The COVID-19 pandemic showed the need for better tools to understand how infectious diseases spread and how health services respond to changing demand. Reliable models can help predict disease trends, study the effects of different measures, and estimate the need for hospital and healthcare resources. Traditional mathematical models are useful for many situations, but they often assume that people behave in similar ways. This can make it difficult to represent differences between individuals and their interactions.
Agent-based modelling (ABM) offers a more flexible approach. In an agent-based model, each person, animal, or other relevant unit is represented as an individual agent with specific characteristics and behaviours. The model describes how agents interact and how these interactions affect disease transmission and the use of healthcare resources. This makes it possible to study complex situations, such as isolation, vaccination, changes in behaviour, and different patterns of contact.
The project focuses on the development and application of agent-based models for infectious disease spread and healthcare logistics. The approach is compared with traditional modelling methods to explore their strengths and limitations. Policy and intervention optimization is also considered, with the aim of reducing infections and hospitalizations while taking economic costs into account.
SINTEFs contribution in the project
SINTEF contributes expertise in mathematical modelling, simulation, optimization, and machine learning. The work builds on previous experience in infectious disease modelling, healthcare logistics, and agent-based modelling. Modern computational methods are used to develop and simulate the models and to analyse complex interactions between individuals and systems. The resulting methods and software provide a basis for future research and decision-support applications in healthcare and other domains.