Leveraging probabilistic AI for heat-related health risk mitigation and adaptation

Background

With temperature records having been broken repeatedly in recent decades, heat-stress is posing a serious health risk (e.g. the fatalities in the 2007 London marathon). This project aims to quantify the spatially-and-temporally varying heat-risk in the UK and to understand how this is affected by climate change. The project will leverage probabilistic (Bayesian) AI methods for modelling weather and health data provided by the UK Met Office, plus access to operational weather forecasts and climate projections. The project is at the interface between AI, environmental science, meteorology and epidemiology. Skills such as machine learning, environmental and health data manipulation, risk mapping and decision making under uncertainty are expected to be gained by the student, who will have the chance to be hosted at the Met Office as a visiting scientist.

PhD opportunity

The first challenge is the question of how to best use machine learning methods for understanding the relationship between heat-stress and health outcomes. Heat-stress is a combination of unfavourable temperature, humidity and wind-speed over a generally unknown number of hours/days etc. Appropriate data modelling tools will need to be utilised to understand health-risk as a function of heat-stress, allowing for socio-economic factors of the population-at-risk, in addition to the inherent spatio-temporal variability.
Next, is the question of how to use the estimated risk in mitigation and adaptation strategies. For mitigation, prescriptive (decision making) approaches for issuing health warnings will be investigated. For adaptation, future climate projections of heat-risk will be computed and contrasted for various climate change scenarios and climate models. Statistical machine learning methods will be used to probabilistically quantify the uncertainty from the various sources of climate projections, enabling their use in decision making.
Expertise and data from the (UK) Met Office (MO) will be available towards tackling these challenges, noting that the current ‘heat-health alert service’ is co-managed by the MO.

Other information

Applicant profile: This project would suit students with a strong background in mathematics/statistics/machine learning or other appropriate quantitative background, who want to apply these skills to environmental epidemiology and more generally the interface between climate change and health.

  • https://www.metoffice.gov.uk/weather/warnings-and-advice/seasonal-advice/heat-health-alert-service
  • https://www.metoffice.gov.uk/research/climate/climate-impacts/health