Modeling Complex Systems: A Case Study of Compartmental Models in Epidemiology

Figure 1

Schematic representation of the impact of various modeling choices/assumptions. The left column lists various details that can be incorporated into a compartmental model, and the right column lists typical potential impacts on the model output. The three panels classify the details by ‘scale,’ with the largest scale details typically having the most impact on model output, and the smallest scale details typically having the least impact, although the impact of any given assumption ultimately depends on precisely for what purpose the model is being used. Furthermore, various assumptions can compound nonlinearly to affect the model output. For instance, policy interventions such as travel restrictions, which both rely on and affect heterogeneity in geographic connectivity, can play a decisive role in determining whether or not a stable elimination is achieved [13]. Of course, the actual effect of any assumption depends on its precise mathematical implementation, as well as the presence or absence of other assumptions within the model, and so this figure should be considered as a rough schematic rather than as a definitive guide.