The transmission of infectious disease in epidemiological models usually is based on the assumption that population within random-mixing. Although medical developments can reduce the consequences of the spread of infectious diseases, prevention of plague remains a major toehold. After a model is formulated containing the main fitur the development and transmission of infectious disease, onward to the model can be used to predict, making eradication strategies, control or prevent the spread. Modeling the spread of the disease has the potential to improve the quality of human life. The social life of humans far more complex exceed a diverse population. The transmission dynamics of infectious diseases is sensitive to patterns the interaction between the individual vulnerable (susceptible) and contracted (infectious). Human social contact very heterogeneous group. To predict the impact of this pattern against the transmission of infectious diseases, the use of epidemiological random net...

Two crucial elements facilitate the understanding and control of communicable disease spread within a social setting. These components are, the underlying contact structure among individuals that determines the pattern of disease transmission; and the evolution of this pattern over time. Mathematical models of infectious diseases, which are in principle analytically tractable, use two general approaches to incorporate these elements. The first approach, generally known as compartmental modeling, addresses the time evolution of disease spread at the expense of simplifying the pattern of transmission. On the other hand, the second approach uses network theory to incorporate detailed information pertaining to the underlying contact structure among individuals. However, while providing accurate estimates on the final size of outbreaks/epidemics, this approach, in its current formalism, disregards the progression of time during outbreaks. So far, the only alternative that enables the int...

It has long been recognised that the structure of social networks plays an important role in the dynamics of disease propagation. The spread of HIV usually results from a complex network of social interactions and other factors related to culture, sexual behaviour, demography, geography and disease characteristics, as well as the availability, accessibility and delivery of healthcare. The small world phenomenon has been used for representing social network interactions. It states that, given some random connections, the degrees of separation between any two individuals within a population can be very small. In this paper we present a discrete event simulation model which uses a variant of the small world network model to represent social interactions and the sexual transmission of HIV within a population. We use the model to demonstrate the importance of the choice of topology and initial distribution of infection, and capture the direct and non-linear relationship between the proba...

In this thesis we analyze the relationship between epidemiology and network theory, starting from the observation that the viral propagation between interacting agents is determined by intrinsic characteristics of the population contact network. We aim to investigate how a particular network structure can impact on the long-term behavior of epidemics. This field is way too large to be fully discussed; we limit ourselves to consider networks that are partitioned into local communities, in order to incorporate realistic contact structures into the model. The gross structure of hierarchical networks of this kind can be described by a quotient graph. The rationale of this approach is that individuals infect those belonging to the same community with higher probability than individuals in other communities. We describe the epidemic process as a continuous-time individual-based susceptible–infected–susceptible (SIS) model using a first-order mean-field approximation, both in homogeneous a...