PhD Studentship: Dynamic Infection Control and Environmental Optimisation in Healthcare (DICE-H)

Key Skills

Effective communication skills, Mathematical modeling

Job Description

Funding

EPSRC Doctoral Training Partnership Studentship offering the award of fees, together with a tax-free maintenance grant of £19,237 per year for 3.5 years. An additional top up of £3,000 per year for 3.5 years is also available to previous graduates of the University of Leeds.

Lead Supervisor’s full name & email address

Dr Marco-Felipe King m.f.king@leeds.ac.uk 

Co-supervisor name(s)

Dr Amirul Khan a.khan@leeds.ac.uk

Dr Martin Lopez-Garcia m.lopezgarcia@leeds.ac.uk

Project summary

Background:

The evolution of healthcare facilities is pivotal for effective patient care and safety. However, hospitals often grapple with the dual challenges of infection control and energy efficiency, particularly in the context of anticipated climate changes. Traditional environmental controls in hospitals, while focusing on infection transmission via air, close-range interactions, and surface contact, have not adequately addressed the emerging needs for energy optimization and climate resilience. The anticipated rise in temperatures by 2050 underscores the urgency for future-proofed healthcare infrastructures.

Hypothesis:

We hypothesise that there are diminishing returns from net-zero energy consumption when also accounting for minimising infection risk in hospital patient and staff areas by 2050.

Aims and Objectives:

The student will advance an existing Discrete Event Simulation(DEVS) Quantitative Microbial Risk Assessment (QMRA) model, integrating multiple transmission pathways to evaluate patient spaces in terms of infection control in hospitals while simultaneously optimising energy usage. The primary aim is to further develop and validate through in-situ measurement of environmental parameters a DEVS QMRA model that holistically considers airborne, close-range, and surface transmission of infections. This model will balance environmental controls, keeping in view the dual objectives of infection risk mitigation and energy efficiency.

Depending on the background of the student, they could choose to focus on or combine the following:

• Agent-Based Discrete Event Simulation Development: Develop the agent-based DEVS model to predict human behavior and airflow movement in hospital settings.
• Model Calibration with Behaviour Data: Calibrate the model against existing human behavior data collected during the HECOIRA project, ensuring the model reflects real-world scenarios within hospital environments. – Dan Peckham to provide staff rotas and contextual information.
• Model Calibration with Ventilation Data: Calibrate the model's parameters specifically for inter-zonal ventilation rates and energy usage. This will involve air quality measurements using wireless sensors within NHS St James’ in Leeds to align the model with practical energy demands and airflow dynamics.
• Evaluate Microbial Exposure from Surface Contmination: Refine the quantitative pathogen exposure model as part of the DEVS QMRA framework. This model will enable a comparison of the relative risk of air and surface transmission and assess the effectiveness of different design and infection control strategies. Prof. Dancer to provide microbiological data and scenario testing.

This project represents a significant step forward in the field of healthcare facility management and infection control. By developing and implementing an advanced DEVS QMRA model, the project aims to optimise hospital environments for better infection control and energy efficiency, addressing both current needs and future challenges posed by climate change. The integration of real-world data and collaborative efforts with healthcare professionals ensures that the solutions are not only innovative but also practically applicable.

Please state your entry requirements plus any necessary or desired background

First or Upper Second Class UK Bachelor (Honours) or equivalent

Subject Area

Civil & Structural Engineering, Applied Mathematics