Journal of Biometrics & Biostatistics

We propose in this paper, a brief review of Muth-Pareto distribution. More precisely, we discuss the concept of record values and give a treatment of some selected properties of the distribution. The properties considered include the quartiles from quantile function, entropy, and limiting distribution of minimum order statistic.We provide some plots for the distribution of lower record values and studied the behavior by varying number of record values in the sample. It was observed that variability decreases by way of increasing number of record values in the sample. The quartiles relatedto the Muth-Pareto distributionweretabularized for some selected parameter values. It is our hope that the discoveries of this paper will be beneficialfor practitioners and also a source of reference for users so as to enhanceresearch interests related to Muth- Pareto distribution and its applications.

Our primary objective in this paper was to determine the impact of various factorsaffecting disproportionate COVID mortality rates between counties in the United States.
We primarily relied on the CDC’s demographics data and the CDC’s data on COVID andcomorbidities in US counties. We used these datasets to visualize mortality rates
andco-morbidity rates. Exploratory data analysis was then performed to attempt to find trends. Afterwards, we fit our data to a linear regression model to identify the factors
that contributed most to the model. The most important features of our model was the proportion of the population that was male and the median age. We found that the
median age of the population was a stronger predictor of COVID mortality than presence of comorbidities like diabetes and heart disease. More analysis has yet to be done
on the intersection of various comorbidities and median age.

Covid 19 that began in the end 0f 2019 has spread rapidly across continents in a short of time. This event makes most countries do not report and record the disease properly. There is considerable number of undocumented infectious individuals that should be considered when we want to estimate the Susceptible, Infectious, and Removal (SIR) parameters and to predict the future cases. The covid 19 data obtained from City of Surabaya were analyzed using Bayesian SIR modeling by BaySIR package in RStudio. The estimated and predicted medians of SIR compartment tend to decrease over time. The estimated and predicted medians of effective reproduction number tend to decrease over time. The results depend on the assumptions of SIR model to use such as the dynamic of covid 19, local government policies, and individual behavior in the community.