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Application of Two-level Fractional Factorial Design to Determine and Optimize the Effect of Demographic Characteristics on HIV Prevalence using the 2006 South African Annual Antenatal HIV and Syphilis Seroprevalence data

by Wilbert Sibanda, Philip Pretorius
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 35 - Number 12
Year of Publication: 2011
Authors: Wilbert Sibanda, Philip Pretorius
10.5120/4539-6421

Wilbert Sibanda, Philip Pretorius . Application of Two-level Fractional Factorial Design to Determine and Optimize the Effect of Demographic Characteristics on HIV Prevalence using the 2006 South African Annual Antenatal HIV and Syphilis Seroprevalence data. International Journal of Computer Applications. 35, 12 ( December 2011), 15-20. DOI=10.5120/4539-6421

@article{ 10.5120/4539-6421,
author = { Wilbert Sibanda, Philip Pretorius },
title = { Application of Two-level Fractional Factorial Design to Determine and Optimize the Effect of Demographic Characteristics on HIV Prevalence using the 2006 South African Annual Antenatal HIV and Syphilis Seroprevalence data },
journal = { International Journal of Computer Applications },
issue_date = { December 2011 },
volume = { 35 },
number = { 12 },
month = { December },
year = { 2011 },
issn = { 0975-8887 },
pages = { 15-20 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume35/number12/4539-6421/ },
doi = { 10.5120/4539-6421 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:21:47.898349+05:30
%A Wilbert Sibanda
%A Philip Pretorius
%T Application of Two-level Fractional Factorial Design to Determine and Optimize the Effect of Demographic Characteristics on HIV Prevalence using the 2006 South African Annual Antenatal HIV and Syphilis Seroprevalence data
%J International Journal of Computer Applications
%@ 0975-8887
%V 35
%N 12
%P 15-20
%D 2011
%I Foundation of Computer Science (FCS), NY, USA
Abstract

A Two-Level Fractional Factorial design was employed to develop and optimize the combination of demographic characteristics that has the greatest effect on the spread of HIV in the South African population. HIV prevalence (dependent variable) was found to be highly sensitive to changes in the mother’s age (15-55years) and level of education (Grades 0-13) (independent variables), using the 2006 South African Annual Antenatal HIV and Syphilis Seroprevalence data.

References
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Index Terms

Computer Science
Information Sciences

Keywords

Screening design Fractional factorial design Demographic characteristics Seroprevalence data

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