IDEAS home Printed from https://ideas.repec.org/a/eee/chsofr/v138y2020ics0960077920303416.html
   My bibliography  Save this article

A python based support vector regression model for prediction of COVID19 cases in India

Author

Listed:
  • Parbat, Debanjan
  • Chakraborty, Monisha

Abstract

The proposed work utilizes support vector regression model to predict the number of total number of deaths, recovered cases, cumulative number of confirmed cases and number of daily cases. The data is collected for the time period of 1st March,2020 to 30th April,2020 (61 Days). The total number of cases as on 30th April is found to be 35043 confirmed cases with 1147 total deaths and 8889 recovered patients. The model has been developed in Python 3.6.3 to obtain the predicted values of aforementioned cases till 30th June,2020. The proposed methodology is based on prediction of values using support vector regression model with Radial Basis Function as the kernel and 10% confidence interval for the curve fitting. The data has been split into train and test set with test size 40% and training 60%. The model performance parameters are calculated as mean square error, root mean square error, regression score and percentage accuracy. The model has above 97% accuracy in predicting deaths, recovered, cumulative number of confirmed cases and 87% accuracy in predicting daily new cases. The results suggest a Gaussian decrease of the number of cases and could take another 3 to 4 months to come down the minimum level with no new cases being reported. The method is very efficient and has higher accuracy than linear or polynomial regression.

Suggested Citation

  • Parbat, Debanjan & Chakraborty, Monisha, 2020. "A python based support vector regression model for prediction of COVID19 cases in India," Chaos, Solitons & Fractals, Elsevier, vol. 138(C).
    • Handle: RePEc:eee:chsofr:v:138:y:2020:i:c:s0960077920303416
    DOI: 10.1016/j.chaos.2020.109942
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960077920303416
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.chaos.2020.109942?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Zhang, Xiaolei & Ma, Renjun & Wang, Lin, 2020. "Predicting turning point, duration and attack rate of COVID-19 outbreaks in major Western countries," Chaos, Solitons & Fractals, Elsevier, vol. 135(C).
    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. Kumar, Anand & Priya, Bhawna & Srivastava, Samir K., 2021. "Response to the COVID-19: Understanding implications of government lockdown policies," Journal of Policy Modeling, Elsevier, vol. 43(1), pages 76-94.
    2. ArunKumar, K.E. & Kalaga, Dinesh V. & Kumar, Ch. Mohan Sai & Kawaji, Masahiro & Brenza, Timothy M, 2021. "Forecasting of COVID-19 using deep layer Recurrent Neural Networks (RNNs) with Gated Recurrent Units (GRUs) and Long Short-Term Memory (LSTM) cells," Chaos, Solitons & Fractals, Elsevier, vol. 146(C).
    3. Parisa Aberi & Rezgar Arabzadeh & Heribert Insam & Rudolf Markt & Markus Mayr & Norbert Kreuzinger & Wolfgang Rauch, 2021. "Quest for Optimal Regression Models in SARS-CoV-2 Wastewater Based Epidemiology," IJERPH, MDPI, vol. 18(20), pages 1-17, October.
    4. Simone Gitto & Carmela Di Mauro & Alessandro Ancarani & Paolo Mancuso, 2021. "Forecasting national and regional level intensive care unit bed demand during COVID-19: The case of Italy," PLOS ONE, Public Library of Science, vol. 16(2), pages 1-16, February.
    5. Khan, Junaid Iqbal & Ullah, Farman & Lee, Sungchang, 2022. "Attention based parameter estimation and states forecasting of COVID-19 pandemic using modified SIQRD Model," Chaos, Solitons & Fractals, Elsevier, vol. 165(P2).
    6. 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.
    7. Xiaojin Xie & Kangyang Luo & Zhixiang Yin & Guoqiang Wang, 2021. "Nonlinear Combinational Dynamic Transmission Rate Model and Its Application in Global COVID-19 Epidemic Prediction and Analysis," Mathematics, MDPI, vol. 9(18), pages 1-17, September.
    8. Shahid, Farah & Zameer, Aneela & Muneeb, Muhammad, 2020. "Predictions for COVID-19 with deep learning models of LSTM, GRU and Bi-LSTM," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).

    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. Zahra Dehghan Shabani & Rouhollah Shahnazi, 2020. "Spatial distribution dynamics and prediction of COVID‐19 in Asian countries: spatial Markov chain approach," Regional Science Policy & Practice, Wiley Blackwell, vol. 12(6), pages 1005-1025, December.
    2. Singhal, Amit & Singh, Pushpendra & Lall, Brejesh & Joshi, Shiv Dutt, 2020. "Modeling and prediction of COVID-19 pandemic using Gaussian mixture model," Chaos, Solitons & Fractals, Elsevier, vol. 138(C).
    3. Luca Bonacini & Giovanni Gallo & Fabrizio Patriarca, 2021. "Identifying policy challenges of COVID-19 in hardly reliable data and judging the success of lockdown measures," Journal of Population Economics, Springer;European Society for Population Economics, vol. 34(1), pages 275-301, January.
    4. Amaral, Marco A. & Oliveira, Marcelo M. de & Javarone, Marco A., 2021. "An epidemiological model with voluntary quarantine strategies governed by evolutionary game dynamics," Chaos, Solitons & Fractals, Elsevier, vol. 143(C).
    5. Swapnarekha, H. & Behera, Himansu Sekhar & Nayak, Janmenjoy & Naik, Bighnaraj, 2020. "Role of intelligent computing in COVID-19 prognosis: A state-of-the-art review," Chaos, Solitons & Fractals, Elsevier, vol. 138(C).
    6. Huang, Chiou-Jye & Shen, Yamin & Kuo, Ping-Huan & Chen, Yung-Hsiang, 2022. "Novel spatiotemporal feature extraction parallel deep neural network for forecasting confirmed cases of coronavirus disease 2019," Socio-Economic Planning Sciences, Elsevier, vol. 80(C).
    7. Koutsellis, Themistoklis & Nikas, Alexandros, 2020. "A predictive model and country risk assessment for COVID-19: An application of the Limited Failure Population concept," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    8. 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.
    9. Sinitsyn, E. V. & Tolmachev, A. V. & Ovchinnikov, A. S., 2020. "Socio-economic factors in the spread of SARS-COV-2 across Russian regions," R-Economy, Ural Federal University, Graduate School of Economics and Management, vol. 6(3), pages 129-145.
    10. Alaeddine Mihoub & Hosni Snoun & Moez Krichen & Montassar Kahia & Riadh Bel Hadj Salah, 2020. "Predicting COVID-19 Spread Level using Socio-Economic Indicators and Machine Learning Techniques," Post-Print hal-03002886, HAL.
    11. Pelinovsky, Efim & Kurkin, Andrey & Kurkina, Oxana & Kokoulina, Maria & Epifanova, Anastasia, 2020. "Logistic equation and COVID-19," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    12. Michał Wieczorek & Jakub Siłka & Dawid Połap & Marcin Woźniak & Robertas Damaševičius, 2020. "Real-time neural network based predictor for cov19 virus spread," PLOS ONE, Public Library of Science, vol. 15(12), pages 1-18, December.
    13. Rafael Pérez Abreu C. & Samantha Estrada & Héctor de-la-Torre-Gutiérrez, 2021. "A Two-Step Polynomial and Nonlinear Growth Approach for Modeling COVID-19 Cases in Mexico," Mathematics, MDPI, vol. 9(18), pages 1-18, September.
    14. Milad Haghani & Michiel C. J. Bliemer, 2020. "Covid-19 pandemic and the unprecedented mobilisation of scholarly efforts prompted by a health crisis: Scientometric comparisons across SARS, MERS and 2019-nCoV literature," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(3), pages 2695-2726, December.
    15. Gregory L Watson & Di Xiong & Lu Zhang & Joseph A Zoller & John Shamshoian & Phillip Sundin & Teresa Bufford & Anne W Rimoin & Marc A Suchard & Christina M Ramirez, 2021. "Pandemic velocity: Forecasting COVID-19 in the US with a machine learning & Bayesian time series compartmental model," PLOS Computational Biology, Public Library of Science, vol. 17(3), pages 1-20, March.
    16. da Silva, Ramon Gomes & Ribeiro, Matheus Henrique Dal Molin & Mariani, Viviana Cocco & Coelho, Leandro dos Santos, 2020. "Forecasting Brazilian and American COVID-19 cases based on artificial intelligence coupled with climatic exogenous variables," Chaos, Solitons & Fractals, Elsevier, vol. 139(C).
    17. Maher Abida & Emna Mnif, 2023. "Investor Attention in Cryptocurrency Markets: Examining the Effects of Vaccination and COVID-19 Spread through a Wavelet Approach," International Journal of Economics and Financial Issues, Econjournals, vol. 13(5), pages 43-51, September.
    18. Dong-Her Shih & Ting-Wei Wu & Ming-Hung Shih & Min-Jui Yang & David C. Yen, 2022. "A Novel βSA Ensemble Model for Forecasting the Number of Confirmed COVID-19 Cases in the US," Mathematics, MDPI, vol. 10(5), pages 1-15, March.
    19. Vinod, Dasari Naga & Prabaharan, S.R.S., 2020. "Data science and the role of Artificial Intelligence in achieving the fast diagnosis of Covid-19," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    20. Fredj, Haifa Ben & Chérif, Farouk, 2020. "Novel Corona virus disease infection in Tunisia: Mathematical model and the impact of the quarantine strategy," Chaos, Solitons & Fractals, Elsevier, vol. 138(C).

    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:eee:chsofr:v:138:y:2020:i:c:s0960077920303416. 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: Thayer, Thomas R. (email available below). General contact details of provider: https://www.journals.elsevier.com/chaos-solitons-and-fractals .

    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.