Periodic Reporting for period 1 - HHS-EWS (Operational Heat-Health-Social Early Warning System)
Reporting period: 2022-10-01 to 2024-03-31
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 aims to 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, we aim to calibrate epidemiological models to transform bias-corrected weather forecasts into predictions of health outcomes. To validate the path from ground-breaking research to innovation, we aim to 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 aims to develop an operational, fit-for-purpose early warning system representing the health impacts of environmental temperatures, which better inform potential end-users such as public health agencies to activate emergency plans directly targeting vulnerable groups.
Firstly, we generated a database of mortality, population and environmental data, including temperature observations and weather forecasts. This database was used to fit epidemiological models, and the resulting associations were used to transform temperature observations and forecasts into temperature-related mortality estimates and predictions. For that purpose, weather forecasts were bias-corrected following standard techniques in the field of weather and climate forecasting. Next, we validated the weather forecasts stored in our database (i.e. daily time series of temperature) against climate observations, and the health predictions (i.e. daily time series of temperature related mortality) against the registered mortality counts. We found that temperature forecasts can be used to issue skilful predictions of heat and cold related mortality accounting for the real impacts of temperature on human health, although the window of predictability was differently reduced by season and location. However, in general, we found that skilful forecasts can be issued beyond 15 days in advance in winter, and beyond 10 days in summer. Consequently, we decided not to set bounds to the predictability window of the operational platform. We also found that the predictability of the early warnings is, to a very large extent, constrained by the original weather forecasts, and not by the epidemiological models. These findings have been included in a paper that is currently in revision in Science Advances.
Finally, we developed an operational, fit-for-purpose, early warning system representing the health impacts that environmental temperatures have on the exposed populations in 33 European countries. The health predictions that appear in the early warning system platform are automatically updated and uploaded every day by a series of protocols and scripts that we wrote, and that are synchronised in real time with the time of release of the daily weather forecasts in the ECMWF data servers.
Finally, we developed an operational, fit-for-purpose, early warning system representing the health impacts that environmental temperatures have on the exposed populations in 33 European countries. The health predictions that appear in the early warning system platform are automatically updated and uploaded every day by a series of protocols and scripts that we wrote, and that are synchronised in real time with the time of release of the daily weather forecasts in the ECMWF data servers.
HHS-EWS has resulted in a breakthrough innovation in the area of health early warning systems due to its multiplicative effect. The driving force of state-of-the-art early warning systems is weather forecasting, from which operational early warnings are issued. HHS-EWS changes this paradigm by moving the centre of gravity of the system from meteorology to environmental epidemiology and social sciences. In this innovative, interdisciplinary concept, physical variables from weather forecasts are not the core of the early warnings. Instead, we applied epidemiological models to health data disaggregated by sociodemographic groups to calculate social-specific health indicators, which will be used to activate public health and emergency plans. In other words, HHS-EWS uses epidemiological models to transform the physical information of weather forecasts into a range of early warnings representing the real health risks of, for example, the elderly, children, women, people with pre-existing diseases or people at risk of exclusion. HHS-EWS thus represents a new generation of early warning systems better fitted for the purpose of explicitly accounting for the real risks and impacts of environmental hazards on the exposed population, including the social inequalities and gaps in vulnerability. In turn, these more precise early warnings can better inform potential end-users, such as public health agencies, to activate fit-for-purpose emergency plans directly targeting vulnerable groups.