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.
Screening for early detection of breast cancer is considered to be an important element of preventive medicine. In this paper, we use numerical simulations to examine the length bias in regular interval screening programmes, by computing the doubling times of breast cancer tumours detected through regular mammographies compared to self-detection. Our analysis shows that doubling times of tumours detected by a regular screening programme are longer than doubling times in the original whole population and considerably longer than those self-detected. Hence regular interval mammographies may be missing a high proportion of fast growing tumours and therefore the benefits of current screening programmes may need to be re-evaluated. We examine the likely size of the length bias for the present UK breast cancer screening programme and perform a sensitivity analysis by varying the screen detection probabilities to reflect future advances in mammographic detection.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.