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Epidemic Situation of Brucellosis in Jinzhou City of China and Prediction Using the ARIMA Model
OBJECTIVE: This study aimed to investigate the specific epidemiological characteristics and epidemic situation of brucellosis in Jinzhou City of China so as to establish a suitable prediction model potentially applied as a decision-supportive tool for reasonably assigning health interventions and he...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6595367/ https://www.ncbi.nlm.nih.gov/pubmed/31312278 http://dx.doi.org/10.1155/2019/1429462 |
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author | Wang, Lulu Liang, Chen Wu, Wei Wu, Shengwen Yang, Jinghua Lu, Xiaobo Cai, Yuan Jin, Cuihong |
author_facet | Wang, Lulu Liang, Chen Wu, Wei Wu, Shengwen Yang, Jinghua Lu, Xiaobo Cai, Yuan Jin, Cuihong |
author_sort | Wang, Lulu |
collection | PubMed |
description | OBJECTIVE: This study aimed to investigate the specific epidemiological characteristics and epidemic situation of brucellosis in Jinzhou City of China so as to establish a suitable prediction model potentially applied as a decision-supportive tool for reasonably assigning health interventions and health delivery. METHODS: Monthly morbidity data from 2004 to 2013 were selected to construct the autoregressive integrated moving average (ARIMA) model using SPSS 13.0 software. Moreover, stability analysis and sequence tranquilization, model recognition, parameter test, and model diagnostic were also carried out. Finally, the fitting and prediction accuracy of the ARIMA model were evaluated using the monthly morbidity data in 2014. RESULTS: A total of 3078 cases affected by brucellosis were reported from January 1998 to December 2015 in Jinzhou City. The incidence of brucellosis had shown a fluctuating growth gradually. Moreover, the ARIMA(1,1,1)(0,1,1)(12) model was finally selected among quite a few plausible ARIMA models based upon the parameter test, correlation analysis, and Box–Ljung test. Notably, the incidence from 2005 to 2014 forecasted using this ARIMA model fitted well with the actual incidence data. Notably, the actual morbidity in 2014 fell within the scope of 95% confidence limit of values predicted by the ARIMA(1,1,1)(0,1,1)(12) model, with the absolute error between the predicted and the actual values in 2014 ranging from 0.02 to 0.74. Meanwhile, the MAPE was 19.83%. CONCLUSION: It is suitable to predict the incidence of brucellosis in Jinzhou City of China using the ARIMA(1,1,1)(0,1,1)(12) model. |
format | Online Article Text |
id | pubmed-6595367 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
spelling | pubmed-65953672019-07-16 Epidemic Situation of Brucellosis in Jinzhou City of China and Prediction Using the ARIMA Model Wang, Lulu Liang, Chen Wu, Wei Wu, Shengwen Yang, Jinghua Lu, Xiaobo Cai, Yuan Jin, Cuihong Can J Infect Dis Med Microbiol Research Article OBJECTIVE: This study aimed to investigate the specific epidemiological characteristics and epidemic situation of brucellosis in Jinzhou City of China so as to establish a suitable prediction model potentially applied as a decision-supportive tool for reasonably assigning health interventions and health delivery. METHODS: Monthly morbidity data from 2004 to 2013 were selected to construct the autoregressive integrated moving average (ARIMA) model using SPSS 13.0 software. Moreover, stability analysis and sequence tranquilization, model recognition, parameter test, and model diagnostic were also carried out. Finally, the fitting and prediction accuracy of the ARIMA model were evaluated using the monthly morbidity data in 2014. RESULTS: A total of 3078 cases affected by brucellosis were reported from January 1998 to December 2015 in Jinzhou City. The incidence of brucellosis had shown a fluctuating growth gradually. Moreover, the ARIMA(1,1,1)(0,1,1)(12) model was finally selected among quite a few plausible ARIMA models based upon the parameter test, correlation analysis, and Box–Ljung test. Notably, the incidence from 2005 to 2014 forecasted using this ARIMA model fitted well with the actual incidence data. Notably, the actual morbidity in 2014 fell within the scope of 95% confidence limit of values predicted by the ARIMA(1,1,1)(0,1,1)(12) model, with the absolute error between the predicted and the actual values in 2014 ranging from 0.02 to 0.74. Meanwhile, the MAPE was 19.83%. CONCLUSION: It is suitable to predict the incidence of brucellosis in Jinzhou City of China using the ARIMA(1,1,1)(0,1,1)(12) model. Hindawi 2019-06-13 /pmc/articles/PMC6595367/ /pubmed/31312278 http://dx.doi.org/10.1155/2019/1429462 Text en Copyright © 2019 Lulu Wang et al. http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Wang, Lulu Liang, Chen Wu, Wei Wu, Shengwen Yang, Jinghua Lu, Xiaobo Cai, Yuan Jin, Cuihong Epidemic Situation of Brucellosis in Jinzhou City of China and Prediction Using the ARIMA Model |
title | Epidemic Situation of Brucellosis in Jinzhou City of China and Prediction Using the ARIMA Model |
title_full | Epidemic Situation of Brucellosis in Jinzhou City of China and Prediction Using the ARIMA Model |
title_fullStr | Epidemic Situation of Brucellosis in Jinzhou City of China and Prediction Using the ARIMA Model |
title_full_unstemmed | Epidemic Situation of Brucellosis in Jinzhou City of China and Prediction Using the ARIMA Model |
title_short | Epidemic Situation of Brucellosis in Jinzhou City of China and Prediction Using the ARIMA Model |
title_sort | epidemic situation of brucellosis in jinzhou city of china and prediction using the arima model |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6595367/ https://www.ncbi.nlm.nih.gov/pubmed/31312278 http://dx.doi.org/10.1155/2019/1429462 |
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