AI for Signal analysis of Human behaviour: Identification, Forecasting and Transition dynamics (SHIFT)
Contact person: Francesco di Ruscio
Status: Project
Understanding and predicting changes in human behaviour is a complex challenge. Yet representing these changes is essential for mathematical models of real-world systems, such as traffic and mobility systems or infectious-disease transmission models. Human behaviour and societal activity leave signals across routinely collected data streams, including public transport usage, financial transactions, mobility data and electricity consumption. These data differ substantially in temporal and spatial resolution, structure and uncertainty, ranging from conventional time series to graph-based data.
This project will investigate how AI and machine-learning methods can combine such heterogeneous data sources to detect, characterise and forecast changes in behaviour and infer their impact on contact structures in near real time. The central methodological challenge lies in integrating signals with different structures, resolutions and uncertainties, and connecting them to mechanistic models through hybrid AI-mechanistic approaches. The ambition is to turn continuous societal data streams into dynamic representations of human behaviour that can update mathematical models as the systems they represent evolve. Rather than replacing mechanistic models with black-box predictions, the aim is to make them more adaptive while preserving their interpretability and explicit representation of underlying processes.
Infectious-disease modelling will provide a key application and methodological test case. Transmission is strongly influenced by changes in contact patterns, mobility and behaviour, which traditional mechanistic models often do not incorporate dynamically. Building on our agent-based modelling framework for infectious disease transmission, we will investigate how AI-derived estimates of changing contact structures can continuously inform the model, creating a digital twin of epidemic dynamics. This will allow us to test whether early signals of behavioural transitions can improve situational awareness, forecasting and the exploration of emerging transmission scenarios.
The findings will support public-health authorities by improving situational awareness and scenario analysis. More broadly, the methods could be applied to other complex societal systems modelled using heterogeneous and continuously changing data. User partners could contribute data, real-world use cases and domain expertise, and help validate both the identified behavioural signals and the practical usefulness of the resulting models.
Participating institutions: Norwegian Institute of Public Health (NIPH); Norwegian Computing Center (NR); Arctic University of Norway (UiT)
Key project members: Francesco Di Ruscio (NIPH); Birgitte F. de Blasio (NIPH); Jørgen E. Midtbø (NIPH); Filippo Maria Bianchi (UiT); Ingrid Dæhlen (NR); Geir Storvik (NR, UiO)
Contact person: Francesco Di Ruscio (francesco.diruscio@fhi.no)
Relevant Action Clusters: AC1 - AI in healthcare and medicine; AC10 - AI for time-series analysis and anomaly detection; AC11 - AI-guided decision making; AC12 - AI for security and preparedness; AC15 - AI for sciences.