Understanding the mechanisms underlying patterns of SARS-CoV-2 transmission is important for informing strategic public health measures against respiratory pathogens more widely. Universities were identified as likely to support rapid transmission of a new pathogen such as SARS-CoV-2 in their usual mode of operation. This project will build on expertise developed during the SARS-CoV-2 pandemic to address key outstanding questions about the degree to which outbreaks can be mitigated while maintaining in-person activities, the extent to which institutional outbreaks are coupled to the epidemic in the surrounding community, and the role of demographic turnover and other seasonal effects.

In contrast to dynamics at the population-level, transmission dynamics in a university setting may be strongly influenced by the local contact network. This project will take a data-driven approach to motivate and construct a network or multitype epidemic model for a university, with opportunity to calibrate this model based on a variety of epidemiological data related to SARS-CoV-2 testing. This modelling may then be extended to explore a number of related open questions, such as the role of waning immunity on dynamics in endemic phases of viral circulation and/or the dynamics of respiratory disease transmission in other institutional settings.

We are looking for an applicant with excellent analytic and critical thinking skills, particularly as relevant for statistical analysis, inference, and modelling of deterministic and stochastic processes, with capability to interpret and communicate results. Ability to code in a scientific programming language (e.g. C/C++/python/R) is also required. Previous research experience, knowledge of epidemic modelling, and motivation to work on interdisciplinary problems, are desirable.