HCV extinction analysis in district Gujrat, Pakistan by using SARIMA and linear regression models. Issue 49 (10th December 2021)
- Record Type:
- Journal Article
- Title:
- HCV extinction analysis in district Gujrat, Pakistan by using SARIMA and linear regression models. Issue 49 (10th December 2021)
- Main Title:
- HCV extinction analysis in district Gujrat, Pakistan by using SARIMA and linear regression models
- Authors:
- Rashid, Muhammad
Ismail, Hammad - Editors:
- Khan., Abrar Hussain
- Abstract:
- Abstract : Supplemental Digital Content is available in the text Abstract: 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 LRAbstract : Supplemental Digital Content is available in the text Abstract: 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. … (more)
- Is Part Of:
- Medicine. Volume 100:Issue 49(2021)
- Journal:
- Medicine
- Issue:
- Volume 100:Issue 49(2021)
- Issue Display:
- Volume 100, Issue 49 (2021)
- Year:
- 2021
- Volume:
- 100
- Issue:
- 49
- Issue Sort Value:
- 2021-0100-0049-0000
- Page Start:
- e28193
- Page End:
- Publication Date:
- 2021-12-10
- Subjects:
- district Gujrat Pakistan -- hepatitis C -- linear regression -- modeling -- seasonal auto-regressive integrated moving average
Medicine -- Periodicals
Medicine -- Periodicals
Médecine -- Périodiques
Geneeskunde
Medicine
Periodicals
Periodicals
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http://journals.lww.com ↗ - DOI:
- 10.1097/MD.0000000000028193 ↗
- Languages:
- English
- ISSNs:
- 0025-7974
- Deposit Type:
- Legaldeposit
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