Title
Modeling space in an agent-based model of malaria: comparison between non-spatial and spatial models
Abstract
In agent-based modeling (ABM), an explicit spatial representation may be required in some cases for certain aspects of the system to be modeled more realistically. In this paper, we describe modeling space in a previous agent-based model of malaria. In the new spatial model, all agents (mosquitoes and aquatic habitats) possess explicit spatial information. The habitat locations of mosquitoes are specified according to different spatial patterns, or landscapes. We use three types of landscapes: regular, random, and hybrid. In the spatial context, we describe the modeling aspects of mosquito agents' movement, the event of oviposition (the process of laying eggs), and compare results between the two models (non-spatial and spatial). Ensuring oviposition is modeled accurately, we show that both models are docked. For both models, we investigate the effect of relative sizes of the aquatic habitats. Using different landscapes, we show that vector abundance (VA) remain unchanged. We also show that with same combined carrying capacity, varying the density of habitats in a landscape does not affect the mean population significantly. Finally, we show that when the density of aquatic habitats is constant, the combined carrying capacity drives VA.
Year
Venue
Keywords
2011
SpringSim (ADS)
ensuring oviposition,combined carrying capacity,modeling aspect,different spatial pattern,aquatic habitat,spatial context,new spatial model,explicit spatial information,explicit spatial representation,agent-based modeling,verification validation,landscape
Field
DocType
ISBN
Econometrics,Spatial analysis,Population,Habitat,Agent-based model,Biological system,Spatial model,Computer science,Simulation,Carrying capacity,Spatial contextual awareness,Spatial ecology
Conference
1-930638-56-6
Citations 
PageRank 
References 
1
0.38
6
Authors
3
Name
Order
Citations
PageRank
S. M. Niaz Arifin11228.12
Gregory J. Davis2275.31
Ying Zhou3394.81