causes, like, unprovided domains of required data, complete refusal by respondents etc. Differential methods are used to linearize the non-linear equations for the model as the parameter space of the original models is increased. Least squares methods are employed to estimate the unknown model coefficients. Results show that the proposed method is able to restore the missing data with an acceptable accuracy. The efficiency of the proposed method is verified by comparing the restored and observed data using the absolute percentage error. The proposed method can be used in practical research and studies to provide a clue on which data to analytically restore in case of missing information.-
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