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  1. Article: Time series forecasting of COVID-19 transmission in Canada using LSTM networks.

    Chimmula, Vinay Kumar Reddy / Zhang, Lei

    Chaos, solitons, and fractals

    2020  Volume 135, Page(s) 109864

    Abstract: ... On March ... ...

    Abstract On March 11
    Keywords covid19
    Language English
    Publishing date 2020-05-08
    Publishing country England
    Document type Journal Article
    ZDB-ID 2003919-0
    ISSN 1873-2887 ; 0960-0779
    ISSN (online) 1873-2887
    ISSN 0960-0779
    DOI 10.1016/j.chaos.2020.109864
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article ; Online: Time series forecasting of COVID-19 transmission in Canada using LSTM networks

    Chimmula, Vinay Kumar Reddy / Zhang, Lei

    Chaos, Solitons & Fractals

    2020  Volume 135, Page(s) 109864

    Keywords General Mathematics ; covid19
    Language English
    Publisher Elsevier BV
    Publishing country us
    Document type Article ; Online
    ZDB-ID 2003919-0
    ISSN 1873-2887 ; 0960-0779
    ISSN (online) 1873-2887
    ISSN 0960-0779
    DOI 10.1016/j.chaos.2020.109864
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

    Full text online

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    Order via subito

    This service is chargeable due to the Delivery terms set by subito. Orders including an article and supplementary material will be classified as separate orders. In these cases, fees will be demanded for each order.

  3. Article: Time Series Forecasting of COVID-19 transmission in Canada Using LSTM Networks

    Chimmula, Vinay Kumar Reddy / Zhang, Lei

    Chaos Solitons Fractals

    Abstract: On March 11 th 2020, World Health Organization (WHO) declared the 2019 novel corona virus as global pandemic. Corona virus, also known as COVID-19 was first originated in Wuhan, Hubei province in China around December 2019 and spread out all over the ... ...

    Abstract On March 11 th 2020, World Health Organization (WHO) declared the 2019 novel corona virus as global pandemic. Corona virus, also known as COVID-19 was first originated in Wuhan, Hubei province in China around December 2019 and spread out all over the world within few weeks. Based on the public datasets provided by John Hopkins university and Canadian health authority, we have developed a forecasting model of COVID-19 outbreak in Canada using state-of-the-art Deep Learning (DL) models. In this novel research, we evaluated the key features to predict the trends and possible stopping time of the current COVID-19 outbreak in Canada and around the world. In this paper we presented the Long short-term memory (LSTM) networks, a deep learning approach to forecast the future COVID-19 cases. Based on the results of our Long short-term memory (LSTM) network, we predicted the possible ending point of this outbreak will be around June 2020. In addition to that, we compared transmission rates of Canada with Italy and USA. Here we also presented the 2, 4, 6, 8, 10, 12 and 14 th day predictions for 2 successive days. Our forecasts in this paper is based on the available data until March 31, 2020. To the best of our knowledge, this of the few studies to use LSTM networks to forecast the infectious diseases.
    Keywords covid19
    Publisher WHO
    Document type Article
    Note WHO #Covidence: #32390691
    Database COVID19

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  4. Article ; Online: Time series forecasting of COVID-19 transmission in Canada using LSTM networks

    Reddy Chimmula, Vinay Kumar / Zhang, Lei

    reponame:Expeditio Repositorio Institucional UJTL ; instname:Universidad de Bogotá Jorge Tadeo Lozano

    2020  

    Abstract: On March 11th 2020, World Health Organization (WHO) declared the 2019 novel corona virus as global pandemic. Corona virus, also known as COVID-19 was first originated in Wuhan, Hubei province in China around December 2019 and spread out all over the ... ...

    Abstract On March 11th 2020, World Health Organization (WHO) declared the 2019 novel corona virus as global pandemic. Corona virus, also known as COVID-19 was first originated in Wuhan, Hubei province in China around December 2019 and spread out all over the world within few weeks. Based on the public datasets provided by John Hopkins university and Canadian health authority, we have developed a forecasting model of COVID-19 outbreak in Canada using state-of-the-art Deep Learning (DL) models. In this novel research, we evaluated the key features to predict the trends and possible stopping time of the current COVID-19 outbreak in Canada and around the world. In this paper we presented the Long short-term memory (LSTM) networks, a deep learning approach to forecast the future COVID-19 cases. Based on the results of our Long short-term memory (LSTM) network, we predicted the possible ending point of this outbreak will be around June 2020. In addition to that, we compared transmission rates of Canada with Italy and USA. Here we also presented the 2, 4, 6, 8, 10, 12 and 14th day predictions for 2 successive days. Our forecasts in this paper is based on the available data until March 31, 2020. To the best of our knowledge, this of the few studies to use LSTM networks to forecast the infectious diseases
    Keywords Epidemic transmission ; Time series forecasting ; Machine learning ; Corona virus ; COVID-19 ; Long short term memory (LSTM) networks ; Síndrome respiratorio agudo grave ; SARS-CoV-2 ; Coronavirus ; covid19
    Publisher Chaos, Solitons & Fractals
    Publishing country co
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

    Full text online

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    Inter-library loan at ZB MED

    Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

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