IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v19y2022i10p5910-d814437.html
   My bibliography  Save this article

A Combined Model of SARIMA and Prophet Models in Forecasting AIDS Incidence in Henan Province, China

Author

Listed:
  • Zixiao Luo

    (Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou 450001, China)

  • Xiaocan Jia

    (Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou 450001, China)

  • Junzhe Bao

    (Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou 450001, China)

  • Zhijuan Song

    (Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou 450001, China)

  • Huili Zhu

    (Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou 450001, China)

  • Mengying Liu

    (Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou 450001, China)

  • Yongli Yang

    (Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou 450001, China)

  • Xuezhong Shi

    (Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou 450001, China)

Abstract

Acquired immune deficiency syndrome (AIDS) is a serious public health problem. This study aims to establish a combined model of seasonal autoregressive integrated moving average (SARIMA) and Prophet models based on an L1-norm to predict the incidence of AIDS in Henan province, China. The monthly incidences of AIDS in Henan province from 2012 to 2020 were obtained from the Health Commission of Henan Province. A SARIMA model, a Prophet model, and two combined models were adopted to fit the monthly incidence of AIDS using the data from January 2012 to December 2019. The data from January 2020 to December 2020 was used to verify. The mean square error (MSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) were used to compare the prediction effect among the models. The results showed that the monthly incidence fluctuated from 0.05 to 0.50 per 100,000 individuals, and the monthly incidence of AIDS had a certain periodicity in Henan province. In addition, the prediction effect of the Prophet model was better than SARIMA model, the combined model was better than the single models, and the combined model based on the L1-norm had the best effect values (MSE = 0.0056, MAE = 0.0553, MAPE = 43.5337). This indicated that, compared with the L2-norm, the L1-norm improved the prediction accuracy of the combined model. The combined model of SARIMA and Prophet based on the L1-norm is a suitable method to predict the incidence of AIDS in Henan. Our findings can provide theoretical evidence for the government to formulate policies regarding AIDS prevention.

Suggested Citation

  • Zixiao Luo & Xiaocan Jia & Junzhe Bao & Zhijuan Song & Huili Zhu & Mengying Liu & Yongli Yang & Xuezhong Shi, 2022. "A Combined Model of SARIMA and Prophet Models in Forecasting AIDS Incidence in Henan Province, China," IJERPH, MDPI, vol. 19(10), pages 1-12, May.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:10:p:5910-:d:814437
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/19/10/5910/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/19/10/5910/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Nicholas G Reich & Craig J McGowan & Teresa K Yamana & Abhinav Tushar & Evan L Ray & Dave Osthus & Sasikiran Kandula & Logan C Brooks & Willow Crawford-Crudell & Graham Casey Gibson & Evan Moore & Reb, 2019. "Accuracy of real-time multi-model ensemble forecasts for seasonal influenza in the U.S," PLOS Computational Biology, Public Library of Science, vol. 15(11), pages 1-19, November.
    2. Sean J. Taylor & Benjamin Letham, 2018. "Forecasting at Scale," The American Statistician, Taylor & Francis Journals, vol. 72(1), pages 37-45, January.
    3. Yuling Xia & Chengsong Liao & Dianming Wu & Yanzhuo Liu, 2020. "Dynamic Analysis and Prediction of Food Nitrogen Footprint of Urban and Rural Residents in Shanghai," IJERPH, MDPI, vol. 17(5), pages 1-13, March.
    4. Junyi Lu & Sebastian Meyer, 2020. "Forecasting Flu Activity in the United States: Benchmarking an Endemic-Epidemic Beta Model," IJERPH, MDPI, vol. 17(4), pages 1-13, February.
    5. Xiao Zang & Emanuel Krebs & Linwei Wang & Brandon D. L. Marshall & Reuben Granich & Bruce R. Schackman & Julio S. G. Montaner & Bohdan Nosyk, 2019. "Structural Design and Data Requirements for Simulation Modelling in HIV/AIDS: A Narrative Review," PharmacoEconomics, Springer, vol. 37(10), pages 1219-1239, October.
    6. Teresa K Yamana & Sasikiran Kandula & Jeffrey Shaman, 2017. "Individual versus superensemble forecasts of seasonal influenza outbreaks in the United States," PLOS Computational Biology, Public Library of Science, vol. 13(11), pages 1-17, November.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Jorge-Eusebio Velasco-López & Ramón-Alberto Carrasco & Jesús Serrano-Guerrero & Francisco Chiclana, 2024. "Profiling Social Sentiment in Times of Health Emergencies with Information from Social Networks and Official Statistics," Mathematics, MDPI, vol. 12(6), pages 1-23, March.
    2. Fadaki, Masih & Asadikia, Atie, 2024. "Augmenting Monte Carlo Tree Search for managing service level agreements," International Journal of Production Economics, Elsevier, vol. 271(C).
    3. Michael Vössing & Niklas Kühl & Matteo Lind & Gerhard Satzger, 2022. "Designing Transparency for Effective Human-AI Collaboration," Information Systems Frontiers, Springer, vol. 24(3), pages 877-895, June.
    4. Maghsoodi, Abtin Ijadi, 2023. "Cryptocurrency portfolio allocation using a novel hybrid and predictive big data decision support system," Omega, Elsevier, vol. 115(C).
    5. Miroslav Navratil & Andrea Kolkova, 2019. "Decomposition and Forecasting Time Series in the Business Economy Using Prophet Forecasting Model," Central European Business Review, Prague University of Economics and Business, vol. 2019(4), pages 26-39.
    6. Fadi Kahwash & Basel Barakat & Ahmad Taha & Qammer H. Abbasi & Muhammad Ali Imran, 2021. "Optimising Electrical Power Supply Sustainability Using a Grid-Connected Hybrid Renewable Energy System—An NHS Hospital Case Study," Energies, MDPI, vol. 14(21), pages 1-23, October.
    7. Lorenzo Menculini & Andrea Marini & Massimiliano Proietti & Alberto Garinei & Alessio Bozza & Cecilia Moretti & Marcello Marconi, 2021. "Comparing Prophet and Deep Learning to ARIMA in Forecasting Wholesale Food Prices," Forecasting, MDPI, vol. 3(3), pages 1-19, September.
    8. Romero-Fiances, Irene & Livera, Andreas & Theristis, Marios & Makrides, George & Stein, Joshua S. & Nofuentes, Gustavo & de la Casa, Juan & Georghiou, George E., 2022. "Impact of duration and missing data on the long-term photovoltaic degradation rate estimation," Renewable Energy, Elsevier, vol. 181(C), pages 738-748.
    9. Zhewei Huang & Yawen Yi, 2024. "Short-Term Load Forecasting for Regional Smart Energy Systems Based on Two-Stage Feature Extraction and Hybrid Inverted Transformer," Sustainability, MDPI, vol. 16(17), pages 1-25, September.
    10. Winita Sulandari & Yudho Yudhanto & Sri Subanti & Crisma Devika Setiawan & Riskhia Hapsari & Paulo Canas Rodrigues, 2023. "Comparing the Simple to Complex Automatic Methods with the Ensemble Approach in Forecasting Electrical Time Series Data," Energies, MDPI, vol. 16(22), pages 1-16, November.
    11. Ashish Shrestha & Bishal Ghimire & Francisco Gonzalez-Longatt, 2021. "A Bayesian Model to Forecast the Time Series Kinetic Energy Data for a Power System," Energies, MDPI, vol. 14(11), pages 1-15, June.
    12. Nik Dawson & Sacha Molitorisz & Marian-Andrei Rizoiu & Peter Fray, 2020. "Layoffs, Inequity and COVID-19: A Longitudinal Study of the Journalism Jobs Crisis in Australia from 2012 to 2020," Papers 2008.12459, arXiv.org, revised Feb 2021.
    13. Luyao Zhang & Fan Zhang, 2023. "Understand Waiting Time in Transaction Fee Mechanism: An Interdisciplinary Perspective," Papers 2305.02552, arXiv.org.
    14. Md Jamal Ahmed Shohan & Md Omar Faruque & Simon Y. Foo, 2022. "Forecasting of Electric Load Using a Hybrid LSTM-Neural Prophet Model," Energies, MDPI, vol. 15(6), pages 1-18, March.
    15. Ángel Cuevas & Ramiro Ledo & Enrique M. Quilis, 2021. "Seasonal adjustment of the Spanish sales daily data," SERIEs: Journal of the Spanish Economic Association, Springer;Spanish Economic Association, vol. 12(4), pages 687-708, December.
    16. Sprangers, Olivier & Schelter, Sebastian & de Rijke, Maarten, 2023. "Parameter-efficient deep probabilistic forecasting," International Journal of Forecasting, Elsevier, vol. 39(1), pages 332-345.
    17. Aleksandr N. Grekov & Elena V. Vyshkvarkova & Aleksandr S. Mavrin, 2024. "Forecasting and Anomaly Detection in BEWS: Comparative Study of Theta, Croston, and Prophet Algorithms," Forecasting, MDPI, vol. 6(2), pages 1-14, May.
    18. Odin Foldvik Eikeland & Filippo Maria Bianchi & Harry Apostoleris & Morten Hansen & Yu-Cheng Chiou & Matteo Chiesa, 2021. "Predicting Energy Demand in Semi-Remote Arctic Locations," Energies, MDPI, vol. 14(4), pages 1-17, February.
    19. Jayesh Thaker & Robert Höller, 2022. "A Comparative Study of Time Series Forecasting of Solar Energy Based on Irradiance Classification," Energies, MDPI, vol. 15(8), pages 1-26, April.
    20. Srinka Basu & Sugata Sen, 2023. "COVID 19 Pandemic, Socio-Economic Behaviour and Infection Characteristics: An Inter-Country Predictive Study Using Deep Learning," Computational Economics, Springer;Society for Computational Economics, vol. 61(2), pages 645-676, February.

    More about this item

    Keywords

    AIDS; SARIMA model; Prophet model; L1-norm;
    All these keywords.

    JEL classification:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jijerp:v:19:y:2022:i:10:p:5910-:d:814437. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.