Operational Heat-Health-Social Early Warning System

Ambient temperatures are associated with more than 5 million annual deaths globally, 300,000 of which in Western Europe alone.

Many European countries have implemented heat early warning systems, but they are generally based on temperature thresholds from weather forecasts that do not account for the inequalities in vulnerability of the exposed populations.

This ERC-funded project will create the first operational Heat-Health-Social Early Warning System (HHS-EWS) by integrating weather forecasting, environmental epidemiology and the social drivers of vulnerability.

Towards this aim, I will calibrate epidemiological models to transform bias-corrected weather forecasts into predictions of health outcomes.

To validate the path from ground-breaking research to innovation, I will analyse and compare the spatiotemporal scales of predictability, and determine if the epidemiological models reduce or suppress the window of predictability of the weather forecasts.

HHS-EWS will develop an operational, fit-for-purpose early warning system representing the health impacts of environmental temperatures, which will better inform potential end-users such as public health agencies to activate emergency plans directly targeting vulnerable groups.