Infectious disease forecasting uses real-time surveillance data on lab-confirmed cases or hospital admissions for a particular disease, and attempts to predict that data for the next few weeks into the future. The accuracy of probabilistic forecasts made ahead of time can then be evaluated as the data rolls in. This project will compare alternative forecasting models, develop methods for fitting models data, and test the accuracy of their forecasts against subsequent data. This will help improve the quality of real-time reporting to public health partners in Australia and New Zealand.
Supervisors
Primary Supervisor: Michael Plank
Key qualifications and skills
This project will require a background in applied probability and 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
