About: Using Supervised Machine Learning and Empirical Bayesian Kriging to reveal Correlates and Patterns of COVID-19 Disease outbreak in sub-Saharan Africa: Exploratory Data Analysis

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About: Using Supervised Machine Learning and Empirical Bayesian Kriging to reveal Correlates and Patterns of COVID-19 Disease outbreak in sub-Saharan Africa: Exploratory Data Analysis Goto Sponge NotDistinct Permalink

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type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
title	Using Supervised Machine Learning and Empirical Bayesian Kriging to reveal Correlates and Patterns of COVID-19 Disease outbreak in sub-Saharan Africa: Exploratory Data Analysis
Creator	Onovo, Amobi Atobatele, Akinyemi Gado, Pamela James, Ezekiel Kalaiwo, Abiye Magaji, Doreen Obanubi, Christopher Odezugo, Gertrude Ogundehin, Dolapo Russell, Michele
source	MedRxiv
abstract	Introduction: Coronavirus disease 2019 (COVID-19) is an emerging infectious disease that was first reported in Wuhan, China, and has subsequently spread worldwide. Knowledge of coronavirus-related risk factors can help countries build more systematic and successful responses to COVID-19 disease outbreak. Here we used Supervised Machine Learning and Empirical Bayesian Kriging (EBK) techniques to reveal correlates and patterns of COVID-19 Disease outbreak in sub-Saharan Africa (SSA). Methods: We analyzed time series aggregate data compiled by Johns Hopkins University on the outbreak of COVID-19 disease across SSA. COVID-19 data was merged with additional data on socio-demographic and health indicator survey data for 39 of SSA 48 countries that reported confirmed cases and deaths from coronavirus between February 28, 2020 through March 26, 2020. We used supervised machine learning algorithm, Lasso for variable selection and statistical inference. EBK was used to also create a raster estimating the spatial distribution of COVID-19 disease outbreak. Results: The lasso Cross-fit partialing out predictive model ascertained seven variables significantly associated with the risk of coronavirus infection (i.e. new HIV infections among pediatric, adolescent, and middle-aged adult PLHIV, time (days), pneumococcal conjugate-based vaccine, incidence of malaria and diarrhea treatment). Our study indicates, the doubling time in new coronavirus cases was 3 days. The steady three-day decrease in coronavirus outbreak rate of change (ROC) from 37% on March 23, 2020 to 23% on March 26, 2020 indicates the positive impact of countries' steps to stymie the outbreak. The interpolated maps show that coronavirus is rising every day and appears to be severely confined in South Africa. In the West African region (i.e. Burkina Faso, Ghana, Senegal, CotedIviore, Cameroon, and Nigeria), we predict that new cases and deaths from the virus are most likely to increase. Interpretation: Integrated and efficiently delivered interventions to reduce HIV, pneumonia, malaria and diarrhea, are essential to accelerating global health efforts. Scaling up screening and increasing COVID-19 testing capacity across SSA countries can help provide better understanding on how the pandemic is progressing and possibly ensure a sustained decline in the ROC of coronavirus outbreak. Funding: Authors were wholly responsible for the costs of data collation and analysis.
has issue date	2020-05-02(xsd:dateTime)
bibo:doi	10.1101/2020.04.27.20082057
has license	medrxiv
sha1sum (hex)	927f883369146cb8623339224a73b39ffc9b82ef
schema:url	https://doi.org/10.1101/2020.04.27.20082057
resource representing a document's title	Using Supervised Machine Learning and Empirical Bayesian Kriging to reveal Correlates and Patterns of COVID-19 Disease outbreak in sub-Saharan Africa: Exploratory Data Analysis
resource representing a document's body	covid:927f883369146cb8623339224a73b39ffc9b82ef#body_text
is schema:about of	named entity 'Disease' named entity 'Empirical Bayesian' named entity 'Kriging' named entity 'CDC' named entity 'HIV' named entity 'CHD' named entity 'incidence rate' named entity 'life expectancy at birth' named entity 'risk factors' named entity 'South Africa' named entity 'BIC' named entity 'health indicator' named entity 'United Nations' named entity 'WHO' named entity 'GDP per capita' named entity 'variable selection' named entity 'pneumococcal' named entity 'malaria' named entity 'reported coronavirus cases' named entity '95% CI' named entity 'west African' named entity 'HIV infection' named entity 'opportunistic infection' named entity 'self-quarantine' named entity 'MDA' named entity 'coronavirus disease' named entity 'coronavirus disease' named entity 'risk factors' named entity 'Johannesburg' named entity 'COVID-19' named entity 'Senegal' named entity 'Ghana' named entity 'SSA' named entity 'SSA' named entity 'diarrhea' named entity 'Diarrhea' named entity 'CC-BY-ND' named entity 'Nigeria' named entity 'medRxiv' named entity 'clinical outcomes' named entity 'Africa' named entity 'coronavirus' named entity 'Tunisia' named entity 'Kinshasa' named entity 'coronavirus' named entity 'South Africa' named entity 'risk factors' named entity 'data analysis' named entity 'SSA' named entity 'medRxiv' named entity 'Egypt' named entity 'Ghana' named entity 'coronavirus disease' named entity 'sub-Saharan' named entity 'supervised machine learning' named entity 'pneumococcal' named entity 'one-unit' named entity 'Senegal' named entity 'SSA' named entity 'older patients' named entity 'lasso regression' named entity 'pediatrics' named entity 'EDA' named entity 'coronavirus outbreak' named entity 'vaccine' named entity 'COVID' named entity 'tuberculosis' named entity 'EDA' named entity 'coronavirus disease 2019' named entity 'Cameroon' named entity 'Africa' named entity 'west African' named entity 'malaria' named entity 'inferential statistics' named entity 'SSA' named entity 'malaria'

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