A study of computational and conceptual complexities of compartment and agent based models

TL;DR

This paper compares equation-based and agent-based models for COVID-19 spread on Oahu, highlighting their similar predictive accuracy but differing in computational and conceptual complexity, guiding model choice based on resources.

Contribution

It provides a comparative analysis of two common modeling approaches for COVID-19, focusing on their performance and complexity in a real-world scenario.

Findings

Both models yield similar results for decision-making parameters.

Agent-based models are more computationally intensive.

Equation-based models are conceptually simpler.

Abstract

The ongoing COVID-19 pandemic highlights the essential role of mathematical models in understanding the spread of the virus along with a quantifiable and science-based prediction of the impact of various mitigation measures. Numerous types of models have been employed with various levels of success. This leads to the question of what kind of a mathematical model is most appropriate for a given situation. We consider two widely used types of models: equation-based models (such as standard compartmental epidemiological models) and agent-based models. We assess their performance by modeling the spread of COVID-19 on the Hawaiian island of Oahu under different scenarios. We show that when it comes to information crucial to decision making, both models produce very similar results. At the same time, the two types of models exhibit very different characteristics when considering their computational and conceptual complexity. Consequently, we conclude that choosing the model should be mostly guided by available computational and human resources.