A machine learning forecasting model for COVID-19 pandemic in India

被引:0
|
作者
Sujath, R. [1 ]
Chatterjee, Jyotir Moy [2 ]
Hassanien, Aboul Ella [3 ,4 ]
机构
[1] Vellore Inst Technol, Vellore, Tamil Nadu, India
[2] Lord Buddha Educ Fdn, Kathmandu, Nepal
[3] Cairo Univ, Fac Comp & Artificial Intelligence, Giza, Egypt
[4] Sci Res Grp Egypt SRGE, Giza, Egypt
关键词
COVID-19; Prediction; Linear regression (LR); Multilayer perceptron (MLP); Vector autoregression (VAR); PREDICTION;
D O I
10.1007/s00477-020-01827-8
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Coronavirus disease (COVID-19) is an inflammation disease from a new virus. The disease causes respiratory ailment (like influenza) with manifestations, for example, cold, cough and fever, and in progressively serious cases, the problem in breathing. COVID-2019 has been perceived as a worldwide pandemic and a few examinations are being led utilizing different numerical models to anticipate the likely advancement of this pestilence. These numerical models dependent on different factors and investigations are dependent upon potential inclination. Here, we presented a model that could be useful to predict the spread of COVID-2019. We have performed linear regression, Multilayer perceptron and Vector autoregression method for desire on the COVID-19 Kaggle data to anticipate the epidemiological example of the ailment and pace of COVID-2019 cases in India. Anticipated the potential patterns of COVID-19 effects in India dependent on data gathered from Kaggle. With the common data about confirmed, death and recovered cases across India for over the time length helps in anticipating and estimating the not so distant future. For extra assessment or future perspective, case definition and data combination must be kept up persistently.
引用
收藏
页码:959 / 972
页数:14
相关论文
共 50 条
  • [21] Prediction of the COVID-19 pandemic with Machine Learning Models
    Sruthi, P. Lakshmi
    Raju, K. Butchi
    PROCEEDINGS OF THE 2021 FIFTH INTERNATIONAL CONFERENCE ON I-SMAC (IOT IN SOCIAL, MOBILE, ANALYTICS AND CLOUD) (I-SMAC 2021), 2021, : 474 - 481
  • [22] Significant Applications of Machine Learning for COVID-19 Pandemic
    Kushwaha, Shashi
    Bahl, Shashi
    Bagha, Ashok Kumar
    Parmar, Kulwinder Singh
    Javaid, Mohd
    Haleem, Abid
    Singh, Ravi Pratap
    JOURNAL OF INDUSTRIAL INTEGRATION AND MANAGEMENT-INNOVATION AND ENTREPRENEURSHIP, 2020, 5 (04): : 453 - 479
  • [23] COVID-19 in Iran: Forecasting Pandemic Using Deep Learning
    Kafieh, Rahele
    Arian, Roya
    Saeedizadeh, Narges
    Amini, Zahra
    Serej, Nasim Dadashi
    Minaee, Shervin
    Yadav, Sunil Kumar
    Vaezi, Atefeh
    Rezaei, Nima
    Javanmard, Shaghayegh Haghjooy
    COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE, 2021, 2021
  • [24] Forecasting the daily and cumulative number of cases for the COVID-19 pandemic in India
    Khajanchi, Subhas
    Sarkar, Kankan
    CHAOS, 2020, 30 (07)
  • [25] COVID-19 pandemic in India
    Ram, C. Venkata S.
    Babu, Giridhara R.
    Prabhakaran, Dorairaj.
    EUROPEAN HEART JOURNAL, 2020, 41 (40) : 3874 - 3876
  • [26] Deep Learning-Based Forecasting of COVID-19 in India
    Pillai, Punitha Kumaresa
    Durairaj, Devaraj
    Samivel, Kanthammal
    JOURNAL OF TESTING AND EVALUATION, 2022, 50 (01) : 225 - 242
  • [27] A mathematical model to study the COVID-19 pandemic in India
    Tripathi, Agraj
    Tripathi, Ram Naresh
    Sharma, Dileep
    MODELING EARTH SYSTEMS AND ENVIRONMENT, 2022, 8 (03) : 3047 - 3058
  • [28] A mathematical model to study the COVID-19 pandemic in India
    Agraj Tripathi
    Ram Naresh Tripathi
    Dileep Sharma
    Modeling Earth Systems and Environment, 2022, 8 : 3047 - 3058
  • [29] Evaluating deep learning and machine learning algorithms for forecasting daily pan evaporation during COVID-19 pandemic
    Latif, Sarmad Dashti
    ENVIRONMENT DEVELOPMENT AND SUSTAINABILITY, 2024, 26 (05) : 11729 - 11742
  • [30] Machine Learning and Deep Learning Based Time Series Prediction and Forecasting of Ten Nations’ COVID-19 Pandemic
    Kumar Y.
    Koul A.
    Kaur S.
    Hu Y.-C.
    SN Computer Science, 4 (1)