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

Prateek Kunwar; Oleksandr Markovichenko; Monique Chyba; Yuriy Mileyko; Alice Koniges; Thomas Lee; Prateek Kunwar; Oleksandr Markovichenko; Monique Chyba; Yuriy Mileyko; Alice Koniges; Thomas Lee

doi:10.3934/nhm.2022011

Networks and Heterogeneous Media

2022, Volume 17, Issue 3: 359-384. doi: 10.3934/nhm.2022011

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

1.
Department of Mathematics, University of Hawai'i at Mānoa, Honolulu, HI 96822, USA
2.
Data Science Institute, University of Hawai'i at Mānoa, Honolulu, HI 96822, USA
3.
Office of Public Health Studies, University of Hawai'i at Mānoa, Honolulu, HI 96822, USA

Received: 01 June 2021 Revised: 01 November 2021 Published: 23 March 2022
Primary: 92D30, 9208; Secondary: 68W01

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.
- SARS-CoV-2,
- Epidemiological Modeling,
- Agent-Based Model,
- Compartmental Model,
- Conceptual and Computational Complexity
Citation: Prateek Kunwar, Oleksandr Markovichenko, Monique Chyba, Yuriy Mileyko, Alice Koniges, Thomas Lee. A study of computational and conceptual complexities of compartment and agent based models[J]. Networks and Heterogeneous Media, 2022, 17(3): 359-384. doi: 10.3934/nhm.2022011

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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.

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