IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v10y2022i10p1714-d817573.html
   My bibliography  Save this article

A Novel Stacking-Based Deterministic Ensemble Model for Infectious Disease Prediction

Author

Listed:
  • Asmita Mahajan

    (Indian Institute of Technology Roorkee, Roorkee 247667, India)

  • Nonita Sharma

    (Department of Information Technology, IGDTUW Delhi, New Delhi 110006, India)

  • Silvia Aparicio-Obregon

    (Faculty of Social Sciences and Humanities, Universidad Europea del Atlántico, C/Isabel Torres 21, 39011 Santander, Spain
    Faculty of Engineering, Universidade Internacional do Cuanza, Estrada Nacional, 250, Bairro Kaluapanda, Cuito-Bié 250, Angola)

  • Hashem Alyami

    (Department of Computer Science, College of Computers and Information Technology, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia)

  • Abdullah Alharbi

    (Department of Information Technology, College of Computers and Information Technology, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia)

  • Divya Anand

    (School of Computer Science and Engineering, Lovely Professional University, Phagwara 144411, India
    Higher Polytechnic School, Universidad Europea del Atlántico, C/Isabel Torres 21, 39011 Santander, Spain)

  • Manish Sharma

    (Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura 140401, India)

  • Nitin Goyal

    (Computer Science Engineering Department, Shri Vishwakarma Skill University, Palwal 121102, India)

Abstract

Infectious Disease Prediction aims to anticipate the aspects of both seasonal epidemics and future pandemics. However, a single model will most likely not capture all the dataset’s patterns and qualities. Ensemble learning combines multiple models to obtain a single prediction that uses the qualities of each model. This study aims to develop a stacked ensemble model to accurately predict the future occurrences of infectious diseases viewed at some point in time as epidemics, namely, dengue, influenza, and tuberculosis. The main objective is to enhance the prediction performance of the proposed model by reducing prediction errors. Autoregressive integrated moving average, exponential smoothing, and neural network autoregression are applied to the disease dataset individually. The gradient boosting model combines the regress values of the above three statistical models to obtain an ensemble model. The results conclude that the forecasting precision of the proposed stacked ensemble model is better than that of the standard gradient boosting model. The ensemble model reduces the prediction errors, root-mean-square error, for the dengue, influenza, and tuberculosis dataset by approximately 30%, 24%, and 25%, respectively.

Suggested Citation

  • Asmita Mahajan & Nonita Sharma & Silvia Aparicio-Obregon & Hashem Alyami & Abdullah Alharbi & Divya Anand & Manish Sharma & Nitin Goyal, 2022. "A Novel Stacking-Based Deterministic Ensemble Model for Infectious Disease Prediction," Mathematics, MDPI, vol. 10(10), pages 1-15, May.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:10:p:1714-:d:817573
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/10/10/1714/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/10/10/1714/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Evan L Ray & Nicholas G Reich, 2018. "Prediction of infectious disease epidemics via weighted density ensembles," PLOS Computational Biology, Public Library of Science, vol. 14(2), pages 1-23, February.
    2. Hyndman, Rob J. & Koehler, Anne B. & Snyder, Ralph D. & Grose, Simone, 2002. "A state space framework for automatic forecasting using exponential smoothing methods," International Journal of Forecasting, Elsevier, vol. 18(3), pages 439-454.
    3. Xingyu Zhang & Tao Zhang & Alistair A Young & Xiaosong Li, 2014. "Applications and Comparisons of Four Time Series Models in Epidemiological Surveillance Data," PLOS ONE, Public Library of Science, vol. 9(2), pages 1-16, February.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Yunlong Ding & Di-Rong Chen, 2023. "Optimization Based Layer-Wise Pruning Threshold Method for Accelerating Convolutional Neural Networks," Mathematics, MDPI, vol. 11(15), pages 1-13, July.

    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. Barrow, Devon & Kourentzes, Nikolaos, 2018. "The impact of special days in call arrivals forecasting: A neural network approach to modelling special days," European Journal of Operational Research, Elsevier, vol. 264(3), pages 967-977.
    2. Hyndman, Rob J. & Ahmed, Roman A. & Athanasopoulos, George & Shang, Han Lin, 2011. "Optimal combination forecasts for hierarchical time series," Computational Statistics & Data Analysis, Elsevier, vol. 55(9), pages 2579-2589, September.
    3. Kourentzes, Nikolaos & Petropoulos, Fotios & Trapero, Juan R., 2014. "Improving forecasting by estimating time series structural components across multiple frequencies," International Journal of Forecasting, Elsevier, vol. 30(2), pages 291-302.
    4. de Silva, Ashton J, 2010. "Forecasting Australian Macroeconomic variables, evaluating innovations state space approaches," MPRA Paper 27411, University Library of Munich, Germany.
    5. Pawlikowski, Maciej & Chorowska, Agata, 2020. "Weighted ensemble of statistical models," International Journal of Forecasting, Elsevier, vol. 36(1), pages 93-97.
    6. Alysha M De Livera, 2010. "Automatic forecasting with a modified exponential smoothing state space framework," Monash Econometrics and Business Statistics Working Papers 10/10, Monash University, Department of Econometrics and Business Statistics.
    7. Spiliotis, Evangelos & Makridakis, Spyros & Kaltsounis, Anastasios & Assimakopoulos, Vassilios, 2021. "Product sales probabilistic forecasting: An empirical evaluation using the M5 competition data," International Journal of Production Economics, Elsevier, vol. 240(C).
    8. Barrow, Devon K., 2016. "Forecasting intraday call arrivals using the seasonal moving average method," Journal of Business Research, Elsevier, vol. 69(12), pages 6088-6096.
    9. Aye, Goodness C. & Balcilar, Mehmet & Gupta, Rangan & Majumdar, Anandamayee, 2015. "Forecasting aggregate retail sales: The case of South Africa," International Journal of Production Economics, Elsevier, vol. 160(C), pages 66-79.
    10. Niematallah Elamin & Mototsugu Fukushige, 2016. "Forecasting extreme seasonal tourism demand," Discussion Papers in Economics and Business 16-23, Osaka University, Graduate School of Economics.
    11. Hyndman, Rob J. & Khandakar, Yeasmin, 2008. "Automatic Time Series Forecasting: The forecast Package for R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 27(i03).
    12. Aviral Kumar Tiwari & Claudiu T Albulescu & Phouphet Kyophilavong, 2014. "A comparison of different forecasting models of the international trade in India," Economics Bulletin, AccessEcon, vol. 34(1), pages 420-429.
    13. Meira, Erick & Cyrino Oliveira, Fernando Luiz & de Menezes, Lilian M., 2022. "Forecasting natural gas consumption using Bagging and modified regularization techniques," Energy Economics, Elsevier, vol. 106(C).
    14. Kang, Yanfei & Hyndman, Rob J. & Smith-Miles, Kate, 2017. "Visualising forecasting algorithm performance using time series instance spaces," International Journal of Forecasting, Elsevier, vol. 33(2), pages 345-358.
    15. Fildes, Robert & Petropoulos, Fotios, 2015. "Simple versus complex selection rules for forecasting many time series," Journal of Business Research, Elsevier, vol. 68(8), pages 1692-1701.
    16. Huber, Jakob & Stuckenschmidt, Heiner, 2020. "Daily retail demand forecasting using machine learning with emphasis on calendric special days," International Journal of Forecasting, Elsevier, vol. 36(4), pages 1420-1438.
    17. Bergmeir, Christoph & Hyndman, Rob J. & Benítez, José M., 2016. "Bagging exponential smoothing methods using STL decomposition and Box–Cox transformation," International Journal of Forecasting, Elsevier, vol. 32(2), pages 303-312.
    18. E. Vercher & A. Corberán-Vallet & J. Segura & J. Bermúdez, 2012. "Initial conditions estimation for improving forecast accuracy in exponential smoothing," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 20(2), pages 517-533, July.
    19. Jerelyn Co & Jason Allan Tan & Ma. Regina Justina Estuar & Kennedy Espina, 2017. "Dengue Spread Modeling in the Absence of Sufficient Epidemiological Parameters: Comparison of SARIMA and SVM Time Series Models," Working papers Conference proceedings The Future of Ethics, Education and Research, October 16-17, 2017 22, Research Association for Interdisciplinary Studies.
    20. Fantazzini, Dean, 2020. "Short-term forecasting of the COVID-19 pandemic using Google Trends data: Evidence from 158 countries," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 59, pages 33-54.

    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:jmathe:v:10:y:2022:i:10:p:1714-:d:817573. 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.