In the early stages of a pandemic, testing is a critical activity to understand the scale of the threat and respond to it in a timely way. However, testing capacity for a novel pathogen may be limited. This poses a dilemma as to who should be tested: travellers arriving from international hotspots, contacts of a known case, or people who have symptoms but no known exposure to a case? The answer to this dilemma may depend on what your strategic objectives are. Are you trying to control the outbreak locally by isolating infectious individuals? Or to gain information about the outbreak’s size and growth rate to inform other interventions? Or a bit of both?
This project will investigate these questions using a simulation model that compares alternative testing strategies. The model will be based on a branching process, with outbreaks seeded by international arrivals. Case isolation and contact tracing will be modelled by reducing transmission from individuals who test positive. Different strategies will be represented by different allocations of a limited number of tests between: (i) arriving travellers; (ii) traced contacts of a case; (iii) symptomatic community members. This allocation may be time-dependent as the epidemic dynamics unfold. Different objectives may be investigated, for example minimising the effective reproduction number, or minimizing the time until the outbreak size can be reliably estimated. This allows for a “value of information” approach, where an immediate epidemiological objective may be traded off against the ability to obtain information about the outbreak.
Supervisors
Primary Supervisor: Michael Plank
Key qualifications and skills
This project will require a background in applied mathematics and/or probability, 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; Probability
