Plague Hazard Assessment Toolset

Lead Research Organisation: University of Leeds
Department Name: Applied Mathematics


The agent of interest in this project is Yersinia pestis, which is a gram-negative bacterium and the causative agent of plague, a disease of humans and mammals which has been recognised since antiquity. Plague is notorious for being the disease associated with the Black Death during the 14th century, one of the most devastating pandemics in world history. However, far from being obsolete, plague remains endemic in many parts of the world (e.g., Madagascar, Peru, DRC, USA), surviving in animal reservoirs, such as small mammals and their fleas. Thus, plague remains a serious public health concern.

This PhD project aims to develop new within-host mathematical models to describe the process of Y. Pestis infection acquired via the inhalation route and its treatment. These models will shed some light on the intra-cellular and within-host infection dynamics following exposure of the pathogen. Furthermore, they will allow us to quantify the efficacy of the existing and available medical treatments and intervention strategies when dealing with this infection.

The main objectives of this PhD project are:

(O1) to develop a human infection module; a within-host, mechanistic, stochastic model of the infection process of Y. pestis within humans, which will allow us to predict the probability of infection and the time to symptoms for an individual exposed to a given dose of the pathogen.

(O2) to develop a medical treatment module, which will allow us to assess the effect of a range of treatments, e.g., different kinds of antibiotics, and taking into account different dosing strategies to be tested.

(O3) to integrate modules in (O1)-(O2) to provide a toolset that can be used to predict the consequences of a deliberate aerosol release of Y. pestis, and to investigate optimal treatment strategies in both military and civilian contexts. The toolset will allow different medical countermeasures to be tested in silico and will also allow alternative scenarios, such as antibiotic resistance, to be explored.

(O4) to calibrate models in (O1)-(O3) by means of Bayesian inference statistical techniques, leveraging published data, as well as data available from the team at Dstl.

Applications and benefits: We aim to develop a mathematical/computational tool, which can be tested and validated, during the project, to be used by Dstl to provide quantitative advice to decision-makers in UK Government.

Mathematical, theoretical and methodological novelty: When analysing the Human infection module in (O1), we will make use of novel methodologies related to the study of multi-dimensional stochastic processes and structured Markov chains. Moreover, the study of these systems will lead to the analysis of probabilities and times until absorption for continuous-time Markov chains, so that recent results related to phase-type distributions (Castro et al. (2018) Scientific Reports) and structured Markov chains (Gomez-Corral & Lopez-Garcia (2018) Numerical Linear Algebra with Applications; Lopez-Garcia et al. (2018) Royal Society Open Biology) will be adapted and extended for these settings. Moreover, recent methodological techniques for the analysis of multi-scale infection dynamics (Carruthers et al. (2018) Frontiers in Microbiology; de la Higuera et al. (2019) Frontiers in Immunology) will be adapted and extended for the Medical treatment module in (O2), and the integrated model in (O3).

Alignment with EPSRC remit: This project falls in the remit of the Global Uncertainties theme, where one of the core elements is terrorism and BWAs. Our project belongs both to the Mathematical Biology and to the Statistics and Applied Probability EPSRC research areas. Within the Mathematical Biology area, EPSRC considers "developing better solutions to acute threats: cyber, defence, financial and health" as a resilient ambition. Both Resilient nation and Healthy nation are two of the four main goals identified in the EPSRC vision, strategic priorities.


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Studentship Projects

Project Reference Relationship Related To Start End Student Name
EP/N509681/1 30/09/2016 29/09/2021
2274469 Studentship EP/N509681/1 30/09/2019 29/11/2019 Phoebe Barnes
EP/T517562/1 30/09/2019 29/09/2024
2274469 Studentship EP/T517562/1 30/09/2019 29/11/2019 Phoebe Barnes