Skip to main content Skip to navigation

Mathematics for Real-World Systems Centre for Doctoral Training

Mark Lynch

I am a third year PhD Student at the Mathematics for Real World Systems (MathSys II) CDT. My project revolves around applications of machine learning and neural networks to epidemiological models. In particular, the use of Physics Informed Neural Networks to predict hidden utility functions that are being optimised by disease dynamics. Some of my current research includes epidemiological models that incorporate human behaviour dependent on infection status. More specifically, how would rational individuals behave if they acted more selfishly vs acting for the social good.

I am a Fellow of the JSPS Summer ProgrammeLink opens in a new window 2023, which provides opportunities for young pre- and post-doctoral researchers from North America and Europe to receive an orientation on Japanese culture and research systems and to pursue research under the guidance of host researchers at a Japanese university or research institute over a period of two months during the summer.

Recorded Talks

Publications

Integrating human behaviour and epidemiological modelling: unlocking the remaining challengesLink opens in a new window

Edward M. Hill, Matthew Ryan, David Haw, Mark P. Lynch, Ruth McCabe, Alice E. Milne, Matthew S. Turner, Kavita Vedhara, Fanqi Zeng, Martine J. Barons, Emily J. Nixon, Stephen Parnell and Kirsty J. Bolton | 2024 | Mathematics in Medical and Life Sciences

This paper is part of a special issue on Behavioural Epidemiology. Historically, responses to health-related emergencies (whether public health, veterinary health or plant health related) have exposed the deficiencies of mathematical models to incorporate data-driven and/or theoretical knowledge on outbreak behavioural dynamics. Interdisciplinary collaboration is vital to improve realism in methodological approaches to considering behavioural dynamics in an unfolding situation. We must bring together novel ideas across the behavioural, biological, data and mathematical sciences. The purpose of our article is threefold. We first present our perspective on the vital role of interdisciplinary collaboration to enable the effective integration of the dynamics of human behaviour and epidemiological models – we refer to such integrated models as “epidemiological-behavioural” models. We then summarise issues to be resolved by interdisciplinary teams of experts within four contemporary epidemiological-behavioural modelling challenge areas that we consider to require immediate and sustained research attention:understanding of human behaviour; data; modelling methodologies and parameterisation; how modelling (and communication of its findings) affects behaviour. Lastly, to serve as a resource for research scientists, practitioners and policy makers interested in getting involved in tackling these epidemiological-behavioural modelling challenges, we pose recommendations to make progress in each of the challenge areas and our viewpoint on their potential societal benefits if enacted.

Projects

  • Nash Neural Networks (PhD Project, 2022 - current)

Supervised by: Matthew TurnerLink opens in a new window (University of Warwick); Ryoichi YamamotoLink opens in a new window, John MolinaLink opens in a new window (Kyoto University); Simon SchnyderLink opens in a new window (University of Tokyo)

External Partners: Ryoichi YamamotoLink opens in a new window, Department of Chemical Engineering, Kyoto University.

  • Inferring the Utility from Optimal Behaviour in an Epidemic using Neural Networks (MSc Individual Project, Summer 2022)

Supervised by: Matthew TurnerLink opens in a new window (University of Warwick); Ryoichi YamamotoLink opens in a new window, John MolinaLink opens in a new window (Kyoto University); Simon SchnyderLink opens in a new window (University of Tokyo)

External Partners: Ryoichi YamamotoLink opens in a new window, Department of Chemical Engineering, Kyoto University.

  • Graph Based Anomaly Detection in Healthcare Data (MSc Group Project, Spring 2022)

Group Members: Mark Lynch, Yueting HanLink opens in a new window, Obed NwachukwuLink opens in a new window

Supervised by: Marya BazziLink opens in a new window (University of Warwick)

External Partners: KirontechLink opens in a new window

Presentations and Conferences

Upcoming
  • ...coming soon
Previous

Education

  • PhD, Mathematics of Systems, University of Warwick MathSys II CDT (Oct 2022 - present)
  • MSc, Mathematics of Systems, University of Warwick MathSys II CDT (Sep 2021 - Sep 2022)
  • BSc (Hons), Mathematics, University of Warwick Mathematics Institute (2018-2021)

Other Responsibilities

Picture of Mark Lynch

Email: Mark.Lynch@warwick.ac.uk

Office: D2.05, Zeeman Building

LinkedInLink opens in a new window

GitHubLink opens in a new window