Viruses, pollution and chronic disease: modern data scientific techniques to address drivers of disease at multiple scales

Health events, like asthma or heart attacks, are recorded at a
fine spatial scale – usually the first part of a patient’s
postcode is available. However, when researchers are
attempting to estimate the effect of potential triggers for these
events for example, air pollution or prevalence of viruses, the
data are often aggregated at a city or district level.
This PhD project will address this problem by combining
mathematical models of viral transmission for the pathogens
that can trigger these health events with data science
methods for estimating impact of environmental drivers
across geographic scales. The project will examine the
impact of air pollution on these disease events in concert with
viral circulation, and (if interested), in environmental change.
This project will allow the student to learn methods in
infectious disease transmission modelling, and how
pathogens spread through populations. They will also learn
about environmental epidemiology, especially in air pollution,
and new statistical techniques for parameter inference.
Further, the student will learn how to handle big data which
will make them well placed for future challenges in modern
epidemiology or data science. We are committed to
development in interdisciplinary skills such as writing,
presenting, and effective teamwork.

See for application details, and contact Rosalind Eggo for informal inquiries or more details.

PhD position
Closing date
January 1st, 2020
Posted on
October 25th, 2019 21:22
Last updated
October 25th, 2019 21:22