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QUANTITATIVE INFECTIOUS DISEASE EPIDEMIOLOGY

Mathematical modeling
When assessing the efficacy of interventions on the control of infectious diseases, a decision to proceed with a specific intervention, such as a vaccination schedule, requires an assessment of the likely consequences. Mathematical transmission models are an essential tool for such assessments.

We are helping Australia to prepare for a pandemic of influenza by using transmission models to assess how effectively various interventions mitigate the impact of a pandemic. This includes assessing the impact of

•  border control interventions on the delay until a local epidemic is initiated;

•  various social-distancing interventions on the progress of a local epidemic;

•  the use of antiviral drugs for prophylaxis and therapy;

•  the emergence of resistant strains of the virus.

We have also used mathematical models to assess the likely impact of mass vaccination with an HPV vaccine on the incidence of cervical cancer.

Recent papers by Belinda Barnes, Niels Becker, Peter Caley, Katie Glass and David Philp illustrate these two modeling projects.

Analysis of infectious disease data
We are also developing new methods for the analysis of infectious disease data that take the transmission process into account. For example, we develop methods to estimate how well vaccines protect individuals against infection or disease, and determine the type and amount of data required for such estimation. Recent papers by Niels Becker illustrate this work. Katie Glass recently applied variations of these methods to estimate the efficacy of antiviral drugs, for both prophylaxis and therapy.

Another example of this work is an analysis of the extent to which social distancing measures affected the Sydney influenza epidemic of 1919; see the recent paper by Peter Caley and David Philp .

Potential PhD candidates interested in working in this area need a background in mathematics or statistics.

Contact: Niels Becker