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Prediction of Epidemic Peak and Infected Cases for COVID-19 Disease in Malaysia, 2020

Author

Listed:
  • Abdallah Alsayed

    (Department of Electrical and Electronic Engineering, Faculty of Engineering, Universiti Putra Malaysia, Serdang 43400, Selangor, Malaysia)

  • Hayder Sadir

    (Department of Computer and Wireless Communication, Faculty of Engineering, Universiti Putra Malaysia, Serdang 43400, Selangor, Malaysia)

  • Raja Kamil

    (Department of Electrical and Electronic Engineering, Faculty of Engineering, Universiti Putra Malaysia, Serdang 43400, Selangor, Malaysia
    Laboratory of Computational Statistics and Operations Research, Institute for Mathematical Research, Universiti Putra Malaysia, Serdang 43400, Selangor, Malaysia)

  • Hasan Sari

    (College of Computer Science and Information Technology, Universiti Tenaga Nasional, Kajang 43000, Malaysia)

Abstract

The coronavirus COVID-19 has recently started to spread rapidly in Malaysia. The number of total infected cases has increased to 3662 on 05 April 2020, leading to the country being placed under lockdown. As the main public concern is whether the current situation will continue for the next few months, this study aims to predict the epidemic peak using the Susceptible–Exposed–Infectious–Recovered (SEIR) model, with incorporation of the mortality cases. The infection rate was estimated using the Genetic Algorithm (GA), while the Adaptive Neuro-Fuzzy Inference System (ANFIS) model was used to provide short-time forecasting of the number of infected cases. The results show that the estimated infection rate is 0.228 ± 0.013, while the basic reproductive number is 2.28 ± 0.13. The epidemic peak of COVID-19 in Malaysia could be reached on 26 July 2020, with an uncertain period of 30 days (12 July–11 August). Possible interventions by the government to reduce the infection rate by 25% over two or three months would delay the epidemic peak by 30 and 46 days, respectively. The forecasting results using the ANFIS model show a low Normalized Root Mean Square Error (NRMSE) of 0.041; a low Mean Absolute Percentage Error (MAPE) of 2.45%; and a high coefficient of determination (R 2 ) of 0.9964. The results also show that an intervention has a great effect on delaying the epidemic peak and a longer intervention period would reduce the epidemic size at the peak. The study provides important information for public health providers and the government to control the COVID-19 epidemic.

Suggested Citation

  • Abdallah Alsayed & Hayder Sadir & Raja Kamil & Hasan Sari, 2020. "Prediction of Epidemic Peak and Infected Cases for COVID-19 Disease in Malaysia, 2020," IJERPH, MDPI, vol. 17(11), pages 1-15, June.
    • Handle: RePEc:gam:jijerp:v:17:y:2020:i:11:p:4076-:d:368558
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    References listed on IDEAS

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    1. Lei Qin & Qiang Sun & Yidan Wang & Ke-Fei Wu & Mingchih Chen & Ben-Chang Shia & Szu-Yuan Wu, 2020. "Prediction of Number of Cases of 2019 Novel Coronavirus (COVID-19) Using Social Media Search Index," IJERPH, MDPI, vol. 17(7), pages 1-14, March.
    2. Mohammadi, Kasra & Shamshirband, Shahaboddin & Kamsin, Amirrudin & Lai, P.C. & Mansor, Zulkefli, 2016. "Identifying the most significant input parameters for predicting global solar radiation using an ANFIS selection procedure," Renewable and Sustainable Energy Reviews, Elsevier, vol. 63(C), pages 423-434.
    3. Hye-Suk Yi & Sangyoung Park & Kwang-Guk An & Keun-Chang Kwak, 2018. "Algal Bloom Prediction Using Extreme Learning Machine Models at Artificial Weirs in the Nakdong River, Korea," IJERPH, MDPI, vol. 15(10), pages 1-20, September.
    4. Xueying Li & Bing Xu & Jeffrey Shaman, 2019. "The Impact of Environmental Transmission and Epidemiological Features on the Geographical Translocation of Highly Pathogenic Avian Influenza Virus," IJERPH, MDPI, vol. 16(11), pages 1-14, May.
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    Cited by:

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    2. Sergej Gričar & Štefan Bojnec, 2022. "Did Human Microbes Affect Tourist Arrivals before the COVID-19 Shock? Pre-Effect Forecasting Model for Slovenia," IJERPH, MDPI, vol. 19(20), pages 1-15, October.

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