{
    "abstract": "The COVID-19 pandemic has put a huge challenge on the Indian health infrastructure. With a larger number of people getting affected during the second wave, hospitals were overburdened, running out of supplies and oxygen. Hence, predicting new COVID-19 cases, new deaths, and total active cases multiple days in advance can aid better utilization of scarce medical resources and prudent pandemic-related decision-making. The proposed method uses gated recurrent unit networks as the main predicting model. A study is conducted by building four models pre-trained on COVID-19 data from four different countries (United States of America, Brazil, Spain, and Bangladesh) and fine-tuned on India's data. Since the four countries chosen have experienced different types of infection curves, the pre-training provides a transfer learning to the models incorporating diverse situations into account. Each of the four models then gives 7-day ahead predictions using the recursive learning method for the Indian test data. The final prediction comes from an ensemble of the predictions of the different models. This method with two countries, Spain and Bangladesh, is seen to achieve the best performance amongst all the combinations as well as compared to other traditional regression models.",
    "authors": [
        {
            "affiliation": "Technology Innovation Hub (TIH), Indian Statistical Institute, Kolkata, India.",
            "firstname": "Debasrita",
            "initials": "D",
            "lastname": "Chakraborty"
        },
        {
            "affiliation": "Department of Computer Science and Engineering, Jadavpur University, Kolkata, India.",
            "firstname": "Debayan",
            "initials": "D",
            "lastname": "Goswami"
        },
        {
            "affiliation": "Department of Computer Science and Engineering, Jadavpur University, Kolkata, India. susmitaghoshju@gmail.com.",
            "firstname": "Susmita",
            "initials": "S",
            "lastname": "Ghosh"
        },
        {
            "affiliation": "Technology Innovation Hub (TIH), Indian Statistical Institute, Kolkata, India.",
            "firstname": "Ashish",
            "initials": "A",
            "lastname": "Ghosh"
        },
        {
            "affiliation": "Innovative Cognitive Computing (IC2) Research Center, School of Information Technology, King Mongkut's University of Technology Thonburi, Bangkok, Thailand. jonathan@sit.kmutt.ac.th.",
            "firstname": "Jonathan H",
            "initials": "JH",
            "lastname": "Chan"
        },
        {
            "affiliation": "School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, Singapore.",
            "firstname": "Lipo",
            "initials": "L",
            "lastname": "Wang"
        }
    ],
    "conclusions": null,
    "copyrights": "\u00a9 2023. The Author(s).",
    "doi": "10.1038/s41598-023-31737-y\n10.1101/2020.04.09.20058933\n10.3390/app112311423\n10.1007/s00354-022-00176-0\n10.3233/JIFS-219273\n10.3390/jcm9051547\n10.1016/j.ijforecast.2020.10.001\n10.2196/19115\n10.1016/j.chaos.2020.109889\n10.1080/09720529.2020.1784535\n10.1016/j.psep.2020.11.007\n10.1111/exsy.12648\n10.1016/j.chaos.2020.110212\n10.1016/j.psep.2020.10.048\n10.1162/neco.1997.9.8.1735\n10.1038/s41598-021-01119-3\n10.1016/j.psep.2020.05.029\n10.1093/infdis/jiw375\n10.1038/s41598-022-07757-5\n10.1016/j.chaos.2020.109853\n10.1109/59.761912\n10.1016/j.physd.2019.132306\n10.1109/ACCESS.2019.2921578\n10.1016/j.neunet.2019.12.030\n10.1016/j.knosys.2019.05.028\n10.1016/j.procs.2020.03.257\n10.1109/TKDE.2009.191\n10.1186/s40537-016-0043-6\n10.1016/j.irbm.2020.05.003\n10.1109/ACCESS.2020.3016780\n10.1016/j.isatra.2020.12.057\n10.1007/s41666-020-00088-y\n10.1016/j.aej.2022.01.011\n10.1080/00031305.2017.1380080",
    "journal": "Scientific reports",
    "keywords": [],
    "methods": null,
    "publication_date": "2023-04-27",
    "pubmed_id": "37100806\n35730008\n32443871\n33071402\n32391801\n32406395\n33204052\n32839642\n33162687\n9377276\n34741093\n32501368\n28830111\n35273252\n32501370\n32062409\n32837678\n34931154\n33422330\n33426422",
    "results": null,
    "title": "Transfer-recursive-ensemble learning for multi-day COVID-19 prediction in India using recurrent neural networks.",
    "xml": "<Element 'PubmedArticle' at 0x788502ed4e00>"
}