A network-patch methodology for adapting agent-based models for directly transmitted disease to mosquito-borne...

Area of Science:

• Epidemiology
• Mathematical Modeling
• Public Health

Background:

• Mosquito-borne diseases pose a significant and growing global health threat.
• Existing models face challenges in predicting disease spread across diverse populations and environments.
• Accurate prediction and mitigation require sophisticated modeling approaches.

Purpose of the Study:

• To develop a novel hybrid network-patch model for mosquito-borne pathogen transmission.
• To integrate individual movement patterns with vector ecology for enhanced disease modeling.
• To investigate the impact of behavioral heterogeneity on pathogen spread.

Main Methods:

• A hybrid agent-based model (ABM) was developed, combining individual-level human movement with patch-based mosquito population dynamics.
• Differential equations were used to represent mosquito populations within distinct habitat patches.
• The model was adapted for human agents to simulate pathogen transmission dynamics.

Main Results:

• The hybrid model demonstrated that heterogeneity in pathogen spread is crucial for accurate prediction.
• Models incorporating frequent visits to high-risk patches resulted in a larger final epidemic size compared to homogeneous models.
• Individual behavior and movement patterns significantly influence disease transmission outcomes.

Conclusions:

• The proposed hybrid network-patch model effectively quantifies the impact of heterogeneity in mosquito-borne pathogen spread.
• Understanding individual movement and behavior is vital for effective disease mitigation strategies.
• This modeling approach offers improved insights for public health interventions against vector-borne diseases.