Use of non-homogeneous Poisson process for the analysis of new cases, deaths, and recoveries of COVID-19 patients: A case study of Kuwait

摘要

The coronavirus disease spread out rapidly in China and then in the whole world. Kuwait is one of those countries which are positively affected by this pandemic.Objective:The current study aims to provide an appropriate and novel framework for the analysis of the Severe Acute Respiratory Syndrome coronavirus 2 (SARS-CoV-2) infected patient's counts and rate of change in these counts with respect to time. Therefore, we considered the number of SARS- CoV-2 patients, i.e., confirmed cases, deaths, and recoveries for Kuwait, ranging from the 24th of February 2020 to the 25th of August 2020.Method:Here, we used the Markov Chain Monte Carlo (MCMC) simulation methods for the data analysis of SARS-CoV-2 to develop the Bayesian analysis of the Non-Homogeneous Poisson Process (NHPP). For this purpose, we used the two unique models of NHPP: the linear intensity function and the power law process. The discrimination methods are also discussed to select a better model for daily basis data of confirmed cases, deaths, and recoveries of SARS-CoV-2 patients. The appropriate model is selected based on the Deviance Information Criteria (DIC).Results:The value of DIC indicates that the power-law process performs better than the linear intensity functions for estimating and presenting all the study variables. The current study explored the usefulness and significance of the proposed research framework to analyze the SARS-CoV-2 new confirmed cases, recoveries, and deaths in a specific area.Conclusion:The findings of the study will be helpful for the health organizations or authorities to develop the approaches based on the current resources and situations due to the pandemic. The provided framework could be beneficial in analyzing the second and third layers of COVID-19 in the area. The analysis of the counts for each study variable and for each variable a comparative analysis of all the three layers is the aim of our future study.