Analysis of Modeling and Statistics for COVID-19
A special issue of COVID (ISSN 2673-8112).
Deadline for manuscript submissions: closed (30 June 2024) | Viewed by 19341
Special Issue Editors
Interests: polymer physics; computational physics; applied mathematics; stochastic differential equations; coarse-graining; biophysics
Special Issues, Collections and Topics in MDPI journals
Interests: astrophysics; space physics; cosmic rays; plasma physics; astroparticle physics
Special Issues, Collections and Topics in MDPI journals
Interests: disease mathematical modeling; computational epidemiology; data-based epidemiological modeling; population dynamics; differential equations; dynamical systems
Special Issues, Collections and Topics in MDPI journals
Interests: theoretical biology; biostatistics; medical informatics
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Modeling the COVID-19 outbreak requires considering a large amount of data at the world level concerning the reported number of new cases, deaths, and people vaccinated, by region and by age group or other groups.
These data must first be analyzed with statistical methods (time series analysis, principal component analysis, technical classification, etc.), then be used to build refutable models of different types, deterministic (differentiable or discrete) or stochastic.
The consideration of the spatial dimension can lead to diffusion models, that of the age of the patients to population dynamics models and that of the advance in the dynamics of the infection to variable reproduction number models (due to characteristics of contagiousness, virulence, and susceptibility in the host and the virus changing over time caused by 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 stochastic variability on the observed data and the parameters introduced into the models. All articles dealing with statistical and dynamic aspects of COVID-19 disease, allowing its statistical description, the study of its mechanisms, and the forecasting of its evolution will be considered in the Special Issue “Analysis of Modeling and Statistics for COVID-19”.
Prof. Dr. Martin Kröger
Prof. Dr. Reinhard Schlickeiser
Prof. Dr. Pierre Magal
Prof. Dr. Jacques Demongeot
Guest Editors
Manuscript Submission Information
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Keywords
- COVID-19 statistics
- epidemiological modeling
- time series analysis
- prediction techniques
- outbreak spatial diffusion
- daily reproduction number
- contagion modeling
- viral mutation modeling
- virulence mechanisms
- host immunity modeling
- mitigation measures dynamics
- vaccination policy
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