Efficiency of pathogen genomic surveillance systems
(GSS) depends on intelligent design based on best
practices and principles of sample survey
methodology. Large-scale genomic surveillance of
SARS-CoV-2 has provided a vivid demonstration of the
promise of GSS and revealed new challenges for the
analysis of epidemiological big data. Optimal design of
GSS must account for different types of analyses and
the expected outputs that are provided to
stakeholders. The implementation of new GSS’s will be
suboptimal or yield uninterpretable data if plans for
data collection are divorced from analytical pipelines.
This PhD project concerns the development of bespoke
sample designs for GSS, including sample size
calculations and power analysis leveraging clinical,
genomic and public health surveillance data. We will
conduct research into optimal design of GSS with a
view towards developing guidelines for design of GSS
to meet a wide variety of public health needs across a
wide variety of pathogens.

This project will involve the design of a GSS which
begins with the anticipated outputs of the surveillance
system and involves the optimisation of data collection
under resource and logistical constraints.
This project will require the Ph.D. candidate to gain skills in
pathogen genomic analysis, phylogenetics, population
genetics, epidemiological modelling, and sample-
survey methodology.

Objective 1. We will carry out research into GSS design
best-practices, including methods for sample size
calculation and the utility of different genomic data
streams, including clinical data, community sampling,
random household surveys, and environmental
sampling including wastewater surveillance. This will
involve the optimisation of sampling effort across data
streams and the investigation of surge-sampling
strategies.

Objective 2. Design choices are more difficult for
surveillance based on contact tracing or convenience
sampling. It is common for convenience sampling from
clinical sources to be used by necessity in GSS, and we
will provide methods and statistical modelling to de-
bias such samples using patient-level covariates. We
will develop methods tailored for clustered data,
contact tracing studies, and household surveys,
providing optimal genomic sequencing choices within
contact pairs or other highly correlated samples.

Objective 3. We will develop easy-to-use software
libraries and dashboard for sample design choices. This
will facilitate uptake of these procedures to a wider
community and translation to real-world applications.

*** Requirements ***
It is expected that the student will have a Master-level
degree or equivalent experience in a computational
field (bioinformatics, computational biology, computer
science) and/or a quantitative (mathematics, statistics)
field.