Modelling Baguio city COVID-19 trend during alert level 1 using non-homogeneous Poisson process

The Non-Homogeneous Poisson Process has been used to determine the occurrence of COVID-19 in an area. Being a Stochastic Process of the Poisson distribution, it deals with the probability of rare events. Unlike the other distributions, including the Poisson distribution and the Homogeneous Poisson P...

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Hauptverfasser:	Marigmen, Joseph Ludwin D. C., Addawe, Rizavel C.
Format:	Tagungsbericht
Sprache:	eng
Schlagworte:	Algorithms COVID-19 Markov chains Modelling Poisson density functions Poisson distribution Power law Statistical analysis Stochastic processes
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creator	Marigmen, Joseph Ludwin D. C. Addawe, Rizavel C.
description	The Non-Homogeneous Poisson Process has been used to determine the occurrence of COVID-19 in an area. Being a Stochastic Process of the Poisson distribution, it deals with the probability of rare events. Unlike the other distributions, including the Poisson distribution and the Homogeneous Poisson Process, the Non-Homogeneous Poisson Process incorporates the idea of having non-homogeneous intensities. In this paper, we apply the process of modelling the trend of COVID-19 cumulative data in Baguio City during Alert Level 1 from March 1, 2022 to May 31, 2022. As part of model fitting, the Linear Intensity Function and the Power Law Process were incorporated. Applying the Non-Homogeneous Poisson Process in determining the best fit for the data through the Markov Chain Monte Carlo algorithm, it concluded that the daily cumulative cases and daily cumulative recoveries follow the Linear Intensity Function. On the other hand, the daily cumulative death follows the Power Law Process.
doi_str_mv	10.1063/5.0192501
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subjects	Algorithms COVID-19 Markov chains Modelling Poisson density functions Poisson distribution Power law Statistical analysis Stochastic processes
title	Modelling Baguio city COVID-19 trend during alert level 1 using non-homogeneous Poisson process
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