  Mathematical models play a crucial role in understanding the spread of
infectious disease outbreaks and influencing policy decisions. These models aid
pandemic preparedness by predicting outcomes under hypothetical scenarios and
identifying weaknesses in existing frameworks. However, their accuracy,
utility, and comparability are being scrutinized. Agent-based models (ABMs)
have emerged as a valuable tool, capturing population heterogeneity and spatial
effects, particularly when assessing intervention strategies. Here we present
EpiGeoPop, a user-friendly tool for rapidly preparing spatially accurate
population configurations of entire countries. EpiGeoPop helps to address the
problem of complex and time-consuming model set up in ABMs, specifically
improving the integration of spatial detail. We subsequently demonstrate the
importance of accurate spatial detail in ABM simulations of disease outbreaks
using Epiabm, an ABM based on Imperial College London's CovidSim with improved
modularity, documentation and testing. Our investigation involves the interplay
between population density, the implementation of spatial transmission, and
realistic interventions implemented in Epiabm.
