Agent-based evolving network modeling: a new simulation method for modeling low prevalence infectious diseases
Matthew Eden1, Rebecca Castonguay1, Buyannemekh Munkhbat1
1Mechanical and Industrial Engineering, University of Massachusetts Amherst, Amherst, MA, 01003, USA.
Health Care Management Science
|May 15, 2021
Summary
Agent-based evolving network modeling (ABENM) offers a computationally feasible approach for simulating infectious diseases, particularly those with low prevalence. This hybrid method efficiently models disease spread in complex networks.
Area of Science:
- Computational epidemiology
- Network science
- Mathematical modeling
Background:
- Agent-based network modeling (ABNM) is effective for infectious disease dynamics but computationally infeasible for low prevalence diseases.
- Simulating individual-level disease spread requires detailed contact networks, which are challenging for large populations.
Purpose of the Study:
- To introduce agent-based evolving network modeling (ABENM), a novel hybrid simulation technique.
- To address the computational limitations of ABNM for low prevalence diseases like HIV.
Main Methods:
- ABENM combines agent-based modeling for infected individuals and their contacts with compartmental modeling for susceptible individuals.
- Utilizes the Evolving Contact Network Algorithm (ECNA) for generating scale-free networks, reflecting real-world social and transmission structures.
- ECNA employs graph theory principles for network generation.
Main Results:
- ABENM demonstrated promising results when compared to traditional ABNM for disease trajectories on scale-free networks.
- The hybrid approach offers a computationally efficient alternative for simulating diseases with low prevalence.
Conclusions:
- ABENM provides a viable and efficient method for studying infectious diseases in low prevalence scenarios.
- The technique is particularly relevant for diseases like HIV that spread through intricate contact networks.
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