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.
Precipitation has a dominant role in agriculture, and a regular rain pattern is usually vital to agriculture; excessive or inadequate rainfall can be harmful. In this paper, an agricultural drought index is utilized to study the agricultural drought periods and analyze them with their intensities at various locations. Some nonhomogeneous Poisson models are also used to calculate the probability of agricultural droughts (number of times occurred) in a time interval of interest. It is to be assumed that the number of agricultural drought event occurrences is a Nonhomogeneous Poisson Process (NHPP) has a rate function, which depends on some parameters that must be estimated. Two cases of these functions are considered: the Weibull and linear intensity function. The Bayesian approach with Gibbs sampling under the Markov Chain Monte Carlo (MCMC) algorithm is used to estimate the parameters of these functions. The most appropriate fitted model is selected by using Deviance Information Criteria (DIC) and use that appropriately fitted model to calculate the accumulated events of agricultural drought in a time interval of interest at each location. Ordinary Kriging (OK) is used to regionalize the parameters and present its spatial behavior. The results based on the DIC indicate that the Power Law Process (PLP) performs better than the linear intensity function, NHPP model. The interpolated parameter values for the appropriate model, their patterns, and fluctuations for the study area are efficiently presented using contour maps. It is a novel and straightforward approach to assess the selected model parameter values used to predict the accumulated drought events at un-sampled locations. The proposed framework might also help to analyze other spatial variables of interest and can be used for climate-change study, ecosystem modeling, etc. The findings can also help to make decisions for sustainable environmental management in Pakistan.
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