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Epidemiological and time series analysis on the incidence and death of AIDS and HIV in China

BACKGROUND: To investigate the regional and age-specific distribution of AIDS/HIV in China from 2004 to 2017 and to conduct time series analysis of the epidemiological trends. METHOD: Using official surveillance data from publicly accessible database of the national infectious disease reporting syst...

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Autores principales: Xu, Bin, Li, Jiayuan, Wang, Mengqiao
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7734828/
https://www.ncbi.nlm.nih.gov/pubmed/33317484
http://dx.doi.org/10.1186/s12889-020-09977-8
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author Xu, Bin
Li, Jiayuan
Wang, Mengqiao
author_facet Xu, Bin
Li, Jiayuan
Wang, Mengqiao
author_sort Xu, Bin
collection PubMed
description BACKGROUND: To investigate the regional and age-specific distribution of AIDS/HIV in China from 2004 to 2017 and to conduct time series analysis of the epidemiological trends. METHOD: Using official surveillance data from publicly accessible database of the national infectious disease reporting system, we described long-term patterns of incidence and death in AIDS/HIV, analyzed age group and regional epidemic characteristics, and established Autoregressive Integrated Moving Average (ARIMA) models for time series analysis. RESULT: The incidence and death of AIDS/HIV have increased rapidly from 2004 to 2017, with significant difference regarding age groups and provincial regions (a few provinces appear as hot spots). With goodness-of-fit criteria and using data from 2004 to 2015, ARIMA (0,1,3) × (2,0,0), ARIMA (3,1,0) × (1,0,1), and ARIMA (0,1,2) × (2,0,0) were chosen as the optimal model for the incidence of AIDS, HIV, and combined; ARIMA (0,1,3) × (1,0,0) was chosen as the optimal model for the death of AIDS, HIV, and combined. ARIMA models robustly predicted the incidence and death of AIDS/HIV in 2016 and 2017. CONCLUSION: A focused intervention strategy targeting specific regions and age groups is essential for the prevention and control of AIDS/HIV. ARIMA models function as data-driven and evidence-based methods to forecast the trends of infectious diseases and formulate public health policies. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12889-020-09977-8.
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spelling pubmed-77348282020-12-15 Epidemiological and time series analysis on the incidence and death of AIDS and HIV in China Xu, Bin Li, Jiayuan Wang, Mengqiao BMC Public Health Research Article BACKGROUND: To investigate the regional and age-specific distribution of AIDS/HIV in China from 2004 to 2017 and to conduct time series analysis of the epidemiological trends. METHOD: Using official surveillance data from publicly accessible database of the national infectious disease reporting system, we described long-term patterns of incidence and death in AIDS/HIV, analyzed age group and regional epidemic characteristics, and established Autoregressive Integrated Moving Average (ARIMA) models for time series analysis. RESULT: The incidence and death of AIDS/HIV have increased rapidly from 2004 to 2017, with significant difference regarding age groups and provincial regions (a few provinces appear as hot spots). With goodness-of-fit criteria and using data from 2004 to 2015, ARIMA (0,1,3) × (2,0,0), ARIMA (3,1,0) × (1,0,1), and ARIMA (0,1,2) × (2,0,0) were chosen as the optimal model for the incidence of AIDS, HIV, and combined; ARIMA (0,1,3) × (1,0,0) was chosen as the optimal model for the death of AIDS, HIV, and combined. ARIMA models robustly predicted the incidence and death of AIDS/HIV in 2016 and 2017. CONCLUSION: A focused intervention strategy targeting specific regions and age groups is essential for the prevention and control of AIDS/HIV. ARIMA models function as data-driven and evidence-based methods to forecast the trends of infectious diseases and formulate public health policies. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12889-020-09977-8. BioMed Central 2020-12-14 /pmc/articles/PMC7734828/ /pubmed/33317484 http://dx.doi.org/10.1186/s12889-020-09977-8 Text en © The Author(s) 2020 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research Article
Xu, Bin
Li, Jiayuan
Wang, Mengqiao
Epidemiological and time series analysis on the incidence and death of AIDS and HIV in China
title Epidemiological and time series analysis on the incidence and death of AIDS and HIV in China
title_full Epidemiological and time series analysis on the incidence and death of AIDS and HIV in China
title_fullStr Epidemiological and time series analysis on the incidence and death of AIDS and HIV in China
title_full_unstemmed Epidemiological and time series analysis on the incidence and death of AIDS and HIV in China
title_short Epidemiological and time series analysis on the incidence and death of AIDS and HIV in China
title_sort epidemiological and time series analysis on the incidence and death of aids and hiv in china
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7734828/
https://www.ncbi.nlm.nih.gov/pubmed/33317484
http://dx.doi.org/10.1186/s12889-020-09977-8
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