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      HCV extinction analysis in district Gujrat, Pakistan by using SARIMA and linear regression models

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          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.

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          Most cited references20

          • Record: found
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          Global Distribution and Prevalence of Hepatitis C Virus Genotypes

          Jane Messina, Isla Humphreys, Abraham Flaxman … (2014)
          Hepatitis C virus (HCV) exhibits high genetic diversity, characterized by regional variations in genotype prevalence. This poses a challenge to the improved development of vaccines and pan-genotypic treatments, which require the consideration of global trends in HCV genotype prevalence. Here we provide the first comprehensive survey of these trends. To approximate national HCV genotype prevalence, studies published between 1989 and 2013 reporting HCV genotypes are reviewed and combined with overall HCV prevalence estimates from the Global Burden of Disease (GBD) project. We also generate regional and global genotype prevalence estimates, inferring data for countries lacking genotype information. We include 1,217 studies in our analysis, representing 117 countries and 90% of the global population. We calculate that HCV genotype 1 is the most prevalent worldwide, comprising 83.4 million cases (46.2% of all HCV cases), approximately one-third of which are in East Asia. Genotype 3 is the next most prevalent globally (54.3 million, 30.1%); genotypes 2, 4, and 6 are responsible for a total 22.8% of all cases; genotype 5 comprises the remaining <1%. While genotypes 1 and 3 dominate in most countries irrespective of economic status, the largest proportions of genotypes 4 and 5 are in lower-income countries. Conclusion: Although genotype 1 is most common worldwide, nongenotype 1 HCV cases—which are less well served by advances in vaccine and drug development—still comprise over half of all HCV cases. Relative genotype proportions are needed to inform healthcare models, which must be geographically tailored to specific countries or regions in order to improve access to new treatments. Genotype surveillance data are needed from many countries to improve estimates of unmet need. (Hepatology 2015;61:77–87)
          Article 0 comments Cited 147 times – based on 0 reviews     Review now
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            Comparative Study of Four Time Series Methods in Forecasting Typhoid Fever Incidence in China

            Xingyu Zhang, Yuanyuan Liu, Min Yang … (2013)
            Accurate incidence forecasting of infectious disease is critical for early prevention and for better government strategic planning. In this paper, we present a comprehensive study of different forecasting methods based on the monthly incidence of typhoid fever. The seasonal autoregressive integrated moving average (SARIMA) model and three different models inspired by neural networks, namely, back propagation neural networks (BPNN), radial basis function neural networks (RBFNN), and Elman recurrent neural networks (ERNN) were compared. The differences as well as the advantages and disadvantages, among the SARIMA model and the neural networks were summarized and discussed. The data obtained for 2005 to 2009 and for 2010 from the Chinese Center for Disease Control and Prevention were used as modeling and forecasting samples, respectively. The performances were evaluated based on three metrics: mean absolute error (MAE), mean absolute percentage error (MAPE), and mean square error (MSE). The results showed that RBFNN obtained the smallest MAE, MAPE and MSE in both the modeling and forecasting processes. The performances of the four models ranked in descending order were: RBFNN, ERNN, BPNN and the SARIMA model.
            Article 0 comments Cited 55 times – based on 0 reviews     Review now
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              Understanding and checking the assumptions of linear regression: a primer for medical researchers.

              Robert J. Casson, Lachlan D M Farmer (2014)
              Linear regression (LR) is a powerful statistical model when used correctly. Because the model is an approximation of the long-term sequence of any event, it requires assumptions to be made about the data it represents in order to remain appropriate. However, these assumptions are often misunderstood. We present the basic assumptions used in the LR model and offer a simple methodology for checking if they are satisfied prior to its use. In doing so, we aim to increase the effectiveness and appropriateness of LR in clinical research.
              Article 0 comments Cited 52 times – based on 0 reviews     Review now
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                Author and article information

                Journal
                Journal ID (nlm-ta): Medicine (Baltimore)
                Journal ID (iso-abbrev): Medicine (Baltimore)
                Journal ID (publisher-id): MEDI
                Title: Medicine
                Publisher: Lippincott Williams & Wilkins (Hagerstown, MD )
                ISSN (Print): 0025-7974
                ISSN (Electronic): 1536-5964
                Publication date Collection: 10 December 2021
                Publication date (Electronic): 10 December 2021
                Volume: 100
                Issue: 49
                Electronic Location Identifier: e28193
                Affiliations
                Department of Biochemistry and Biotechnology, University of Gujrat-Hafiz Hayat Campus, Gujrat, Punjab, Pakistan.
                Author notes
                [∗ ]Correspondence: Hammad Ismail, University of Gujrat - Hafiz Hayat Campus, University of Gujrat, Gujrat 50700, Punjab, Pakistan (e-mail: hammad.ismail@ 123456uog.edu.pk ).
                Article
                Publisher ID: MD-D-21-01736 Accession ID: 28193
                DOI: 10.1097/MD.0000000000028193
                PMC ID: 8663847
                PubMed ID: 34889300
                SO-VID: d8309821-64de-4de0-b9f4-90b344663b11
                Copyright © Copyright © 2021 the Author(s). Published by Wolters Kluwer Health, Inc.
                License:

                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

                History
                Date received : 3 March 2021
                Date revision received : 17 November 2021
                Date accepted : 19 November 2021
                Categories
                Subject: 4500
                Subject: Research Article
                Subject: Clinical Trial/Experimental Study
                Custom metadata
                OPEN-ACCESS TRUE

                Keywords: district gujrat pakistan,hepatitis c,linear regression,modeling,seasonal auto-regressive integrated moving average
                Data availability:
                Keywords: district gujrat pakistan, hepatitis c, linear regression, modeling, seasonal auto-regressive integrated moving average

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