Using artificial intelligence to make epidemic models more realistic and fair

Yamir Moreno

Universidad de Zaragoza, Spain

During epidemics, human behaviour influences how diseases spread and how effective public-health measures are. Decisions about vaccination and adherence to recommendations depend on trust, information, misinformation, social norms and socioeconomic factors. But traditional epidemic models often simplify these factors, making it difficult to predict how interventions will affect different groups. This project aims to create models that reflect social diversity and behavioural change more realistically.

The team combines expertise in behavioural science, health inequalities, artificial intelligence, epidemic modelling and policy engagement. The researchers will build virtual populations in which simulated individuals have distinct beliefs, behaviours and social conditions. Then, they will combine them with epidemic models and test how disease-control strategies perform across diverse communities.

The results could help identify which interventions are likely to work, for whom and under which circumstances. This, in turn, could help inform public-health authorities for developing epidemic responses that are more effective, fair and adapted to real-life social conditions.

• Joan Benach, Universitat Pompeu Fabra (UPF), Spain

• Angel Sánchez, Universidad Carlos III de Madrid, Spain

AI-Driven Behavioural Epidemic Models for Intervention Design

€970,139.00