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  1. Article: Using country-level variables to classify countries according to the number of confirmed COVID-19 cases: An unsupervised machine learning approach.

    Carrillo-Larco, Rodrigo M / Castillo-Cara, Manuel

    Wellcome open research

    2020  Volume 5, Page(s) 56

    Abstract: Background: ...

    Abstract Background:
    Keywords covid19
    Language English
    Publishing date 2020-06-15
    Publishing country England
    Document type Journal Article
    ISSN 2398-502X
    ISSN 2398-502X
    DOI 10.12688/wellcomeopenres.15819.3
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article: Using country-level variables to classify countries according to the number of confirmed COVID-19 cases: An unsupervised machine learning approach

    Carrillo-Larco, R. M. / Castillo-Cara, M.

    Wellcome Open Res

    Abstract: ... of confirmed COVID-19 cases (p<0 001) However, the model could not stratify countries in terms of number ... information before the COVID-19 pandemic, seemed able to classify countries in terms of the number ... pragmatic information With country-level pre-COVID-19-pandemic variables, we aimed to cluster countries ...

    Abstract Background: The COVID-19 pandemic has attracted the attention of researchers and clinicians whom have provided evidence about risk factors and clinical outcomes Research on the COVID-19 pandemic benefiting from open-access data and machine learning algorithms is still scarce yet can produce relevant and pragmatic information With country-level pre-COVID-19-pandemic variables, we aimed to cluster countries in groups with shared profiles of the COVID-19 pandemic Methods: Unsupervised machine learning algorithms (k-means) were used to define data-driven clusters of countries;the algorithm was informed by disease prevalence estimates, metrics of air pollution, socio-economic status and health system coverage Using the one-way ANOVA test, we compared the clusters in terms of number of confirmed COVID-19 cases, number of deaths, case fatality rate and order in which the country reported the first case Results: The model to define the clusters was developed with 155 countries The model with three principal component analysis parameters and five or six clusters showed the best ability to group countries in relevant sets There was strong evidence that the model with five or six clusters could stratify countries according to the number of confirmed COVID-19 cases (p<0 001) However, the model could not stratify countries in terms of number of deaths or case fatality rate Conclusions: A simple data-driven approach using available global information before the COVID-19 pandemic, seemed able to classify countries in terms of the number of confirmed COVID-19 cases The model was not able to stratify countries based on COVID-19 mortality data
    Keywords covid19
    Publisher WHO
    Document type Article
    Note WHO #Covidence: #617235
    Database COVID19

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  3. Article ; Online: Using country-level variables to classify countries according to the number of confirmed COVID-19 cases: An unsupervised machine learning approach

    Carrillo-Larco, R. M. Castillo-Cara M.

    Wellcome Open Res

    Abstract: ... of confirmed COVID-19 cases (p<0 001) However, the model could not stratify countries in terms of number ... information before the COVID-19 pandemic, seemed able to classify countries in terms of the number ... pragmatic information With country-level pre-COVID-19-pandemic variables, we aimed to cluster countries ...

    Abstract Background: The COVID-19 pandemic has attracted the attention of researchers and clinicians whom have provided evidence about risk factors and clinical outcomes Research on the COVID-19 pandemic benefiting from open-access data and machine learning algorithms is still scarce yet can produce relevant and pragmatic information With country-level pre-COVID-19-pandemic variables, we aimed to cluster countries in groups with shared profiles of the COVID-19 pandemic Methods: Unsupervised machine learning algorithms (k-means) were used to define data-driven clusters of countries;the algorithm was informed by disease prevalence estimates, metrics of air pollution, socio-economic status and health system coverage Using the one-way ANOVA test, we compared the clusters in terms of number of confirmed COVID-19 cases, number of deaths, case fatality rate and order in which the country reported the first case Results: The model to define the clusters was developed with 155 countries The model with three principal component analysis parameters and five or six clusters showed the best ability to group countries in relevant sets There was strong evidence that the model with five or six clusters could stratify countries according to the number of confirmed COVID-19 cases (p<0 001) However, the model could not stratify countries in terms of number of deaths or case fatality rate Conclusions: A simple data-driven approach using available global information before the COVID-19 pandemic, seemed able to classify countries in terms of the number of confirmed COVID-19 cases The model was not able to stratify countries based on COVID-19 mortality data
    Keywords covid19
    Publisher WHO
    Document type Article ; Online
    Note WHO #Covidence: #32587900
    DOI 10.12688/wellcomeopenres.15819.1
    Database COVID19

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  4. Article ; Online: Using country-level variables to classify countries according to the number of confirmed COVID-19 cases

    Carrillo-Larco, R / Castillo-Cara, M

    21 ; 1

    An unsupervised machine learning approach

    2020  

    Abstract: ... of confirmed COVID-19 cases (p<0.001). However, the model could not stratify countries in terms of number ... information before the COVID-19 pandemic, seemed able to classify countries in terms of the number ... pragmatic information. With country-level pre-COVID-19-pandemic variables, we aimed to cluster countries ...

    Abstract Background: The COVID-19 pandemic has attracted the attention of researchers and clinicians whom have provided evidence about risk factors and clinical outcomes. Research on the COVID-19 pandemic benefiting from open-access data and machine learning algorithms is still scarce yet can produce relevant and pragmatic information. With country-level pre-COVID-19-pandemic variables, we aimed to cluster countries in groups with shared profiles of the COVID-19 pandemic. Methods: Unsupervised machine learning algorithms (k-means) were used to define data-driven clusters of countries; the algorithm was informed by disease prevalence estimates, metrics of air pollution, socio-economic status and health system coverage. Using the one-way ANOVA test, we compared the clusters in terms of number of confirmed COVID-19 cases, number of deaths, case fatality rate and order in which the country reported the first case. Results: The model to define the clusters was developed with 155 countries. The model with three principal component analysis parameters and five or six clusters showed the best ability to group countries in relevant sets. There was strong evidence that the model with five or six clusters could stratify countries according to the number of confirmed COVID-19 cases (p<0.001). However, the model could not stratify countries in terms of number of deaths or case fatality rate. Conclusions : A simple data-driven approach using available global information before the COVID-19 pandemic, seemed able to classify countries in terms of the number of confirmed COVID-19 cases. The model was not able to stratify countries based on COVID-19 mortality data.
    Keywords covid19
    Publishing date 2020-05-01
    Publisher F1000Research
    Publishing country uk
    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.

  5. Article ; Online: Using country-level variables to classify countries according to the number of confirmed COVID-19 cases

    Carrillo-Larco, RM / Castillo-Cara, M

    An unsupervised machine learning approach [version 3; peerreview: 2 approved]

    2020  

    Abstract: ... of confirmed COVID-19 cases (p<0.001). However, the model could not stratify countries in terms of number ... information before the COVID-19 pandemic, seemed able to classify countries in terms of the number ... pragmatic information. With country-level pre-COVID-19-pandemic variables, we aimed to cluster countries ...

    Abstract Background: The COVID-19 pandemic has attracted the attention of researchers and clinicians whom have provided evidence about risk factors and clinical outcomes. Research on the COVID-19 pandemic benefiting from open-access data and machine learning algorithms is still scarce yet can produce relevant and pragmatic information. With country-level pre-COVID-19-pandemic variables, we aimed to cluster countries in groups with shared profiles of the COVID-19 pandemic. Methods: Unsupervised machine learning algorithms (k-means) were used to define data-driven clusters of countries; the algorithm was informed by disease prevalence estimates, metrics of air pollution, socio-economic status and health system coverage. Using the one-way ANOVA test, we compared the clusters in terms of number of confirmed COVID-19 cases, number of deaths, case fatality rate and order in which the country reported the first case. Results: The model to define the clusters was developed with 155 countries. The model with three principal component analysis parameters and five or six clusters showed the best ability to group countries in relevant sets. There was strong evidence that the model with five or six clusters could stratify countries according to the number of confirmed COVID-19 cases (p<0.001). However, the model could not stratify countries in terms of number of deaths or case fatality rate. Conclusions: A simple data-driven approach using available global information before the COVID-19 pandemic, seemed able to classify countries in terms of the number of confirmed COVID-19 cases. The model was not able to stratify countries based on COVID-19 mortality data.
    Keywords COVID-19 ; clustering ; k-mean ; pandemic ; unsupervised algorithms ; covid19
    Language English
    Publishing date 2020-03-25
    Publisher F1000Research
    Publishing country uk
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

    Full text online

    More links

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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.

  6. Article ; Online: Using country-level variables to classify countries according to the number of confirmed COVID-19 cases

    Rodrigo M. Carrillo-Larco / Manuel Castillo-Cara

    Wellcome Open Research, Vol

    An unsupervised machine learning approach [version 3; peer review: 2 approved]

    2020  Volume 5

    Abstract: ... of confirmed COVID-19 cases (p<0.001). However, the model could not stratify countries in terms of number ... information before the COVID-19 pandemic, seemed able to classify countries in terms of the number ... pragmatic information. With country-level pre-COVID-19-pandemic variables, we aimed to cluster countries ...

    Abstract Background: The COVID-19 pandemic has attracted the attention of researchers and clinicians whom have provided evidence about risk factors and clinical outcomes. Research on the COVID-19 pandemic benefiting from open-access data and machine learning algorithms is still scarce yet can produce relevant and pragmatic information. With country-level pre-COVID-19-pandemic variables, we aimed to cluster countries in groups with shared profiles of the COVID-19 pandemic. Methods: Unsupervised machine learning algorithms (k-means) were used to define data-driven clusters of countries; the algorithm was informed by disease prevalence estimates, metrics of air pollution, socio-economic status and health system coverage. Using the one-way ANOVA test, we compared the clusters in terms of number of confirmed COVID-19 cases, number of deaths, case fatality rate and order in which the country reported the first case. Results: The model to define the clusters was developed with 155 countries. The model with three principal component analysis parameters and five or six clusters showed the best ability to group countries in relevant sets. There was strong evidence that the model with five or six clusters could stratify countries according to the number of confirmed COVID-19 cases (p<0.001). However, the model could not stratify countries in terms of number of deaths or case fatality rate. Conclusions: A simple data-driven approach using available global information before the COVID-19 pandemic, seemed able to classify countries in terms of the number of confirmed COVID-19 cases. The model was not able to stratify countries based on COVID-19 mortality data.
    Keywords Medicine ; R ; Science ; Q
    Subject code 306
    Language English
    Publishing date 2020-06-01T00:00:00Z
    Publisher Wellcome
    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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