Using small world models to study infection communication and control

The modelling of infection transmission has taken many forms: The simple Susceptible-Infected-Removed (SIR) model yields good epidemiological results, but is not well suited to the modelling of the application of interventions. Attention has focused in recent years on graph (network) models and especially on those exhibiting the small-world properties described by Watts and Strogatz in “Nature” in 1998. This thesis examines such graph models, discovering several attributes which may yield improved results. In order to quantify the effects of these proposals, a classification system was developed together with a Goodness-of-Fit (GoF) measure. Additionally, a questionnaire was developed to reveal the operational organisational structure of the NHS Trust being examined. The resultant theoretical model was implemented in software and seeded with a graph derived from this questionnaire. This model was then examined to determine the effectiveness of these proposals, as measured via the GoF. The additional features proving beneficial were shown to be: full directionality in the graphs; modelling unknown paths via a new concept termed an “external path”; the division of the probability of infection transmission into three components; the seeding of the model with one derived from an organizational questionnaire. The resulting model was shown to yield very good results and be applicable to modelling both infection propagation and control.

Name dc:type.qualificationname

PhD

Level dc:type.qualificationlevel

Doctoral

Grantor dc:publisher.institution

University of Hull

Year dc:date.issued

2011

Author dc:creator

• Ganney, Paul Sefton

Advisor dc:contributor.advisor

• Phillips, Roger, computer scientist

• Computer science

Language dc:language

en

Identifier

oai:hull-repository.worktribe.com:4211386

OAI identifier oai:identifier

oai:hull-repository.worktribe.com:4211386