×F8. Integrated modelling & data science for environmental and human health

This session will bring together researchers from environmental and health sciences addressing the complex challenges of integrating data and modelling approaches, with the aim order to harness the opportunities emerging for a fast increasing richness of environmental and health data. Pilot research in the UK has focused on how linking health and population records to forecasts of near-term air pollution can help to inform learning health systems and improve the management of frontline health services and patient health and well-being. Research focusing on life-course exposure and exposomics highlight the need for integrated modelling approaches and novel data science methods (e.g. blending, data cubes, deep/machine learning) to utilise long-term datasets, informing next-generation models of human exposure at individual to population level. We invite contributions focusing on methods for modelling and simulation of human-environment interactions, exposure and epidemiological studies with an emphasis on the utilisation of models and emerging environmental datasets (e.g. earth observation products from the SENTINEL satellites), as well as new methodological approaches for the integration of large, rich datasets. Our emphasis in this session will be on fostering an interdisciplinary dialogue between computer & data scientists, environmental researchers and public health experts to generate new ideas.

We would welcome specific case studies in bringing together human health and environmental data, with a focus on current and emerging challenges where modelling and simulation can play a vital role in promoting progress towards ecological public health and the attainment of sustainable development goals.

Key topics: Environmental pollution, Public health, Integrated modelling, Data science