Statistical inference for unknown parameters of stochastic SIS epidemics on complete graphs

in stochastic SIS epidemics

SIS epidemics on Triadic Random Graphs

It has been shown in the past that many real-world networks exhibit community structures and non-trivial clustering which comes with the occurrence of a notable number of triangular connections. Yet the influence of such connection patterns on the dynamics of disease transmission is not fully understood. In order to study their role in the context of Susceptible-Infected-Susceptible (SIS) epide...

Inferring Epidemic Parameters in Networked SIS Epidemics

The mathematical modeling and analysis of epidemic spread is of great importance in understating how epidemics behave and and how to control them, and it has attracted significant interest from different communities. The study of epidemics applies to many areas such as epidemiology [1], rumor spread [2, 3], information propagation [4, 5], network security [6, 7], and viral marketing [8, 9]. Alt...

Estimating Parameters for Stochastic Epidemics

Understanding the spread of infectious disease is an important issue in order to prevent major outbreaks. In this report mathematical modeling is used to gain insight into the dynamics of an epidemic. A process model, the SIR model, exploiting knowledge about population dynamics serves as framework. Key interest is in adapting the stochastic model to observed data – especially from animal produ...

Time Evolution of SIS epidemics in the Complete Graph

We show that, at the time being, the probability Pr [M (t) = k] that the number of infected nodes M (t) at time t equals k in the Markovian continuous-time "-SIS process on the complete graph cannot be determined exactly.