Infectious Disease Modelling Workshop
If you're looking for the The AMSI Infectious Disease Modelling Workshop 2013, please click here.
Friday 3rd February, 2012, 9am – 4pm
Russel Love Theatre
Richard Berry Building (Building 160)
University of Melbourne, Parkville campus
A personal perspective . . .
Traditional epidemiological approaches cannot be easily applied to all public health policy questions relating to communicable (infectious) diseases. For example, strategic planning for antiviral-drug distribution in an influenza pandemic requires simulation of the likely effects of large scale deployment. Short of undertaking a whole-of-population experiment, thereby unreasonably depleting the very resource one is attempting to understand how to use, the outcomes can only be predicted, and informed policy developed, using modelling techniques.
Routinely applied to challenges in physics, climate science and economics, mathematical modelling provides an alternative when biological and public health problems are not amenable to randomised controlled experiments.
Modelling studies rely on detailed and accurate biological and epidemiological data for parameterisation. Without it, they contribute little to public health policy development. Conversely, well informed models may be influential, as evidenced by policy development for HIV, SARS and pandemic influenza. Traditional statistical measures assist in identifying key associations of variables with outcomes. Disease transmission models then use this information to explicitly characterise the processes by which these factors influence outcomes. Once validated against data, imposing
hypothetical interventions within these model frameworks allows for optimisation of interventions through computer simulation. Furthermore, modelling studies highlight where data is lacking, guiding design of future experimental investigations.
In this workshop you will be introduced to the basic tools used by infectious disease modelling researchers, and hear on ‘Work-in-Progress’ from active researchers and PhD candidates in the field.
Geoff Mercer and James McCaw
|Methods in infectious diseases modelling|
|9:00||James McCaw||An introduction to deterministic models of infection|
|9:30||Stephen Davis||Long-range percolation: simulating epidemics on networks|
|10:00||John Murray||Within-host modelling: Assessing the likely effectiveness of anti-HIV gene therapy|
|11:00||Joshua Ross||Stochastic models|
|11:30||Emma McBryde||Making the link between statistics and mathematical models: MRSA in hospitals|
|12:15-12:45||Mick Roberts||Modelling measles vaccination strategies in New Zealand|
|Roslyn Hickson||On the control of TB in the Torres Strait region|
|Trish Campbell||Using mathematical models to improve understanding of the factors driving pertussis transmission in the Australian population|
|Katie Glass||How can we fit age-structured disease models to routinely collected data?|
|2:00-3:30||Wuryatmo Sidik||Effects of human behaviour on the spread and controls of Avian influenza|
|Tong Hua Zhang||Dynamical behavior of an epidemic model with a ratio-dependent nonlinear incidence rate|
|Andrew Black||Modelling household disease dynamics and control|
|Natalya Levenkova||Modelling of a sexual network based on random geometric graphs and random motion|
|Deborah Cromer||Estimating growth rates using only a single point per patient|
|Alex Skvortsov||Monitoring and prediction of an epidemic outbreak using syndromic observations|
|Melissa Welsh||Mathematical epidemiology and Acute Rheumatic Fever|
|Mykola Pinkevych||Modelling the dynamics of malaria infection|
|Laith Yakob||Strategizing malaria control combinations with models|
|Stephen Petrie||Within-host modelling of influenza: improving parameter estimation and quantifying relative fitness|
|Janka Petravic||Characterising immune responses to HIV infection|
|Glenn Fulford||Sensitivity of outbreaks: wild rabbit diseases and hospital infections|
|3:30-4:00||Afternoon tea and Discussion|