Agent-based models of malaria transmission: a systematic review

Area of Science:

• Mathematical modeling
• Epidemiology
• Computational biology

Background:

• Mathematical modeling, particularly compartmental models, has long been central to malaria transmission research.
• Agent-based models (ABMs) are increasingly adopted for their potential to enhance realism by simulating individual-level interactions.
• ABMs allow system-level behaviors to emerge from accumulated individual interactions, mirroring real-world population dynamics.

Purpose of the Study:

• To systematically review agent-based models (ABMs) used in malaria transmission research.
• To characterize current ABM approaches, identify their advantages, and propose future directions.
• To explore the utility of ABMs in informing malaria elimination strategies.

Main Methods:

• A systematic review of 90 articles published between 1998 and May 2018 was conducted.
• Articles focusing on agent-based models (ABMs) relevant to malaria transmission were analyzed.
• The review synthesized approaches, advantages, and future potential of ABMs in this field.

Main Results:

• Agent-based models (ABMs) are advantageous for accurately representing stochasticity in low-transmission settings.
• High-resolution spatial simulations and individual-level heterogeneities in drug/vaccine efficacy are key benefits of ABMs.
• Potential extensions include spatial landscape variations, larger model scales, human movement dynamics, and improved parameter estimation.

Conclusions:

• The literature covers diverse transmission and intervention scenarios, suggesting a common framework could aid malaria elimination.
• Standardization of ABM implementation may not be feasible; contextually appropriate and well-described models are recommended.
• Enhanced parameter estimation and optimization techniques are crucial for robust results to support malaria elimination efforts.