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Optimized Forecasting Method for Weekly Influenza Confirmed Cases

Author

Listed:
  • Mohammed A. A. Al-qaness

    (State Key Laboratory for Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China)

  • Ahmed A. Ewees

    (Department of e-Systems, University of Bisha, Bisha 61922, Saudi Arabia
    Department of Computer, Damietta University, Damietta 34517, Egypt)

  • Hong Fan

    (State Key Laboratory for Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China)

  • Mohamed Abd Elaziz

    (Department of Mathematics, Faculty of Science, Zagazig University, Zagazig 44519, Egypt)

Abstract

Influenza epidemic is a serious threat to the entire world, which causes thousands of death every year and can be considered as a public health emergency that needs to be more addressed and investigated. Forecasting influenza incidences or confirmed cases is very important to do the necessary policies and plans for governments and health organizations. In this paper, we present an enhanced adaptive neuro-fuzzy inference system (ANFIS) to forecast the weekly confirmed influenza cases in China and the USA using official datasets. To overcome the limitations of the original ANFIS, we use two metaheuristics, called flower pollination algorithm (FPA) and sine cosine algorithm (SCA), to enhance the prediction of the ANFIS. The proposed FPASCA-ANFIS is evaluated using two datasets collected from the CDC and WHO websites. Furthermore, it was compared to some previous state-of-the-art approaches. Experimental results confirmed that the FPASCA-ANFIS outperformed the compared methods using variant measures, including RMSRE, MAPE, MAE, and R 2 .

Suggested Citation

  • Mohammed A. A. Al-qaness & Ahmed A. Ewees & Hong Fan & Mohamed Abd Elaziz, 2020. "Optimized Forecasting Method for Weekly Influenza Confirmed Cases," IJERPH, MDPI, vol. 17(10), pages 1-12, May.
    • Handle: RePEc:gam:jijerp:v:17:y:2020:i:10:p:3510-:d:359422
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    References listed on IDEAS

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    Cited by:

    1. Qiang Wang & Min Su & Min Zhang & Rongrong Li, 2021. "Integrating Digital Technologies and Public Health to Fight Covid-19 Pandemic: Key Technologies, Applications, Challenges and Outlook of Digital Healthcare," IJERPH, MDPI, vol. 18(11), pages 1-50, June.
    2. Mohammed A. A. Al-qaness & Ahmed A. Ewees & Hong Fan & Laith Abualigah & Mohamed Abd Elaziz, 2020. "Marine Predators Algorithm for Forecasting Confirmed Cases of COVID-19 in Italy, USA, Iran and Korea," IJERPH, MDPI, vol. 17(10), pages 1-14, May.
    3. Bader S. Al-Anzi & Mohammad Alenizi & Jehad Al Dallal & Frage Lhadi Abookleesh & Aman Ullah, 2020. "An Overview of the World Current and Future Assessment of Novel COVID-19 Trajectory, Impact, and Potential Preventive Strategies at Healthcare Settings," IJERPH, MDPI, vol. 17(19), pages 1-19, September.
    4. Sergio Contreras-Espinoza & Francisco Novoa-Muñoz & Szabolcs Blazsek & Pedro Vidal & Christian Caamaño-Carrillo, 2022. "COVID-19 Active Case Forecasts in Latin American Countries Using Score-Driven Models," Mathematics, MDPI, vol. 11(1), pages 1-17, December.
    5. Jelena Musulin & Sandi Baressi Šegota & Daniel Štifanić & Ivan Lorencin & Nikola Anđelić & Tijana Šušteršič & Anđela Blagojević & Nenad Filipović & Tomislav Ćabov & Elitza Markova-Car, 2021. "Application of Artificial Intelligence-Based Regression Methods in the Problem of COVID-19 Spread Prediction: A Systematic Review," IJERPH, MDPI, vol. 18(8), pages 1-39, April.
    6. Tasneem Kamal Aldeen Muhamed & Mona Yahya Salim Alfefi & Nahla Morad, 2022. "Analysis Impact of Coronavirus in the Kingdom of Saudi Arabia by Using the Artificial Neural Network," Eximia Journal, Plus Communication Consulting SRL, vol. 5(1), pages 146-157, July.
    7. Yu-Feng Zhao & Ming-Huan Shou & Zheng-Xin Wang, 2020. "Prediction of the Number of Patients Infected with COVID-19 Based on Rolling Grey Verhulst Models," IJERPH, MDPI, vol. 17(12), pages 1-20, June.
    8. Dabiah Alboaneen & Bernardi Pranggono & Dhahi Alshammari & Nourah Alqahtani & Raja Alyaffer, 2020. "Predicting the Epidemiological Outbreak of the Coronavirus Disease 2019 (COVID-19) in Saudi Arabia," IJERPH, MDPI, vol. 17(12), pages 1-10, June.
    9. Tian-Shyug Lee & I-Fei Chen & Ting-Jen Chang & Chi-Jie Lu, 2020. "Forecasting Weekly Influenza Outpatient Visits Using a Two-Dimensional Hierarchical Decision Tree Scheme," IJERPH, MDPI, vol. 17(13), pages 1-15, July.
    10. Godahewa, Rakshitha & Bergmeir, Christoph & Webb, Geoffrey I. & Montero-Manso, Pablo, 2023. "An accurate and fully-automated ensemble model for weekly time series forecasting," International Journal of Forecasting, Elsevier, vol. 39(2), pages 641-658.
    11. Sheikh Safiullah & Asadur Rahman & Shameem Ahmad Lone & S. M. Suhail Hussain & Taha Selim Ustun, 2022. "Novel COVID-19 Based Optimization Algorithm (C-19BOA) for Performance Improvement of Power Systems," Sustainability, MDPI, vol. 14(21), pages 1-27, November.

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