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This week, the webinar “From classical epidemiological models to Explainable AI: building resilience against future pandemics” was held within the framework of the RESILIENCE project, an initiative focused on pandemic prediction and medical resource planning through Explainable Artificial Intelligence.

The session was led by Marco Hernández Pérez, collaborator of the BISITE Research Group and researcher in the project, who explained the role of Explainable AI in anticipating pandemics and supporting decision-making in public health — the main objective of the RESILIENCE project.

The COVID-19 crisis highlighted the need to promote Precision Medicine (PM), strengthen research, and collect and analyse data that enable the anticipation of pandemics or other highly transmissible viral diseases. This involves extracting epidemiological data from past pandemics and using technologies such as Explainable AI (XAI) to prevent the spread of pandemics through evidence-based models, as well as generalising these models to address unknown future outbreaks of other diseases.

The event continued with the participation of Simón Mariño Perea, researcher in the BISITE Research Group, who explained the importance of anticipating epidemics, their potential social impact, and some of the scientific challenges arising from new lines of research.

Finally, Pablo Enrique Guillem Fernández, PhD candidate at the University of Salamanca, explained how mathematical modelling and machine learning can be used to anticipate outbreaks or pandemics.

The RESILIENCE project represents a major challenge led by the BISITE Research Group and a valuable contribution to scientific research, with Explainable Artificial Intelligence as its central pillar.

If you were unable to attend the webinar, you can watch the full session here.

RESILIENCE is a project funded by the Spanish State Research Agency (MCIN/AEI /10.13039/501100011033) and the European Union NextGenerationEU/PRTR (ref. CNS2022-135101).