Epidemiological SIR and SEIR ODE Models in Interdisciplinary Applications: Commonalities and Discipline-Specific Structural Differences

Currently, epidemiological models can not only be found in epidemiology but also in other research disciplines. However, an interdisciplinary perspective that highlights the commonalities of epidemiological models across disciplines is missing. The goal of the current study is to foster such a perspective. To this end, a methodology is used that sets the current study apart from traditional review studies. Two benchmark epidemiological models formulated in terms of coupled ordinary differential equations, the susceptible–infected–recovered model and the susceptible–exposed–infected–recovered model, are followed through eight disciplines: epidemiology, virus dynamics within humans, computer viruses, drug addiction, voter dynamics, rumor spreading, sales dynamics, and viral marketing. Structural similarities and structural differences across these disciplines within the context of these two models are worked out. It is shown how the exact same mathematical structure can be applied for quite different interpretations across the selected disciplines. It is also shown that more complex model variants exhibit structural differences across research disciplines. In this way, this study helps researchers compare their own works on a structural level with related works in other disciplines. The particular importance of the current study is that it can boost progress in epidemiological modeling by making researchers aware of an interdisciplinary perspective.