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Prediction of epidemic trends in COVID-19 with logistic model and machine learning technics

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  • Wang, Peipei
  • Zheng, Xinqi
  • Li, Jiayang
  • Zhu, Bangren

Abstract

COVID-19 has now had a huge impact in the world, and more than 8 million people in more than 100 countries are infected. To contain its spread, a number of countries published control measures. However, it’s not known when the epidemic will end in global and various countries. Predicting the trend of COVID-19 is an extremely important challenge. We integrate the most updated COVID-19 epidemiological data before June 16, 2020 into the Logistic model to fit the cap of epidemic trend, and then feed the cap value into FbProphet model, a machine learning based time series prediction model to derive the epidemic curve and predict the trend of the epidemic. Three significant points are summarized from our modeling results for global, Brazil, Russia, India, Peru and Indonesia. Under mathematical estimation, the global outbreak will peak in late October, with an estimated 14.12 million people infected cumulatively.

Suggested Citation

  • Wang, Peipei & Zheng, Xinqi & Li, Jiayang & Zhu, Bangren, 2020. "Prediction of epidemic trends in COVID-19 with logistic model and machine learning technics," Chaos, Solitons & Fractals, Elsevier, vol. 139(C).
    • Handle: RePEc:eee:chsofr:v:139:y:2020:i:c:s0960077920304550
    DOI: 10.1016/j.chaos.2020.110058
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    References listed on IDEAS

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    Cited by:

    1. Gaetano Perone, 2022. "Using the SARIMA Model to Forecast the Fourth Global Wave of Cumulative Deaths from COVID-19: Evidence from 12 Hard-Hit Big Countries," Econometrics, MDPI, vol. 10(2), pages 1-23, April.
    2. Wang, Peipei & Zheng, Xinqi & Ai, Gang & Liu, Dongya & Zhu, Bangren, 2020. "Time series prediction for the epidemic trends of COVID-19 using the improved LSTM deep learning method: Case studies in Russia, Peru and Iran," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    3. Ballı, Serkan, 2021. "Data analysis of Covid-19 pandemic and short-term cumulative case forecasting using machine learning time series methods," Chaos, Solitons & Fractals, Elsevier, vol. 142(C).
    4. Munir Ahmad & Nadeem Akhtar & Gul Jabeen & Muhammad Irfan & Muhammad Khalid Anser & Haitao Wu & Cem Işık, 2021. "Intention-Based Critical Factors Affecting Willingness to Adopt Novel Coronavirus Prevention in Pakistan: Implications for Future Pandemics," IJERPH, MDPI, vol. 18(11), pages 1-28, June.
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    10. Matvey Pavlyutin & Marina Samoyavcheva & Rasul Kochkarov & Ekaterina Pleshakova & Sergey Korchagin & Timur Gataullin & Petr Nikitin & Mohiniso Hidirova, 2022. "COVID-19 Spread Forecasting, Mathematical Methods vs. Machine Learning, Moscow Case," Mathematics, MDPI, vol. 10(2), pages 1-19, January.
    11. Tayarani N., Mohammad-H., 2021. "Applications of artificial intelligence in battling against covid-19: A literature review," Chaos, Solitons & Fractals, Elsevier, vol. 142(C).
    12. Fadaki, Masih & Asadikia, Atie, 2024. "Augmenting Monte Carlo Tree Search for managing service level agreements," International Journal of Production Economics, Elsevier, vol. 271(C).
    13. Paul, Ayan & Reja, Selim & Kundu, Sayani & Bhattacharya, Sabyasachi, 2021. "COVID-19 pandemic models revisited with a new proposal: Plenty of epidemiological models outcast the simple population dynamics solution," Chaos, Solitons & Fractals, Elsevier, vol. 144(C).
    14. Frederik Seeup Hass & Jamal Jokar Arsanjani, 2021. "The Geography of the Covid-19 Pandemic: A Data-Driven Approach to Exploring Geographical Driving Forces," IJERPH, MDPI, vol. 18(6), pages 1-19, March.
    15. Matouk, A.E., 2020. "Complex dynamics in susceptible-infected models for COVID-19 with multi-drug resistance," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    16. Dalton Garcia Borges de Souza & Erivelton Antonio dos Santos & Francisco Tarcísio Alves Júnior & Mariá Cristina Vasconcelos Nascimento, 2021. "On Comparing Cross-Validated Forecasting Models with a Novel Fuzzy-TOPSIS Metric: A COVID-19 Case Study," Sustainability, MDPI, vol. 13(24), pages 1-25, December.
    17. Kim-Hung Pho & Michael McAleer, 2021. "Specification and Estimation of a Logistic Function, with Applications in the Sciences and Social Sciences," Advances in Decision Sciences, Asia University, Taiwan, vol. 25(2), pages 74-104, June.
    18. 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.
    19. Ben R. Craig & Tom Phelan & Jan-Peter Siedlarek & Jared Steinberg, 2021. "Two Approaches to Predicting the Path of the COVID-19 Pandemic: Is One Better?," Economic Commentary, Federal Reserve Bank of Cleveland, vol. 2021(10), pages 1-8, April.
    20. Se Yoon Lee, 2022. "Bayesian Nonlinear Models for Repeated Measurement Data: An Overview, Implementation, and Applications," Mathematics, MDPI, vol. 10(6), pages 1-51, March.
    21. Pawan Kumar Singh & Anushka Chouhan & Rajiv Kumar Bhatt & Ravi Kiran & Ansari Saleh Ahmar, 2022. "Implementation of the SutteARIMA method to predict short-term cases of stock market and COVID-19 pandemic in USA," Quality & Quantity: International Journal of Methodology, Springer, vol. 56(4), pages 2023-2033, August.
    22. Askar Akaev & Alexander I. Zvyagintsev & Askar Sarygulov & Tessaleno Devezas & Andrea Tick & Yuri Ichkitidze, 2022. "Growth Recovery and COVID-19 Pandemic Model: Comparative Analysis for Selected Emerging Economies," Mathematics, MDPI, vol. 10(19), pages 1-18, October.
    23. Md Rashidul Hasan & Muntasir A Kabir & Rezoan A Shuvro & Pankaz Das, 2022. "A Comparative Study on Forecasting of Retail Sales," Papers 2203.06848, arXiv.org.

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