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Plague hazard assessment toolset

  • Full or part time
    Prof C Molina-París
  • Application Deadline
    Thursday, February 28, 2019
  • Funded PhD Project (European/UK Students Only)
    Funded PhD Project (European/UK Students Only)

About This PhD Project

Project Description

The pathogen of interest in this project is Yersinia pestis, a gram-negative bacterium and the causative agent of plague, a disease of humans and mammals which has been recognised since antiquity. Far from being obsolete, plague remains endemic in many parts of the world surviving in animal reservoirs.

The World Health Organisation estimates that approximately 800 cases are reported worldwide each year. Thus, plague remains a serious public health concern. Since it is highly pathogenic and can only be handled at the highest levels of biological containment, very few organisations are able to work with plague.

This PhD project aims to develop new within-host mathematical and computational models of Yersinia pestis infection acquired via the inhalation route and its treatment. In particular, the aim of this PhD project is to develop a plague hazard assessment mathematical and computational toolset, which can be used to compute the probability that an exposed individual will become infected as a function of dose of pathogen.

The mathematical and computational toolset will consist of two principal modules:
1) Human infection module: a within-host, mechanistic, stochastic model of the infection process of Yersinia pestis within humans will be developed. This module will predict the probability of infection and the time to symptoms for an individual exposed to a given dose of Yersinia pestis. Published data from animal models (primarily primate) will be leveraged, together with available human clinical case data.
2) Medical treatment module: a within-host mechanistic model of the effect of a range of treatments (e.g., different varieties of antibiotic) will be developed, allowing different dosing strategies to be tested.

Published pharmacokinetic data will be leveraged in model development.

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