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

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.

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

Wellcome Open Research ◽

10.12688/wellcomeopenres.15819.1 ◽

2020 ◽

Vol 5 ◽

pp. 56 ◽

Cited By ~ 2

Author(s):

Rodrigo M. Carrillo-Larco ◽

Manuel Castillo-Cara

Keyword(s):

Machine Learning ◽

Case Fatality Rate ◽

Learning Algorithms ◽

Case Fatality ◽

Machine Learning Algorithms ◽

Data Driven ◽

Mortality Data ◽

Fatality Rate ◽

Unsupervised Machine Learning ◽

Country Level

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.

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Classification of Daily Irradiance Profiles and the Behaviour of Photovoltaic Plant Elements: The Effects of Cloud Enhancement

Applied Sciences ◽

10.3390/app11115230 ◽

2021 ◽

Vol 11 (11) ◽

pp. 5230

Author(s):

Isabel Santiago ◽

Jorge Luis Esquivel-Martin ◽

David Trillo-Montero ◽

Rafael Jesús Real-Calvo ◽

Víctor Pallarés-López

Keyword(s):

Machine Learning ◽

Learning Algorithms ◽

Automatic Classification ◽

Sampling Frequency ◽

Machine Learning Algorithms ◽

Unsupervised Machine Learning ◽

Average Efficiency ◽

Clear Sky ◽

Photovoltaic Plant

In this work, the automatic classification of daily irradiance profiles registered in a photovoltaic installation located in the south of Spain was carried out for a period of nine years, with a sampling frequency of 5 min, and the subsequent analysis of the operation of the elements of the installation on each type of day was also performed. The classification was based on the total daily irradiance values and the fluctuations of this parameter throughout the day. The irradiance profiles were grouped into nine different categories using unsupervised machine learning algorithms for clustering, implemented in Python. It was found that the behaviour of the modules and the inverter of the installation was influenced by the type of day obtained, such that the latter worked with a better average efficiency on days with higher irradiance and lower fluctuations. However, the modules worked with better average efficiency on days with irradiance fluctuations than on clear sky days. This behaviour of the modules may be due to the presence, on days with passing clouds, of the phenomenon known as cloud enhancement, in which, due to reflections of radiation on the edges of the clouds, irradiance values can be higher at certain moments than those that occur on clear sky days, without passing clouds. This is due to the higher energy generated during these irradiance peaks and to the lower temperatures that the module reaches due to the shaded areas created by the clouds, resulting in a reduction in its temperature losses.

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Data-driven decision making based on evidential reasoning approach and machine learning algorithms

Applied Soft Computing ◽

10.1016/j.asoc.2021.107622 ◽

2021 ◽

pp. 107622

Author(s):

Chao Fu ◽

Che Xu ◽

Min Xue ◽

Weiyong Liu ◽

Shanlin Yang

Keyword(s):

Machine Learning ◽

Decision Making ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Evidential Reasoning ◽

Data Driven ◽

Data Driven Decision Making ◽

Evidential Reasoning Approach

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Unsupervised Machine Learning Algorithms to Detect CO2 Clouds on Mars

10.5194/epsc2020-758 ◽

2020 ◽

Author(s):

Vincent Caillé ◽

Anni Määttänen ◽

Aymeric Spiga ◽

Lola Falleti ◽

Gregory A. Neumann

Keyword(s):

Machine Learning ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Unsupervised Machine Learning

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An Evidence Base for School Health Policy during the COVID-19 Pandemic

Health Behavior and Policy Review ◽

10.14485/hbpr.8.1.4 ◽

2021 ◽

Vol 8 (1) ◽

Author(s):

Saif Badran ◽

Omran Musa ◽

Somaya Al-maadeed ◽

Egon Toft ◽

Suhail Doi

Keyword(s):

Dose Response ◽

Case Fatality Rate ◽

School Health ◽

Meta Analysis ◽

Case Fatality ◽

Evidence Base ◽

Mortality Data ◽

Response Analysis ◽

Fatality Rate ◽

Epidemiological Characteristics

Objective: Children represent a small fraction of confirmed COVID-19 cases, with a low case fatality rate (CFR). In this paper, we lay out an evidence-based policy for reopening schools. Methods: We gathered age-specific COVID-19 case counts and identified mortality data for 14 countries. Dose-response meta-analysis was used to examine the relationship of the incremental case fatality rate (CFR) to age. In addition, an evidence-to-decision framework (EtD) was used to correlate the dose-response data with other epidemiological characteristics of COVID-19 in childhood. Results: In the dose-response analysis, we found that there was an almost negligible fatality below age 18. CFR rose little between ages 5 to 50 years. The confidence intervals were narrow, suggesting relative homogeneity across countries. Further data suggested decreased childhood transmission from respiratory droplets and a low viral load among children. Conclusions: Opening up schools and kindergartens is unlikely to impact COVID-19 case or mortality rates in both the child and adult populations. We outline a robust plan for schools that recommends that general principles not be micromanaged, with authority left to schools and monitored by public health authorities.

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