Sign inSubmit
computersciencejournal.org is not ours. This journal is published only at thescipub.com/jcs. Report a suspicious emailcomputersciencejournal.org is not ours · Report
ArticleMetrics
Research Article · Open access · Published 16 March 2021

First-Degree Polynomial Gradient Approach to Reveal the Severity of COVID-19 Pandemic in Affected Countries

Dedy Rahman Wijaya1, Anak Agung Gde Agung1, Tora Fahrudin1 & Suryatiningsih1

Journal of Computer Science, Volume 17, Issue 2, pp. 167–177 · doi.org/10.3844/jcssp.2021.167.177

7,171 accesses3,489 downloads0 citations (Crossref)Metrics
Cite Check for updates

Abstract

COVID-19 is a new type of Coronavirus (2019-nCoV) which originated from Wuhan in China. Since 11 March 2020, WHO has declared COVID-19 as a pandemic. Currently, it has spread to 175 countries or regions around the world. From day to day, confirmed, recovered and death cases have been reported. This data rapidly changes that indicates an uncertain situation. This uncertain situation might affect many social-economic activities. However, until now, there is no approach to categorize these countries in conjunction with the latest situation. The typical measure, for example, the Case Fatality Rate (CFR) is used to measure the proportion of deaths compared to the total number of confirmed from a certain disease. It utilizes for diseases with discrete, limited-time courses, such as outbreaks of acute infections. The major drawback of CFR is it can only be considered as a final result when all the cases have been accomplished (either died or recovered). According to this gap, we proposed the first-degree polynomial or linear gradient approach to categorize the COVID-19 severity status of areas or countries based on the rate of confirmed, recovered and death cases. The status categorization is necessary information for all parties to be aware of the situation. It can be used for consideration to determine policies related to COVID-19 pandemic such as travel warning, self-isolation, work from home, lock-down, etc.

Licence and reuse

Open access. This article is licensed under a Creative Commons Attribution 4.0 International Licence (CC BY 4.0), as printed on the article, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original authors and the source. No permission is needed.

How to reuse this article

Responses (0)

Have a comment on this research? Sign in to respond. Responses are moderated and published with your name and affiliation; the authors are invited to reply. How we use your dataWrite a response

About this article

Cite this article

Wijaya, D. R., Agung, A. A. G., Fahrudin, T. & Suryatiningsih, (2021). First-Degree Polynomial Gradient Approach to Reveal the Severity of COVID-19 Pandemic in Affected Countries. Journal of Computer Science, 17(2), 167–177. https://doi.org/10.3844/jcssp.2021.167.177

Licence
CC BY 4.0
Issue
Volume 17, Issue 2
Policies
Editorial independenceUse of generative AIData availabilityAppeals and complaints
Version
Version of record
First-Degree Polynomial Gradient Approach to Reveal the Severity of COVID-19 Pandemic in Affected CountriesJournal of Computer Science · Volume 17, Issue 2 · pp. 167–177 · CC BY 4.0Download