🧭 Overview
The MOMA (Model Of Mosquito Aedes) is a spatially explicit Agent-Based Simulation Model designed to investigate the population dynamics of the female Aedes aegypti mosquito, the principal vector of Dengue virus. The model represents mosquitoes as individual agents interacting with a heterogeneous environment, allowing localized biological processes and spatial constraints to collectively generate emergent population-level dynamics. This framework is particularly well suited for urban and peri-urban settings where mosquito ecology is strongly shaped by fine-scale environmental variability.
🧱 Compartmental Structure and Life-Cycle Flow
MOMA explicitly tracks the biological life cycle of Aedes aegypti through discrete developmental stages. Each mosquito agent transitions through states based on age, environmental suitability, and stochastic survival rules.
Vector Life-Cycle Structure:
• Egg
Initial developmental stage requiring stagnant water. Eggs hatch into larvae when environmental conditions are favorable.
• Larva
Aquatic feeding stage subject to density-dependent competition and environmental constraints.
• Pupa
Non-feeding transitional stage preceding adult emergence.
• Adult Mosquito
Active phase. Female adults seek blood meals, disperse spatially, and lay eggs. Female mosquitoes are the primary agents of interest due to their epidemiological role.
Environmental Structure:
• Breeding Resources and Spatial Constraints
Water containers, temperature, and urban structure regulate development rates, survival, flight range, and oviposition behavior.
The overall flow follows:
Egg → Larva → Pupa → Adult Female → Death
Population dynamics emerge from the aggregation of individual-level transitions occurring within the spatial environment.
📐 Mathematical Formulation (Mean-Field Approximation)
Although MOMA is implemented as a stochastic Agent-Based Model, its expected population behavior can be summarized using a mean-field life-cycle formulation representing average transition rates between stages.
Let E(t), L(t), P(t), and A(t) denote egg, larval, pupal, and adult female mosquito densities.
The expected dynamics are:
dE/dt = b · A − (σₑ + μₑ) · E
dL/dt = σₑ · E − (σₗ + μₗ) · L
dP/dt = σₗ · L − (σₚ + μₚ) · P
dA/dt = σₚ · P − μₐ · A
Where:
• b is the oviposition rate of adult females
• σₑ, σₗ, σₚ are stage-specific development rates
• μₑ, μₗ, μₚ, μₐ are mortality rates
In the full ABM, these transitions occur stochastically at the individual level and are explicitly influenced by temperature, spatial proximity to breeding sites, and environmental heterogeneity.
🔢 Parameter Definitions
Table 1. Model Parameters and Definitions
| Parameter | Definition |
|---|---|
| b | Oviposition rate of adult female mosquitoes |
| σₑ | Egg-to-larva development rate |
| σₗ | Larva-to-pupa development rate |
| σₚ | Pupa-to-adult emergence rate |
| μₑ | Egg mortality rate |
| μₗ | Larval mortality rate |
| μₚ | Pupal mortality rate |
| μₐ | Adult female mortality rate |
| T | Ambient temperature influencing development and survival |
| H | Availability of aquatic breeding habitats |
📊 Typical Parameter Ranges
Table 2. Typical Parameter Ranges for Aedes aegypti Dynamics
| Parameter | Typical Range |
|---|---|
| Adult female lifespan (1/μₐ) | Approximately 20–30 days (observed range up to ~75 days) |
| Egg-to-adult development time | 7–15 days (strongly temperature dependent) |
| Optimal temperature for population growth | 30–32 °C |
| Oviposition frequency | One gonotrophic cycle every 3–5 days |
| Daily adult mortality probability | 0.03–0.06 |
| Larval mortality probability | Highly variable; density and environment dependent |
🌍 Applicability and Limitations
Applicability
MOMA is particularly effective for studying dengue vector ecology in urban environments. It supports the evaluation of localized vector control strategies such as larval habitat removal, targeted insecticide deployment, and environmental modification. The model is well suited for scenario analysis where spatial heterogeneity and individual behavior are central drivers of mosquito population dynamics.
Strengths
The Agent-Based structure enables explicit representation of spatial heterogeneity, individual variability, and environmental constraints. MOMA provides high-resolution insight into mosquito movement, breeding behavior, and population clustering that cannot be captured by aggregate compartmental models.
Limitations
The model is computationally intensive, with simulation cost scaling directly with mosquito population size. Accurate parameterization requires fine-grained environmental, climatic, and land-use data. Additionally, pathogen transmission is not intrinsic to MOMA and must be coupled to a human infection model for full epidemiological analysis.
📚 Selected References
Maneerat, S., & Daudé, E.
A spatial agent-based simulation model of the dengue vector Aedes aegypti to explore its population dynamics in urban areas.
Mahmood, I., Jahan, M., Groen, D., et al.
An Agent-Based Simulation of the Spread of Dengue Fever.
McDonald, G. W., & Osgood, N. D.
Agent-Based Modeling and Its Tradeoffs: An Introduction and Examples.
Oshinubi, K., Chen, Y., Doerry, E., et al.
A Systematic Review of Spatial Epidemiological Modeling Approaches Applied During the COVID-19 Pandemic.