Sentence examples for models of epidemics from inspiring English sources

Mathematical models of epidemics have created a major area of research interest during the last few decades.

In Canada, a federally funded network of centers of excellence called MITACS (Mathematics, Information Technology, and Complex Systems) includes a biomedical theme that deals with developing statistical tools for genetic research, mathematical and computer models of epidemics, biomedical models of cellular and physiological systems, and computer models in pharmaceutical development.

Nowadays, dynamic models of epidemics are widely accepted as efficient tools to help understand the spread and management of infectious diseases (see e.g. [32] [35]).

Traditionally, mathematical models of epidemics often take the form of deterministic differential equations in which the variables represent the expected number of individuals in broad disease classes (e.g., susceptible, infected, or recovered) [6].

Models of epidemics in complex networks are improving our predictive understanding of infectious disease outbreaks.

Despite the growing sophistications, spatial models of epidemics in networks are still simplifications of a complicated system (Reppas et al. 2010).

The resulting data is often temporally resolved and has been increasingly used in various contexts including the study of human behaviour, the validation of models of human interactions and data-driven models of epidemic spreading [3, 20, 21].

In this paper, we have investigated whether low resolution co-presence information can be used as a substitute for detailed face-to-face proximity data, both from the point of view of extracting large-scale structural and statistical features of the temporal contact network in a population and in data-driven models of epidemic processes in a population.

We explore the effect of including such behavior in models of epidemic dynamics.

We further show that the dynamics of donations follow a logistic growth already known from models of epidemic spreading.

One key assumption that is often taken in models of epidemic spread in networks is the symmetrical nature of contacts (Bianconi et al. 2008).