|Funding for:||UK Students, EU Students, International Students|
|Placed On:||15th October 2021|
|Closes:||15th January 2022|
Supervisor: Dr Olatunji Johnson
Funding Details: This is a 3 year funded PhD studentship covering fees and stipend (£15,609 in 2021-22)
Open to all applicants.
Start Date: September 2022
Predicting low prevalence is a pressing problem in global public health settings, especially in diseases that have received many interventions. As intervention programs intensify, the prevalence of the diseases declines and precise prediction of disease prevalence becomes more difficult. In particular, the logistic-type geo-statistical model commonly used for prevalence mapping can lead to imprecise predictions when the disease prevalence is low. If there are many (structured or random) zeros in the data, zero-inflated models provide a flexible way to address it, but no extensive study has been done on data containing very low prevalences. One solution for predicting low-prevalence is the use of very informative covariates, however, this is not usually available in most epidemiological settings.
In this project, the student will:
Academic background of candidates
Applicants are expected to hold, or be about to obtain, a minimum upper second class undergraduate degree (or the overseas equivalent) in a relevant subject area. A Masters degree in a relevant discipline is desirable.
Please follow the link below to submit your application:
Please contact the PGR Maths Team for further information - firstname.lastname@example.org
Type / Role: