Speaker
Description
With the recent increase in computing power, agent-based simulations have become progressively more complex. There is a growing tendency to include greater detail of sexual networks, disease progression, and targeted interventions into STI models to answer relevant public health questions. However, increasing model complexity also amplifies data requirements, and often necessitates the integration of data from different sources for model parameterization. One key challenge in STI transmission modeling is the accurate representation of the dynamic partnership network. Formation and separation of partnerships depend on many factors, including demographics (age, sex), partnership history, infection status, and an individual’s perceived risk of acquiring infection. AI models offer a promising avenue to better integrate data analysis with agent-based modelling to link partnership dynamics directly to underlying determinants. In a first step to explore such a hybrid model, we integrated an XGBoost algorithm—trained on behavioural survey data—into a static network model to inform decisions of agents to engage in protective behaviour during an outbreak of a respiratory infection. We find that epidemic trajectories are influenced by underlying network structure as well as the adaptive behaviour of agents. For agent-based models of STI our approach may provide a flexible way to incorporate determinants of partnerships, predicting their formation or separation from behaviour surveys.
