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Computational disease spread models can be broadly classified into differential equation-based models EBMs and agent-based models ABMs. We examine these models in the context of illuminating their hidden assumptions and the impact these may have on the model outcomes. Drawing relevant conclusions about the usability of a model requires reliable information regarding its modeling strategy and its associated assumptions. Hence, we aim to provide clear guidelines on the development of these models and delineate important modeling choices that cause the differences between the model outputs. In this study, we present a quantitative analysis of how the choice of model trajectories and temporal resolution continuous versus discrete-event models, coupling between agents instantaneous versus delayed interactions, and progress of patients from one stage of the disease to the next affect the overall outcomes of modeling disease spread. Our study reveals that the magnitude and velocity of the simulated epidemic depends critically on the selection of modeling principles, various assumptions of disease process, and the choice of time advance. In order to inform public health officials and improve reproducibility, these initial decisions of modelers should be carefully considered and recorded when building and documenting an ABM.

Analyzing the impact of modeling choices and assumptions in compartmental epidemiological models