Complex networks, comprised of individual elements that interact with each other through
reaction channels, are ubiquitous across many scientific and engineering disciplines …

Phylogenetic trees can be used to infer the processes that generated them. Here, we
introduce a model, the Bayesian birth–death skyline plot, which explicitly estimates the rate …

Epidemic modeling of infectious diseases has a long history in both theoretical and
empirical research. However the recent explosion of genetic data has revealed the rapid …

The role of stochasticity and its interplay with nonlinearity are central current issues in
studies of the complex population patterns observed in nature, including the pronounced …

Diffusion processes are a promising instrument for realistically modelling the time-
continuous evolution of phenomena not only in the natural sciences but also in finance and …

A great variety of complex physical, natural and artificial systems are governed by statistical
distributions, which often follow a standard exponential function in the bulk, while their tail …

Reaction networks are systems in which the populations of a finite number of species evolve
through predefined interactions. Such networks are found as modeling tools in many …

To improve mathematical models of epidemics it is essential to move beyond the traditional
assumption of homogeneous well–mixed population and involve more precise information …

Mathematical epidemiology has a long history of origin and development. In particular,
mathematical modeling and analysis of infectious diseases has become a fundamental and …

The paper focuses on the study of an epidemic model for the evolution of diseases, using
stochastic models. We demonstrated the encoding of this intricate model into formalisms …