Climate-AI-infection-REsponse (CLAIRE)

Purpose and goal

The rapid warming of the Earth, caused by anthropogenic greenhouse gas emissions, has profound long-term implications for the prevention and control of infectious diseases. The CLAIRE project aims to develop robust climate-based algorithms and decision-making tools to support public health adaptation to infectious disease risks following from climate variability and change. The decision-making frameworks developed within the CLAIRE project will enable timely and coordinated public health responses to prevent climate-sensitive infectious disease in Sweden and Europe.

Expected results and effects

The decision-making frameworks developed and validated within the CLAIRE project will enable timely and coordinated public health responses to prevent climate-sensitive infectious disease emergence and disease incidence in Sweden and Europe. For this purpose, it will make use of AI technologies, such as data mining and machine learning, to provide new algorithms and prototype decisionmaking dashboards of value for public health protection, specifically tailored to the needs of the European Centre of Disease Prevention and Control (ECDC).

Planned approach and implementation

CLAIRE will first explore and investigate the climate-sensitivity of the disease, then establish predictive models at the relevant time scales using machine learning, and lastly, if predictions have sufficient predictive skill, CLAIRE will prototype dashboards for decision-making with embedded data pipelines, algorithms and interactive user-features. The work is structured into four interactive work packages.