A comparison of random mixing in a structured agent-based model with empirical contact survey data

Agent-based models (ABMs) are powerful tools for simulating disease spread, relying on individual-level representations and interaction rules from which emergent dynamics arise. These rules need to be accurately specified as minor differences can lead to vastly different disease dynamics. An important component in ABMs is the contact behaviour. To decrease the computational complexity the contact behaviour is often assumed as random mixing within settings. Here, individuals randomly contact other individuals associated with the same settings, such as colleagues in their workplace, where the setting associations are based on empirical data such as census data. However, the validity of the random mixing assumption within settings remains unclear. We address this gap by comparing the contact structure in a large-scale ABM (GEMS) with empirical contact survey data (COVIMOD). We compare the age contact matrices for households, schools, workplaces, all remaining contact settings combined, and all contacts combined. This includes calculating the difference matrix and the sum of squared errors (SSE), i.e. the element-wise squared difference. Our results demonstrate that random mixing in settings generated based on known age-compositions like households (SSE:0.7(95%CI0.4–0.9)), schools (SSE:0.7(95%CI:0.3–1.1)) and workplaces (SSE:0.5(95%CI:0.2-0.7)), can capture the basic interaction patterns. However, it fails to account for age-related variations in contact numbers, leading to discrepancies between the simulated and observed contact behaviour. The largest differences arise for contacts outside of households, schools and workplaces (SSE:3.8(95%CI:1.2–6.5)), due to the model’s structure. These contacts are modelled as random regional contacts not capturing the age-structured behaviour observed in COVIMOD. We conclude that random mixing in accurately defined settings provides an approximation for contact structures in settings where the age-structure of associated individuals is similar to the observed contact structure. For settings where the age-structure deviates from the contact structure, advanced methods are required to represent real-world contact structures.