Mathematical models of epidemics have created a major area of research interest during the last few decades.
A very simple mathematical model of epidemics appears to account for the evolution of Myspace and Facebook, argue John Cannarella and Joshua Spechler, graduate students in mechanical and aerospace engineering at Princeton University in a paper posted to the arXiv preprint server.
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.
Term 2 began with systems of DEs, including predator prey and the susceptible-infected-recovered (SIR) model of epidemics.
Only a few computational tools are currently available to the public for the analysis and modeling of epidemics.
While the epidemiology of the pathogen is understood well enough to generate computer models of epidemics, the molecular aspects of the infection process are largely unknown.
Despite the growing sophistications, spatial models of epidemics in networks are still simplifications of a complicated system (Reppas et al. 2010).
