There are several reasons for increasing model complexity in epidemiology:
Accuracy: Simple models may overlook critical variables, leading to inaccurate predictions. Complex models can provide a more realistic representation of disease dynamics. Granularity: Complex models can offer detailed insights at finer spatial and temporal scales, which is essential for localized interventions. Policy Guidance: Policymakers require detailed models to evaluate the potential impact of different intervention policies.
