Equation-Based Modeling vs. Agent-Based Modeling with Applications to the Spread of COVID-19 Outbreak

Abstract

In this paper, we explore two modeling approaches to understanding the dynamics of infectious diseases in the population: equation-based modeling (EBM) and agent-based modeling (ABM). To achieve this, a comparative study of these approaches was conducted and we highlighted their advantages and disadvantages. Two case studies on the spread of the COVID-19 pandemic were carried out using both approaches. The results obtained show that differential equation-based models are faster but still simplistic, while agent-based models require more machine capabilities but are more realistic and very close to biology. Based on these outputs, it seems that the coupling of both approaches could be an interesting compromise.

Hybridizing intra and extra perspectives in infectious disease modeling

“…In the continuation of the above-mentioned work, several other researchers have contributed to the study of infectious disease dynamics. In [3], a SEIAR (Susceptible-Exposed-Infectious-Asymptomatic-Recovered) model is compared to an agent-based one to understand the dynamics of COVID-19 a cure for an infectious disease, certain measures should be taken to mitigate the damage. Mathematical models supported by computer simulations allow for determining the factors that most in luence the spread of disease.…”

“…Among such models, we can mention SI, SIS, SIR, SEIR, SEIRS, SEIRD, etc. This approach provides an analysis of the model's pure mathematical characteristics such as the positivity of the solution, the uniqueness of the solution, the endemic and the disease-free equilibria, the stability of equilibrium points, and the basic number of reproduction [3].…”