Cargando…

Time series analysis of cumulative incidences of typhoid and paratyphoid fevers in China using both Grey and SARIMA models

Typhoid and paratyphoid fevers are common enteric diseases causing disability and death in China. Incidence data of typhoid and paratyphoid between 2004 and 2016 in China were analyzed descriptively to explore the epidemiological features such as age-specific and geographical distribution. Cumulativ...

Descripción completa

Detalles Bibliográficos
Autores principales: Gao, Jiaqi, Li, Jiayuan, Wang, Mengqiao
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7592733/
https://www.ncbi.nlm.nih.gov/pubmed/33112899
http://dx.doi.org/10.1371/journal.pone.0241217
_version_ 1783601241964675072
author Gao, Jiaqi
Li, Jiayuan
Wang, Mengqiao
author_facet Gao, Jiaqi
Li, Jiayuan
Wang, Mengqiao
author_sort Gao, Jiaqi
collection PubMed
description Typhoid and paratyphoid fevers are common enteric diseases causing disability and death in China. Incidence data of typhoid and paratyphoid between 2004 and 2016 in China were analyzed descriptively to explore the epidemiological features such as age-specific and geographical distribution. Cumulative incidence of both fevers displayed significant decrease nationally, displaying a drop of 73.9% for typhoid and 86.6% for paratyphoid in 2016 compared to 2004. Cumulative incidence fell in all age subgroups and the 0–4 years-old children were the most susceptible ones in recent years. A cluster of three southwestern provinces (Yunnan, Guizhou, and Guangxi) were the top high-incidence regions. Grey model GM (1,1) and seasonal autoregressive integrated moving average (SARIMA) model were employed to extract the long-term trends of the diseases. Annual cumulative incidence for typhoid and paratyphoid were formulated by GM (1,1) as [Image: see text] and [Image: see text] respectively. SARIMA (0,1,7) × (1,0,1)(12) was selected among a collection of constructed models for high R(2) and low errors. The predictive models for both fevers forecasted cumulative incidence to continue the slightly downward trend and maintain the cyclical seasonality in near future years. Such data-driven insights are informative and actionable for the prevention and control of typhoid and paratyphoid fevers as serious infectious diseases.
format Online
Article
Text
id pubmed-7592733
institution National Center for Biotechnology Information
language English
publishDate 2020
publisher Public Library of Science
record_format MEDLINE/PubMed
spelling pubmed-75927332020-11-02 Time series analysis of cumulative incidences of typhoid and paratyphoid fevers in China using both Grey and SARIMA models Gao, Jiaqi Li, Jiayuan Wang, Mengqiao PLoS One Research Article Typhoid and paratyphoid fevers are common enteric diseases causing disability and death in China. Incidence data of typhoid and paratyphoid between 2004 and 2016 in China were analyzed descriptively to explore the epidemiological features such as age-specific and geographical distribution. Cumulative incidence of both fevers displayed significant decrease nationally, displaying a drop of 73.9% for typhoid and 86.6% for paratyphoid in 2016 compared to 2004. Cumulative incidence fell in all age subgroups and the 0–4 years-old children were the most susceptible ones in recent years. A cluster of three southwestern provinces (Yunnan, Guizhou, and Guangxi) were the top high-incidence regions. Grey model GM (1,1) and seasonal autoregressive integrated moving average (SARIMA) model were employed to extract the long-term trends of the diseases. Annual cumulative incidence for typhoid and paratyphoid were formulated by GM (1,1) as [Image: see text] and [Image: see text] respectively. SARIMA (0,1,7) × (1,0,1)(12) was selected among a collection of constructed models for high R(2) and low errors. The predictive models for both fevers forecasted cumulative incidence to continue the slightly downward trend and maintain the cyclical seasonality in near future years. Such data-driven insights are informative and actionable for the prevention and control of typhoid and paratyphoid fevers as serious infectious diseases. Public Library of Science 2020-10-28 /pmc/articles/PMC7592733/ /pubmed/33112899 http://dx.doi.org/10.1371/journal.pone.0241217 Text en © 2020 Gao et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Gao, Jiaqi
Li, Jiayuan
Wang, Mengqiao
Time series analysis of cumulative incidences of typhoid and paratyphoid fevers in China using both Grey and SARIMA models
title Time series analysis of cumulative incidences of typhoid and paratyphoid fevers in China using both Grey and SARIMA models
title_full Time series analysis of cumulative incidences of typhoid and paratyphoid fevers in China using both Grey and SARIMA models
title_fullStr Time series analysis of cumulative incidences of typhoid and paratyphoid fevers in China using both Grey and SARIMA models
title_full_unstemmed Time series analysis of cumulative incidences of typhoid and paratyphoid fevers in China using both Grey and SARIMA models
title_short Time series analysis of cumulative incidences of typhoid and paratyphoid fevers in China using both Grey and SARIMA models
title_sort time series analysis of cumulative incidences of typhoid and paratyphoid fevers in china using both grey and sarima models
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7592733/
https://www.ncbi.nlm.nih.gov/pubmed/33112899
http://dx.doi.org/10.1371/journal.pone.0241217
work_keys_str_mv AT gaojiaqi timeseriesanalysisofcumulativeincidencesoftyphoidandparatyphoidfeversinchinausingbothgreyandsarimamodels
AT lijiayuan timeseriesanalysisofcumulativeincidencesoftyphoidandparatyphoidfeversinchinausingbothgreyandsarimamodels
AT wangmengqiao timeseriesanalysisofcumulativeincidencesoftyphoidandparatyphoidfeversinchinausingbothgreyandsarimamodels