Authors
Title
Stochastic Dynamics In Epidemic Networks
Abstract
Human life and diseases are inseparable. Diseases can be caused by our own bodies as they age and degenerate or by infectious pathogens. Our study is about infectious diseases, such as flu or sexually transmitted diseases. The simple model of progress of an epidemic in a large population divides the population into three different compartments: Susceptible, Infected, and Recovered (SIR). There are several important factors on modeling epidemic diseases such as the structure of the population representing the possible contact among individuals and the virus transmission, the time to recover from the disease, and life history of the virus affecting incubation time and infectiousness. The contacts can be modeled as a weighted, static or dynamic network; the virus transmission can be modeled as transition rates of becoming infected when in direct contact with an infectious person; the recovery can be expressed as rate at which the individual heals and becomes resistant to the disease.