Mathematical models are useful for interpreting disease surveillance data streams in real-time to provide situational awareness and to support the public health response. This includes model outputs such as estimates of the effective reproduction number and epidemic doubling time, and near-term forecasts of case incidence. However, these metrics rely on there being sustained community transmission producing a sufficient number of cases for reliable inference.
Periods in which there is no or very low community transmission, such as may occur in countries following an elimination strategy, require a different approach. The risk of a local outbreak starting as a result of imported infections is a key consideration in this context. Estimating this risk requires real-time data on the number of arrivals from overseas, and the number of cases detected in those arrivals. It will also depend on travel-related measures, such as testing and quarantine, and community transmission potential.
This project will develop a mathematical model for travel-related outbreaks and explore how outbreak risk depends on key parameters such as travel volume, traveller infection prevalence, border policy, and community vaccination rate. The aim of the project is to carry out a “simulation-estimation” study, where the model is used to simulate synthetic data, and then to test whether model parameters can be reliably estimated from that data. This is a precursor to deploying the model on real-time data during a future outbreak or pandemic.
Supervisors
Primary Supervisor: Michael Plank
Key qualifications and skills
This project will require a background in applied mathematics and/or computational statistics, with experience of coding in a language such as Python, R or Matlab.
Does the project come with funding
No - Student must be self-funded
Final date for receiving applications
Ongoing
How to apply
Email to primary supervisor michael.plank@canterbury.ac.nz
Keywords
Mathematical modelling; Infectious disease dynamics; Epidemiology; Computational statistics; Bayesian inference
