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Association Rules Extraction From the Coronavirus Disease 2019: Attributes on Morbidity and Mortality

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  • Donald Douglas Atsa'am

    (University of the Free State, South Africa)

  • Ruth Wario

    (University of the Free State, South Africa)

Abstract

This research was aimed to extract association rules on the morbidity and mortality of corona virus disease 2019 (COVID-19). The dataset has four attributes that determine morbidity and mortality; including Confirmed Cases, New Cases, Deaths, and New Deaths. The dataset was obtained as of 2nd April, 2020 from the WHO website and converted to transaction format. The Apriori algorithm was then deployed to extract association rules on these attributes. Six rules were extracted: Rule 1. {Deaths, NewDeaths}=>{NewCases}, Rule 2. {ConfCases, NewDeaths}=>{NewCases}, Rule 3. {ConfCases, Deaths}=>{NewCases}, Rule 4. {Deaths, NewCases}=>{NewDeaths}, Rule 5. {ConfCases, Deaths}=>{NewDeaths}, Rule 6. {ConfCases, NewCases}=>{NewDeaths}, with confidence 0.96, 0.96, 0.86, 0.66, 0.59, 0.51 respectively. These rules provide useful information that is vital on how to curtail further spread and deaths from the virus, both in areas where the pandemic is already ravaging and in areas yet to experience the outbreak.

Suggested Citation

  • Donald Douglas Atsa'am & Ruth Wario, 2022. "Association Rules Extraction From the Coronavirus Disease 2019: Attributes on Morbidity and Mortality," International Journal of Healthcare Information Systems and Informatics (IJHISI), IGI Global, vol. 17(1), pages 1-10, January.
    • Handle: RePEc:igg:jhisi0:v:17:y:2022:i:1:p:1-10
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