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HCV extinction analysis in district Gujrat, Pakistan by using SARIMA and linear regression models
BACKGROUND: To investigate the track of Gujrat, a District of Pakistan is very essential, either it follow-up World Health Organization (WHO) Hepatitis C Virus (HCV) elimination plan or not. This study aimed to find out HCV extinction analysis by time series forecast from District Gujrat, Pakistan....
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Lippincott Williams & Wilkins
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8663847/ https://www.ncbi.nlm.nih.gov/pubmed/34889300 http://dx.doi.org/10.1097/MD.0000000000028193 |
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author | Rashid, Muhammad Ismail, Hammad |
author_facet | Rashid, Muhammad Ismail, Hammad |
author_sort | Rashid, Muhammad |
collection | PubMed |
description | BACKGROUND: To investigate the track of Gujrat, a District of Pakistan is very essential, either it follow-up World Health Organization (WHO) Hepatitis C Virus (HCV) elimination plan or not. This study aimed to find out HCV extinction analysis by time series forecast from District Gujrat, Pakistan. METHODS: From January 1, 2016 to December 31, 2020 total n-5,111 numbers of HCV real-time polymerase chain reaction (RT-PCR) tests were performed in Gujrat. For extinction analysis we used 2 different models, the first model was seasonal auto-regressive integrated moving average (SARIMA) and the second linear regression (LR) model. First, we fitted both models then these fitted and valid models were used to predict future HCV percentage in District Gujrat. RESULTS: In District Gujrat, the men HCV infected ratio is high with a higher viral load as compared with women, from year 2016 to 2020 male to female ratio was (53.75:53.19), (45.67:43.84), (39.67:39.36), (41.94:35.88), (37.70:31.38) respectively. HCV percentage is decreasing from 2016 to 2020 with an average of 4.98%. Our both fitted models SARIMAX (0,1,1)(0,1,1,6) at 95% confidence intervals and LR model Y = –0.379 X + 53.378 at 99% confidence intervals (P-value = .00) revealed that in June 2029 and in August 2027 respectively HCV percentage will be 0 from district Gujrat, Pakistan. CONCLUSIONS: This study concluded that both SARIMA and LR models showed an effective modeling process for forecasting yearly HCV incidence. District Gujrat, Punjab, Pakistan is on track to achieve the WHO HCV elimination plan, before 2030 HCV will be extinct from this region. |
format | Online Article Text |
id | pubmed-8663847 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Lippincott Williams & Wilkins |
record_format | MEDLINE/PubMed |
spelling | pubmed-86638472021-12-13 HCV extinction analysis in district Gujrat, Pakistan by using SARIMA and linear regression models Rashid, Muhammad Ismail, Hammad Medicine (Baltimore) 4500 BACKGROUND: To investigate the track of Gujrat, a District of Pakistan is very essential, either it follow-up World Health Organization (WHO) Hepatitis C Virus (HCV) elimination plan or not. This study aimed to find out HCV extinction analysis by time series forecast from District Gujrat, Pakistan. METHODS: From January 1, 2016 to December 31, 2020 total n-5,111 numbers of HCV real-time polymerase chain reaction (RT-PCR) tests were performed in Gujrat. For extinction analysis we used 2 different models, the first model was seasonal auto-regressive integrated moving average (SARIMA) and the second linear regression (LR) model. First, we fitted both models then these fitted and valid models were used to predict future HCV percentage in District Gujrat. RESULTS: In District Gujrat, the men HCV infected ratio is high with a higher viral load as compared with women, from year 2016 to 2020 male to female ratio was (53.75:53.19), (45.67:43.84), (39.67:39.36), (41.94:35.88), (37.70:31.38) respectively. HCV percentage is decreasing from 2016 to 2020 with an average of 4.98%. Our both fitted models SARIMAX (0,1,1)(0,1,1,6) at 95% confidence intervals and LR model Y = –0.379 X + 53.378 at 99% confidence intervals (P-value = .00) revealed that in June 2029 and in August 2027 respectively HCV percentage will be 0 from district Gujrat, Pakistan. CONCLUSIONS: This study concluded that both SARIMA and LR models showed an effective modeling process for forecasting yearly HCV incidence. District Gujrat, Punjab, Pakistan is on track to achieve the WHO HCV elimination plan, before 2030 HCV will be extinct from this region. Lippincott Williams & Wilkins 2021-12-10 /pmc/articles/PMC8663847/ /pubmed/34889300 http://dx.doi.org/10.1097/MD.0000000000028193 Text en Copyright © 2021 the Author(s). Published by Wolters Kluwer Health, Inc. https://creativecommons.org/licenses/by-nc/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution-Non Commercial License 4.0 (CCBY-NC), where it is permissible to download, share, remix, transform, and buildup the work provided it is properly cited. The work cannot be used commercially without permission from the journal. http://creativecommons.org/licenses/by-nc/4.0 (https://creativecommons.org/licenses/by-nc/4.0/) |
spellingShingle | 4500 Rashid, Muhammad Ismail, Hammad HCV extinction analysis in district Gujrat, Pakistan by using SARIMA and linear regression models |
title | HCV extinction analysis in district Gujrat, Pakistan by using SARIMA and linear regression models |
title_full | HCV extinction analysis in district Gujrat, Pakistan by using SARIMA and linear regression models |
title_fullStr | HCV extinction analysis in district Gujrat, Pakistan by using SARIMA and linear regression models |
title_full_unstemmed | HCV extinction analysis in district Gujrat, Pakistan by using SARIMA and linear regression models |
title_short | HCV extinction analysis in district Gujrat, Pakistan by using SARIMA and linear regression models |
title_sort | hcv extinction analysis in district gujrat, pakistan by using sarima and linear regression models |
topic | 4500 |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8663847/ https://www.ncbi.nlm.nih.gov/pubmed/34889300 http://dx.doi.org/10.1097/MD.0000000000028193 |
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