Particle emission concept and probabilistic consideration of the development of infections in systems: Dynamics from logarithm and exponent in the infection process, percolation effects

Description

The book describes the possibility of making a probabilistic prognosis, which uses the mean n-day logarithm of case numbers in the past to determine an exponent for a probability density for a prognosis, as well as the particle emission concept, which is derived from contact and distribution rates that increase the exponent of the probable development to the extent that a group of people can be formed.  Preface Cordial Thanks to What You Can Find In This Book Declaration of waiver Contents 1 Statement 2 Systemic Epidemics 3 The Occurrence of Events 3.1 Events (E) 3.2 Risk and Opportunity (R, C) 4 Interactions 4.1 Thought Sketch 4.2 One-time Event that Occurs Within a Common Period of Time, the Infection, the Beginning of Percolations 4.2.1 Tabular Representation of the Development, the Distribution Rate 4.3 Evolution of the Spread of Biological Infections, Distribution Rate and Contact Rate 4.3.1 Particulate Emission Concept 4.3.2 Initial Conditions, Initial Population 4.3.3 Finding the Initial Population 4.3.4 Determination of the Exponential Growth Over Subsequent Intervals 4.4 Basis for a Probabilistic Prognosis 4.4.1 Statistical Surveys 4.4.2 Probability 4.4.3 The Difference: Mathematical Truth Through Proof—Statistical Approximation to Truth Through Experiments 4.5 Doubts About Statistical Measurements 5 The Difference Between Influenza and COVID Waves 6 Limits of Symmetrical Variance 6.1 Analysis of the Eqb Density 6.2 Adding the Kurtosis Parameter to the Density Eqb4 6.2.1 Parameter Estimation 6.3 Forecast using the density function and continuous adjustment of the parameters 6.3.1 Statistical Basis 6.3.2 Basics for the Exponential Expansion 6.4 Data Analysis on the Particle Emissions Concept 6.4.1 Consequences of Hygiene, Handshake, Breathing Air (Aerosols) 6.4.2 Determination of the Prognosis for a Future Increase or Decrease in the Infection Rate 6.4.3 Forecast Using the Density Function and Continuous Adjustment of the Parameters Based on a Dynamic Exponent 6.4.4 Germany 6.4.5 Knowledge of Germany 6.4.6 United States of America 6.4.7 Spain 6.5 Consideration of Some Developments in the United States 6.6 Incidence Under a Probabilistic View 6.6.1 Probabilistic Incidence preview for Texas 7 Leakage Effect—Percolation of the Virus 7.1 Potential Implications for Health Care Settings and Epidemiological Modeling 7.1.1 Beta Coefficient in Nonlinear Epidemiological Modeling 7.1.2 Health Care Setting Management 7.2 On the Percolation Theory COVID 7.2.1 A Basic Consideration, Mold Percolation 7.2.2 Consideration of the Vius Percolation in Human Populations 7.2.3 Conditions for a COVID Model Calculation 7.3 Principles of Percolation- Interface Effects 7.4 Examples of Percolation Effects, Clustering 7.4.1 Table of Initial-Cluster Without Percolation Effect 7.4.2 Initial-Cluster/Follwing-Cluster with a Percolation Effect 7.5 The Consequences of the Percolation Effect, Germany 7.6 Summary 7.7 Bibliography/Source Information