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  1. Article: Forecasting Brazilian and American COVID-19 cases based on artificial intelligence coupled with climatic exogenous variables.

    da Silva, Ramon Gomes / Ribeiro, Matheus Henrique Dal Molin / Mariani, Viviana Cocco / Coelho, Leandro Dos Santos

    Chaos, solitons, and fractals

    2020  Volume 139, Page(s) 110027

    Abstract: ... to deals with the COVID-19. The application of the forecasting artificial intelligence (AI) models has ... daily incidence of new cases and deaths. Therefore, it is important to forecast the number of new cases ... The novel coronavirus disease (COVID-19) is a public health problem once according ...

    Abstract The novel coronavirus disease (COVID-19) is a public health problem once according to the World Health Organization up to June 24th, 2020, more than 9.1 million people were infected, and more than 470 thousand have died worldwide. In the current scenario, the Brazil and the United States of America present a high daily incidence of new cases and deaths. Therefore, it is important to forecast the number of new cases in a time window of one week, once this can help the public health system developing strategic planning to deals with the COVID-19. The application of the forecasting artificial intelligence (AI) models has the potential of deal with dynamical behavior of time-series like of COVID-19. In this paper, Bayesian regression neural network, cubist regression,
    Keywords covid19
    Language English
    Publishing date 2020-06-30
    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.2020.110027
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article ; Online: Forecasting Brazilian and American COVID-19 cases based on artificial intelligence coupled with climatic exogenous variables

    da Silva, Ramon Gomes / Ribeiro, Matheus Henrique Dal Molin / Mariani, Viviana Cocco / Coelho, Leandro dos Santos

    Chaos, Solitons & Fractals

    2020  Volume 139, Page(s) 110027

    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.2020.110027
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

    Full text online

    More links

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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: Forecasting Brazilian and American COVID-19 cases based on artificial intelligence coupled with climatic exogenous variables

    da Silva, Ramon Gomes / Ribeiro, Matheus Henrique Dal Molin / Mariani, Viviana Cocco / Coelho, Leandro dos Santos

    Chaos Solitons Fractals

    Abstract: ... the cumulative COVID-19 cases in five Brazilian and American states, with a high number of cases up to April 28th ... 2020. Previous cumulative COVID-19 cases and exogenous variables as daily temperature and precipitation ... to deals with the COVID-19. The application of the forecasting artificial intelligence (AI) models has ...

    Abstract The novel coronavirus disease (COVID-19) is a public health problem once according to the World Health Organization up to June 24th, 2020, more than 9.1 million people were infected, and more than 470 thousand have died worldwide. In the current scenario, the Brazil and the United States of America present a high daily incidence of new cases and deaths. Therefore, it is important to forecast the number of new cases in a time window of one week, once this can help the public health system developing strategic planning to deals with the COVID-19. The application of the forecasting artificial intelligence (AI) models has the potential of deal with dynamical behavior of time-series like of COVID-19. In this paper, Bayesian regression neural network, cubist regression, k-nearest neighbors, quantile random forest, and support vector regression, are used stand-alone, and coupled with the recent pre-processing variational mode decomposition (VMD) employed to decompose the time series into several intrinsic mode functions. All AI techniques are evaluated in the task of time-series forecasting with one, three, and six-days-ahead the cumulative COVID-19 cases in five Brazilian and American states, with a high number of cases up to April 28th, 2020. Previous cumulative COVID-19 cases and exogenous variables as daily temperature and precipitation were employed as inputs for all forecasting models. The models’ effectiveness are evaluated based on the performance criteria. In general, the hybridization of VMD outperformed single forecasting models regarding the accuracy, specifically when the horizon is six-days-ahead, the hybrid VMD–single models achieved better accuracy in 70% of the cases. Regarding the exogenous variables, the importance ranking as predictor variables is, from the upper to the lower, past cases, temperature, and precipitation. Therefore, due to the efficiency of evaluated models to forecasting cumulative COVID-19 cases up to six-days-ahead, the adopted models can be recommended as a promising models for forecasting and be used to assist in the development of public policies to mitigate the effects of COVID-19 outbreak.
    Keywords covid19
    Publisher WHO
    Document type Article
    Note WHO #Covidence: #624706
    Database COVID19

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  4. Book ; Online: Forecasting Brazilian and American COVID-19 cases based on artificial intelligence coupled with climatic exogenous variables

    da Silva, Ramon Gomes / Ribeiro, Matheus Henrique Dal Molin / Mariani, Viviana Cocco / Coelho, Leandro dos Santos

    2020  

    Abstract: ... forecasting with one, three, and six-days-ahead the cumulative COVID-19 cases in five Brazilian and American ... states up to April 28th, 2020. Previous cumulative COVID-19 cases and exogenous variables as daily ... to forecasting cumulative COVID-19 cases up to six-days-ahead, the adopted models can be recommended ...

    Abstract The novel coronavirus disease (COVID-19) is a public health problem once according to the World Health Organization up to June 10th, 2020, more than 7.1 million people were infected, and more than 400 thousand have died worldwide. In the current scenario, the Brazil and the United States of America present a high daily incidence of new cases and deaths. It is important to forecast the number of new cases in a time window of one week, once this can help the public health system developing strategic planning to deals with the COVID-19. In this paper, Bayesian regression neural network, cubist regression, k-nearest neighbors, quantile random forest, and support vector regression, are used stand-alone, and coupled with the recent pre-processing variational mode decomposition (VMD) employed to decompose the time series into several intrinsic mode functions. All Artificial Intelligence techniques are evaluated in the task of time-series forecasting with one, three, and six-days-ahead the cumulative COVID-19 cases in five Brazilian and American states up to April 28th, 2020. Previous cumulative COVID-19 cases and exogenous variables as daily temperature and precipitation were employed as inputs for all forecasting models. The hybridization of VMD outperformed single forecasting models regarding the accuracy, specifically when the horizon is six-days-ahead, achieving better accuracy in 70% of the cases. Regarding the exogenous variables, the importance ranking as predictor variables is past cases, temperature, and precipitation. Due to the efficiency of evaluated models to forecasting cumulative COVID-19 cases up to six-days-ahead, the adopted models can be recommended as a promising models for forecasting and be used to assist in the development of public policies to mitigate the effects of COVID-19 outbreak.

    Comment: 24 pages, 6 figures. Published paper
    Keywords Quantitative Biology - Populations and Evolution ; Computer Science - Machine Learning ; covid19
    Subject code 310 ; 006
    Publishing date 2020-07-21
    Publishing country us
    Document type Book ; 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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