Influence of COVID-19 vaccination on the dynamics of new infected cases in the world.

The initial COVID-19 vaccinations were created and distributed to the general population in 2020 thanks to emergency authorization and conditional approval. Consequently, numerous countries followed the process that is currently a global campaign. Taking into account the fact that people are being vaccinated, there are concerns about the effectiveness of that medical solution. Actually, this study is the first one focusing on how the number of vaccinated people might influence the spread of the pandemic in the world. From the Global Change Data Lab "Our World in Data", we were able to get data sets about the number of new cases and vaccinated people. This study is a longitudinal one from 14/12/2020 to 21/03/2021. In addition, we computed Generalized log-Linear Model on count time series (Negative Binomial distribution due to over dispersion in data) and implemented validation tests to confirm the robustness of our results. The findings revealed that when the number of vaccinated people increases by one new vaccination on a given day, the number of new cases decreases significantly two days after by one. The influence is not notable on the same day of vaccination. Authorities should increase the vaccination campaign to control well the pandemic. That solution has effectively started to reduce the spread of COVID-19 in the world.

A superior extension for the Lomax distribution with application to Covid-19 infections real data

We present a new continuous lifetime model with four parameters by combining the Lomax and the Weibull distributions. The extended odd Weibull Lomax (EOWL) distribution is what we’ll call it. This new distribution possesses several desirable properties thanks to the simple linear representation of its hazard rate function, moments, and moment -generating function, with stress-strength reliability that are provided in a simple closed forms. The parameters of the EOWL model are estimated using classical methods such as the maximum likelihood (MLE) and the maximum product of spacing (MPS) and estimated also but using a non-classical method such as Bayesian analytical approaches. Bayesian estimation is performed using the Monte Carlo Markov Chain method. Monte Carlo simulation are used to assess the effectiveness of the estimation methods throughout the Metropolis Hasting (MH) algorithm. To illustrate the suggested distribution’s effectiveness and suitability for simulating real-world pandemics, we used three existing COVID-19 data sets from the United Kingdom, the United States of America, and Italy which are studied to serve as illustrative examples. We graphed the P-P plots and TTT plots for the proposed distribution proving its superiority in a graphical manner for modelling the three data sets in the paper.