Data modelling to optimise emergency healthcare responses to terrorism.

Lead Research Organisation: University of Manchester
Department Name: Mathematics


There is an absence of data internationally to inform civilian health systems planning for terrorist attacks. During such events pre-planned and rehearsed decision support mechanisms are essential to improve organisation of complex public health services and systems [1,2]. The patient presentations and timing of arrival in healthcare systems will be scenario dependent and subject to complex constraints [2]. The nature of the interaction of a terrorist incident and healthcare service responses will, therefore, be highly stochastic and ideally placed for analysis with state of the art artificial intelligence techniques.

Manchester Academic Health Science Centre has a unique detailed real-world dataset on the care pathways of casualties arising from the recent terrorist event [1, 2] and detailed NHS systems response data at individual hospital and system network scales. The studentship will have full data access under established ethics arrangements (NHS Health Research Authority: Confidential Advisory Group Section 251), and from international simulation training exercises [3] to develop mathematical models. Models will consider the dynamic constraints on health systems looking at patient flows from incident scene and through acute hospital care. The aim will be to identify patient pathways, healthcare surge and key bottlenecks as well as understand the scale of events and offer advice on mitigation of impact on health system resilience.

Close supervision by clinical experts will be essential for meaningful tools to be developed. They will benefit from the School of Mathematics expertise and facilities and have access to experts within Faculty of Medicine and Biology, the Thomas Ashton Institute and Alan Turing Institute. This project will supplement efforts on a potential EPSRC programme grant and, because of the nature of terrorism, will link to the University 'Global Inequalities' Research Beacon through established partnership with the Humanitarian Conflict and Response Institute.

Once real-world datasets are understood, mechanistically motivated discrete event simulation tools will be developed for individual hospitals initially but increasing complexity to account for trauma networks. These tools can address setting specific questions but work will also consider simplification and generalisation of results. Furthermore, these simulation models will be fused with predictive modelling techniques to enable investigation of care pathway choices and their optimisation. This should facilitate learning and advice about how the complex care pathway could be improved. The ultimate aim will be the creation of a "virtual reality" training tool to help clinical decision makers learn from past terrorist events affecting healthcare settings at local levels.


10 25 50