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Pandemics, Particles and Parameter (s)paces

The project "Pandemics, Particles and Parameter (s)paces" is a cross-disciplinary collaboration between theoretical physics at the University of Oslo and the Norwegian Institute of Public Health (NIPH/FHI) aimed at developing software to meet the United Nations Sustainable Development Goal 3, in particular Target 3.d: "Strengthen the capacity of all countries, in particular developing countries, for early warning, risk reduction and management of national and global health risks."

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About the project

Research on epidemic dynamics and particle physics may seem worlds apart, but a common challenge is limiting progress in both fields: the need to explore computationally heavy Monte Carlo simulations in high detail across many dimensional parameter spaces. Seeing as the two fields have similar computational challenges, we can in principle take advantage of the same computational tools to explore both fields. The theoretical physics group at UiO has been involved in the development of new tools to explore computationally heavy simulations in great detail. This software is called GAMBIT, however, so far its application to other fields has been limited. In this project, we will use GAMBIT to perform comprehensive, simulation-based explorations of epidemic dynamics and new particle physics theories and develop a discipline-agnostic lightweight tool for continual learning.

This research aims to meet the United Nations Sustainable Development Goal 3, in particular Target 3.d: "Strengthen the capacity of all countries, in particular developing countries, for early warning, risk reduction and management of national and global health risks.". Our project will enable large and data-driven exploration of agent-based simulations of epidemics. This will allow a detailed investigation of the efficacy, combined effects and interaction dynamics of various government interventions, knowledge paramount in preparation for possible future outbreaks.

On the physics side, our project will allow us to probe deeper into the space of possible new physics models. In doing so, we directly extend the scientific impact of expensive, publicly funded experiments such as the LHC, and may pave the way for spectacular discoveries in future searches.

Financing

This project is financed as a Sustainability Project by the Faculty of Mathematics and Natural Sciences, University of Oslo.

Tags: Particle physics, Public Health, Statistical learning, Machine learning, Inference, Data science, Computational physics
Published Sep. 23, 2023 11:51 AM - Last modified Aug. 2, 2024 10:46 AM

Contact

Are Raklev is the principle investigator for the project.