Cardiff University | Prifysgol Caerdydd ORCA
Online Research @ Cardiff 
WelshClear Cookie - decide language by browser settings

Small world network models of the dynamics of HIV infection

Vieira, Israel Teixeira, Cheng, R. C. H., Harper, Paul Robert and de Senna, V. 2010. Small world network models of the dynamics of HIV infection. Annals of Operations Research 178 (1) , pp. 173-200. 10.1007/s10479-009-0571-y

Full text not available from this repository.

Abstract

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 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 recently 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 probability of a casual partnership (small world randomness parameter) and the spread of HIV. Finally, we illustrate the use of our model for the evaluation of interventions such as the promotion of safer sex and introduction of a vaccine.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Mathematics
Subjects: Q Science > QA Mathematics
R Medicine > R Medicine (General)
Additional Information: From the issue entitled "Operational Research Applied to Health Services: A Special Volume Dedicated to the International Conference ORAHS2007 / Edited by Xiaolan Xie, Steve Gallivan, Alain Guinet and Marion Rauner"
Publisher: Springer Verlag
ISSN: 0254-5330
Last Modified: 04 Jun 2017 02:52
URI: http://orca.cf.ac.uk/id/eprint/13173

Citation Data

Cited 21 times in Google Scholar. View in Google Scholar

Cited 21 times in Scopus. View in Scopus. Powered By Scopus® Data

Actions (repository staff only)

Edit Item Edit Item