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Analysis of HIV/AIDS Epidemic and Socioeconomic Factors in Sub-Saharan Africa

Sub-Saharan Africa has been the epicenter of the outbreak since the spread of acquired immunodeficiency syndrome (AIDS) began to be prevalent. This article proposes several regression models to investigate the relationships between the HIV/AIDS epidemic and socioeconomic factors (the gross domestic...

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Detalles Bibliográficos
Autores principales: Sun, Shuman, Li, Zhiming, Zhang, Huiguo, Jiang, Haijun, Hu, Xijian
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7712028/
https://www.ncbi.nlm.nih.gov/pubmed/33286998
http://dx.doi.org/10.3390/e22111230
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author Sun, Shuman
Li, Zhiming
Zhang, Huiguo
Jiang, Haijun
Hu, Xijian
author_facet Sun, Shuman
Li, Zhiming
Zhang, Huiguo
Jiang, Haijun
Hu, Xijian
author_sort Sun, Shuman
collection PubMed
description Sub-Saharan Africa has been the epicenter of the outbreak since the spread of acquired immunodeficiency syndrome (AIDS) began to be prevalent. This article proposes several regression models to investigate the relationships between the HIV/AIDS epidemic and socioeconomic factors (the gross domestic product per capita, and population density) in ten countries of Sub-Saharan Africa, for 2011–2016. The maximum likelihood method was used to estimate the unknown parameters of these models along with the Newton–Raphson procedure and Fisher scoring algorithm. Comparing these regression models, there exist significant spatiotemporal non-stationarity and auto-correlations between the HIV/AIDS epidemic and two socioeconomic factors. Based on the empirical results, we suggest that the geographically and temporally weighted Poisson autoregressive (GTWPAR) model is more suitable than other models, and has the better fitting results.
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spelling pubmed-77120282021-02-24 Analysis of HIV/AIDS Epidemic and Socioeconomic Factors in Sub-Saharan Africa Sun, Shuman Li, Zhiming Zhang, Huiguo Jiang, Haijun Hu, Xijian Entropy (Basel) Article Sub-Saharan Africa has been the epicenter of the outbreak since the spread of acquired immunodeficiency syndrome (AIDS) began to be prevalent. This article proposes several regression models to investigate the relationships between the HIV/AIDS epidemic and socioeconomic factors (the gross domestic product per capita, and population density) in ten countries of Sub-Saharan Africa, for 2011–2016. The maximum likelihood method was used to estimate the unknown parameters of these models along with the Newton–Raphson procedure and Fisher scoring algorithm. Comparing these regression models, there exist significant spatiotemporal non-stationarity and auto-correlations between the HIV/AIDS epidemic and two socioeconomic factors. Based on the empirical results, we suggest that the geographically and temporally weighted Poisson autoregressive (GTWPAR) model is more suitable than other models, and has the better fitting results. MDPI 2020-10-29 /pmc/articles/PMC7712028/ /pubmed/33286998 http://dx.doi.org/10.3390/e22111230 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Sun, Shuman
Li, Zhiming
Zhang, Huiguo
Jiang, Haijun
Hu, Xijian
Analysis of HIV/AIDS Epidemic and Socioeconomic Factors in Sub-Saharan Africa
title Analysis of HIV/AIDS Epidemic and Socioeconomic Factors in Sub-Saharan Africa
title_full Analysis of HIV/AIDS Epidemic and Socioeconomic Factors in Sub-Saharan Africa
title_fullStr Analysis of HIV/AIDS Epidemic and Socioeconomic Factors in Sub-Saharan Africa
title_full_unstemmed Analysis of HIV/AIDS Epidemic and Socioeconomic Factors in Sub-Saharan Africa
title_short Analysis of HIV/AIDS Epidemic and Socioeconomic Factors in Sub-Saharan Africa
title_sort analysis of hiv/aids epidemic and socioeconomic factors in sub-saharan africa
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7712028/
https://www.ncbi.nlm.nih.gov/pubmed/33286998
http://dx.doi.org/10.3390/e22111230
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