Data-driven analytics of disease spread under close contact for optimal testing and mitigation.

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Outbreaks of infection in close contact settings can have a major impact on the population involved. Examples include: blood-borne diseases in prisons; gastrointestinal illness at a major sporting event or on a cruise ship; and scabies in care homes.Enhanced techniques for early detection, and the deployment thereof, may enable better responses to such outbreaks. This project will work with multiple sources of data to build models for an exemplar set of diseases and contexts, and in doing so develop a general software framework for modelling tool creation to enable scenario planning for close-contact outbreaks.The work will be carried out in collaboration with Danaher (manufacturers of the GeneXpert System that is commonly known as the "Cepheid Machine"), who will ensure that the latest information on current and future fast diagnostic technologies is available to the student, and who will benefit from the availability of a modelling tool that can determine in which contexts faster diagnostics are going to be most beneficial to patients, healthcare and care systems, which will inform product development and marketing.The student will join a lively group of mathematical epidemiologists in the School of Mathematics at the University of Manchester (3 Faculty - House, Hall and Pellis, 3 Postdoctoral, 8 PhD) and will have the opportunity to collaborate with researchers in this group funded by the Alan Turing Institute for Data Science and Artificial Intelligence working on complementary projects. Dr. Ustianowski will also provide access to a rich collection of clinical research organisations in Greater Manchester and beyond (e.g. North Manchester General Hospital research, Greater Manchester Comprehensive Research Network, NIHR Infection National Specialty Group).Skills to be developed and trained during the project include mathematical modelling, statistical inference, working with efficient, modern computational algorithms, incorporation of economic, industrial and clinical aspects into epidemiological models.