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Modeling Covid-19 Deaths in Nigeria Using Exponential-Gamma Distribution

Authors: Ogunwale Olukunle Daniel; Ayeni Taiwo Michael; Odukoya Elijah Ayooluwa;

Modeling Covid-19 Deaths in Nigeria Using Exponential-Gamma Distribution

Abstract

This work examines the performance of Exponential-Gamma distribution and the existing Exponetial and Gamma in relative to the distribution that will best fit the data on Covid-19 death in Nigeria from March 2020 to April 2021. The parameters of the distribution were estimated using the method of maximum likelihood estimates. The newly developed Exponential-Gamma distribution was compared with the exiting Exponential and Gamma distributions using the log-likelihood function, Akaike information criterion (AIC), and Bayesian information criterion (BIC) as the criteria for selecting the best fit model. The results show that the newly developed Exponential-Gamma distribution performed better than the existing Exponential and Gamma distributions in contrast in terms of model fit, this showed that the newly developed Exponential-Gamma distribution is more flexible and precise in analyzing the Covid-19 data other than the existing Exponential and Gamma distributions.

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Keywords

Exponential-Gamma Distribution, Maximum likelihood Estimate, AIC, BIC, log-likelihood function, Covid19

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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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