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

D. Atsa’am, R. Wario
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引用次数: 1

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.
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2019冠状病毒病的关联规则提取:发病率和死亡率的属性
本研究旨在提取2019冠状病毒病(COVID-19)发病率和死亡率的关联规则。该数据集有四个属性决定发病率和死亡率;包括确诊病例、新病例、死亡病例和新死亡病例。截至2020年4月2日,该数据集从世卫组织网站获得并转换为交易格式。然后部署Apriori算法来提取这些属性的关联规则。从中提取出六条规则:规则1。{死亡,新死亡}=>{新案例},规则2。{ConfCases, NewDeaths}=>{NewCases},规则3。{ConfCases, Deaths}=>{NewCases},规则4。{Deaths, NewCases}=>{NewDeaths},规则5。{ConfCases, Deaths}=>{NewDeaths},规则6。{ConfCases, NewCases}=>{NewDeaths},置信度分别为0.96,0.96,0.86,0.66,0.59,0.51。这些规则提供了有用的信息,对于如何在大流行已经肆虐的地区和尚未爆发疫情的地区遏制病毒的进一步传播和死亡至关重要。
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