Analysis of Modeling and Statistics for COVID-19, 2nd edition

Dear Colleagues,

Modeling the COVID-19 outbreak requires considering a large amount of data, at the global level, related to the reported number of new cases, deaths, and people vaccinated, segmented by region, age group, and other groups.

These data must first be analyzed via statistical methods (time series analysis, principal component analysis, technical classification, etc.), before being used to build refutable models of different types, such as deterministic (differentiable or discrete) or stochastic models.

Considering the spatial dimension can lead to diffusion models, which can consider the ages of the patients for population dynamics models and advances in the dynamics of the infection for variable reproduction number models (due to the contagiousness, virulence, and susceptibility in the host and the virus changing over time due to viral mutations, environmental changes, host immunity, public health policy, etc.).

A combination of these three types of models is also possible, with the additional consideration of the stochastic variability on the observed data and the parameters introduced into the models. All articles dealing with the statistical and dynamic aspects of COVID-19, which allow for its statistical description, the study of its mechanisms, and the forecasting of its evolution, will be considered in this Special Issue, titled “Analysis of Modeling and Statistics for COVID-19, 2nd Edition”.

Prof. Dr. Martin Kröger
Prof. Dr. Reinhard Schlickeiser
Guest Editors

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