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Published in: BMC Infectious Diseases 1/2024

Open Access 01-12-2024 | Hepatitis E | Research

Collateral effects of COVID-19 countermeasures on hepatitis E incidence pattern: a case study of china based on time series models

Authors: Yajun Qin, Haiyang Peng, Jinhao Li, Jianping Gong

Published in: BMC Infectious Diseases | Issue 1/2024

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Abstract

Background

There are abundant studies on COVID-19 but few on its impact on hepatitis E. We aimed to assess the effect of the COVID-19 countermeasures on the pattern of hepatitis E incidence and explore the application of time series models in analyzing this pattern.

Methods

Our pivotal idea was to fit a pre-COVID-19 model with data from before the COVID-19 outbreak and use the deviation between forecast values and actual values to reflect the effect of COVID-19 countermeasures. We analyzed the pattern of hepatitis E incidence in China from 2013 to 2018. We evaluated the fitting and forecasting capability of 3 methods before the COVID-19 outbreak. Furthermore, we employed these methods to construct pre-COVID-19 incidence models and compare post-COVID-19 forecasts with reality.

Results

Before the COVID-19 outbreak, the Chinese hepatitis E incidence pattern was overall stationary and seasonal, with a peak in March, a trough in October, and higher levels in winter and spring than in summer and autumn, annually. Nevertheless, post-COVID-19 forecasts from pre-COVID-19 models were extremely different from reality in sectional periods but congruous in others.

Conclusions

Since the COVID-19 pandemic, the Chinese hepatitis E incidence pattern has altered substantially, and the incidence has greatly decreased. The effect of the COVID-19 countermeasures on the pattern of hepatitis E incidence was temporary. The incidence of hepatitis E was anticipated to gradually revert to its pre-COVID-19 pattern.
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Literature
2.
go back to reference Terrault NA, Levy MT, Cheung KW, Jourdain G. Viral hepatitis and pregnancy. Nat Rev Gastroenterol Hepatol. 2021;18(2):117–30.CrossRefPubMed Terrault NA, Levy MT, Cheung KW, Jourdain G. Viral hepatitis and pregnancy. Nat Rev Gastroenterol Hepatol. 2021;18(2):117–30.CrossRefPubMed
3.
go back to reference Aslan AT, Balaban HY. Hepatitis E virus: Epidemiology, diagnosis, clinical manifestations, and treatment. World J Gastroenterol. 2020;26(37):5543–60.CrossRefPubMedPubMedCentral Aslan AT, Balaban HY. Hepatitis E virus: Epidemiology, diagnosis, clinical manifestations, and treatment. World J Gastroenterol. 2020;26(37):5543–60.CrossRefPubMedPubMedCentral
4.
go back to reference Peron JM, Larrue H, Izopet J, Buti M. The pressing need for a global HEV vaccine. J Hepatol. 2023. Peron JM, Larrue H, Izopet J, Buti M. The pressing need for a global HEV vaccine. J Hepatol. 2023.
5.
go back to reference Debing Y, Moradpour D, Neyts J, Gouttenoire J. Update on hepatitis E virology: Implications for clinical practice. J Hepatol. 2016;65(1):200–12.CrossRefPubMed Debing Y, Moradpour D, Neyts J, Gouttenoire J. Update on hepatitis E virology: Implications for clinical practice. J Hepatol. 2016;65(1):200–12.CrossRefPubMed
7.
go back to reference Rana MS, Usman M, Alam MM, Ikram A, Salman M, Zaidi SSZ, Umair M, Qadir M. Impact of COVID-19 preventive measures on other infectious and non-infectious respiratory diseases in Pakistan. J Infect. 2021;82(5):e31–2.CrossRefPubMedPubMedCentral Rana MS, Usman M, Alam MM, Ikram A, Salman M, Zaidi SSZ, Umair M, Qadir M. Impact of COVID-19 preventive measures on other infectious and non-infectious respiratory diseases in Pakistan. J Infect. 2021;82(5):e31–2.CrossRefPubMedPubMedCentral
8.
go back to reference Lee HH, Lin SH. Effects of COVID-19 Prevention Measures on Other Common Infections. Taiwan Emerg Infect Dis. 2020;26(10):2509–11.CrossRefPubMed Lee HH, Lin SH. Effects of COVID-19 Prevention Measures on Other Common Infections. Taiwan Emerg Infect Dis. 2020;26(10):2509–11.CrossRefPubMed
9.
go back to reference Zhou J, Chen HJ, Lu TJ, Chen P, Zhuang Y, Li JL. Impact of COVID-19 prevention and control on tuberculosis and scarlet fever in China’s Guizhou. Sci Rep. 2023;13(1):9540.CrossRefPubMedPubMedCentral Zhou J, Chen HJ, Lu TJ, Chen P, Zhuang Y, Li JL. Impact of COVID-19 prevention and control on tuberculosis and scarlet fever in China’s Guizhou. Sci Rep. 2023;13(1):9540.CrossRefPubMedPubMedCentral
10.
go back to reference Lau K, Dorigatti I, Miraldo M, Hauck K. SARIMA-modelled greater severity and mortality during the 2010/11 post-pandemic influenza season compared to the 2009 H1N1 pandemic in English hospitals. Int J Infect Dis. 2021;105:161–71.CrossRefPubMed Lau K, Dorigatti I, Miraldo M, Hauck K. SARIMA-modelled greater severity and mortality during the 2010/11 post-pandemic influenza season compared to the 2009 H1N1 pandemic in English hospitals. Int J Infect Dis. 2021;105:161–71.CrossRefPubMed
11.
go back to reference Hyndman RJ, Athanasopoulos G. Forecasting: principles and practice: OTexts; 2018. Hyndman RJ, Athanasopoulos G. Forecasting: principles and practice: OTexts; 2018.
12.
go back to reference Hyndman RJ, Khandakar Y. Automatic time series forecasting: the forecast package for R. J Stat Softw. 2008;27:1–22.CrossRef Hyndman RJ, Khandakar Y. Automatic time series forecasting: the forecast package for R. J Stat Softw. 2008;27:1–22.CrossRef
13.
go back to reference Holt CC. Forecasting seasonals and trends by exponentially weighted moving averages. Int J Forecast. 2004;20(1):5–10.CrossRef Holt CC. Forecasting seasonals and trends by exponentially weighted moving averages. Int J Forecast. 2004;20(1):5–10.CrossRef
14.
go back to reference Winters PR. Forecasting sales by exponentially weighted moving averages. Manage Sci. 1960;6(3):324–42.CrossRef Winters PR. Forecasting sales by exponentially weighted moving averages. Manage Sci. 1960;6(3):324–42.CrossRef
15.
go back to reference Lewis CD. Industrial and business forecasting methods: A practical guide to exponential smoothing and curve fitting: Butterworth-Heinemann; 1982. Lewis CD. Industrial and business forecasting methods: A practical guide to exponential smoothing and curve fitting: Butterworth-Heinemann; 1982.
16.
go back to reference Ren-jie Q, Ting-xin S. Manufacturing Quality Control and Prediction Based on R Language. Modular Machine Tool & Automatic Manufacturing Technique. 2019;02:127–30. Ren-jie Q, Ting-xin S. Manufacturing Quality Control and Prediction Based on R Language. Modular Machine Tool & Automatic Manufacturing Technique. 2019;02:127–30.
17.
go back to reference Nosratabadi S, Ardabili S, Lakner Z, Mako C, Mosavi A. Prediction of food production using machine learning algorithms of multilayer perceptron and ANFIS. Agriculture. 2021;11(5):408.CrossRef Nosratabadi S, Ardabili S, Lakner Z, Mako C, Mosavi A. Prediction of food production using machine learning algorithms of multilayer perceptron and ANFIS. Agriculture. 2021;11(5):408.CrossRef
18.
go back to reference Memarzadeh G, Keynia F. A new short-term wind speed forecasting method based on fine-tuned LSTM neural network and optimal input sets. Energy Convers Manage. 2020;213: 112824.CrossRef Memarzadeh G, Keynia F. A new short-term wind speed forecasting method based on fine-tuned LSTM neural network and optimal input sets. Energy Convers Manage. 2020;213: 112824.CrossRef
19.
go back to reference Pala Z, Atici R. Forecasting sunspot time series using deep learning methods. Sol Phys. 2019;294(5):50.CrossRef Pala Z, Atici R. Forecasting sunspot time series using deep learning methods. Sol Phys. 2019;294(5):50.CrossRef
20.
go back to reference Pak U, Ma J, Ryu U, Ryom K, Juhyok U, Pak K, Pak C. Deep learning-based PM2. 5 prediction considering the spatiotemporal correlations: A case study of Beijing, China. Science of The Total Environment. 2020; 699:133561. Pak U, Ma J, Ryu U, Ryom K, Juhyok U, Pak K, Pak C. Deep learning-based PM2. 5 prediction considering the spatiotemporal correlations: A case study of Beijing, China. Science of The Total Environment. 2020; 699:133561.
21.
go back to reference Carmona Benitez RB, Carmona Paredes RB, Lodewijks G, Nabais JL. Damp trend Grey Model forecasting method for airline industry. Expert Syst Appl. 2013;40(12):4915–21.CrossRef Carmona Benitez RB, Carmona Paredes RB, Lodewijks G, Nabais JL. Damp trend Grey Model forecasting method for airline industry. Expert Syst Appl. 2013;40(12):4915–21.CrossRef
22.
go back to reference Livieris IE, Pintelas E, Pintelas P. A CNN–LSTM model for gold price time-series forecasting. Neural Comput Appl. 2020;32:17351–60.CrossRef Livieris IE, Pintelas E, Pintelas P. A CNN–LSTM model for gold price time-series forecasting. Neural Comput Appl. 2020;32:17351–60.CrossRef
23.
go back to reference Bridge JA, Greenhouse JB, Ruch D, Stevens J, Ackerman J, Sheftall AH, Horowitz LM, Kelleher KJ, Campo JV. Association Between the Release of Netflix’s 13 Reasons Why and Suicide Rates in the United States: An Interrupted Time Series Analysis. J Am Acad Child Adolesc Psychiatry. 2020;59(2):236–43.CrossRefPubMed Bridge JA, Greenhouse JB, Ruch D, Stevens J, Ackerman J, Sheftall AH, Horowitz LM, Kelleher KJ, Campo JV. Association Between the Release of Netflix’s 13 Reasons Why and Suicide Rates in the United States: An Interrupted Time Series Analysis. J Am Acad Child Adolesc Psychiatry. 2020;59(2):236–43.CrossRefPubMed
24.
go back to reference Chimmula VKR, Zhang L. Time series forecasting of COVID-19 transmission in Canada using LSTM networks. Chaos, Solitons Fractals. 2020;135: 109864.CrossRefPubMed Chimmula VKR, Zhang L. Time series forecasting of COVID-19 transmission in Canada using LSTM networks. Chaos, Solitons Fractals. 2020;135: 109864.CrossRefPubMed
25.
go back to reference Vollset SE, Goren E, Yuan C-W, Cao J, Smith AE, Hsiao T, Bisignano C, Azhar GS, Castro E, Chalek J. Fertility, mortality, migration, and population scenarios for 195 countries and territories from 2017 to 2100: a forecasting analysis for the Global Burden of Disease Study. The Lancet. 2020;396(10258):1285–306.CrossRef Vollset SE, Goren E, Yuan C-W, Cao J, Smith AE, Hsiao T, Bisignano C, Azhar GS, Castro E, Chalek J. Fertility, mortality, migration, and population scenarios for 195 countries and territories from 2017 to 2100: a forecasting analysis for the Global Burden of Disease Study. The Lancet. 2020;396(10258):1285–306.CrossRef
26.
go back to reference Khan MAB, Hashim MJ, King JK, Govender RD, Mustafa H, Al KJ. Epidemiology of type 2 diabetes–global burden of disease and forecasted trends. Journal of epidemiology and global health. 2020;10(1):107.CrossRefPubMedPubMedCentral Khan MAB, Hashim MJ, King JK, Govender RD, Mustafa H, Al KJ. Epidemiology of type 2 diabetes–global burden of disease and forecasted trends. Journal of epidemiology and global health. 2020;10(1):107.CrossRefPubMedPubMedCentral
27.
go back to reference Guo-guang H, Yu L, Shou-feng M. Discussion on Short-Term Traffic Flow Forecasting Methods Based on Mathematical Models. Systems Engineering-Theory & Practice. 2000;12:51–6. Guo-guang H, Yu L, Shou-feng M. Discussion on Short-Term Traffic Flow Forecasting Methods Based on Mathematical Models. Systems Engineering-Theory & Practice. 2000;12:51–6.
28.
go back to reference Daniyal M, Tawiah K, Muhammadullah S, Opoku-Ameyaw K. Comparison of Conventional Modeling Techniques with the Neural Network Autoregressive Model (NNAR): Application to COVID-19 Data. Journal of Healthcare Engineering. 2022; 2022. Daniyal M, Tawiah K, Muhammadullah S, Opoku-Ameyaw K. Comparison of Conventional Modeling Techniques with the Neural Network Autoregressive Model (NNAR): Application to COVID-19 Data. Journal of Healthcare Engineering. 2022; 2022.
29.
go back to reference Shan W, Yi-han L, Mei-yang G, Guo-rong W, Qing-Wu J, Nai-qing Z, Ying-jie Z. Time Series Analysis of Hepatitis E Incidence in China. Chinese Journal of Health Statistics. 2012;29(06):808–11. Shan W, Yi-han L, Mei-yang G, Guo-rong W, Qing-Wu J, Nai-qing Z, Ying-jie Z. Time Series Analysis of Hepatitis E Incidence in China. Chinese Journal of Health Statistics. 2012;29(06):808–11.
31.
go back to reference Xue-feng H. Issues in the Prevention and Control of the COVID-19 Epidemic. Sociological Review of China. 2020;8(02):8–12. Xue-feng H. Issues in the Prevention and Control of the COVID-19 Epidemic. Sociological Review of China. 2020;8(02):8–12.
Metadata
Title
Collateral effects of COVID-19 countermeasures on hepatitis E incidence pattern: a case study of china based on time series models
Authors
Yajun Qin
Haiyang Peng
Jinhao Li
Jianping Gong
Publication date
01-12-2024
Publisher
BioMed Central
Published in
BMC Infectious Diseases / Issue 1/2024
Electronic ISSN: 1471-2334
DOI
https://doi.org/10.1186/s12879-024-09243-x

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