Investigating the Influence of Spatial and Temporal Granularities on Agent-Based Modeling

Authored by Shaowen Wang, Eric Shook

Date Published: 2015

DOI: 10.1111/gean.12080

Sponsors: No sponsors listed

Platforms: No platforms listed

Model Documentation: Other Narrative

Model Code URLs: Model code not found

Abstract

Epidemic agent-based models (ABMs) simulate individuals in artificial societies that are capable of movement, interaction, and transmitting disease among themselves. ABMs have been used to study the spread of disease at various spatial and temporal scales ranging from small communities to the world, over days, months, and years. The representations of space and time often vary between different epidemic ABMs and can be influenced by factors such as the size of a modeled population, computational requirements, population environments, and disease-related data. The influence that the representations of space and time have on epidemic ABMs is difficult to assess. Here we show that the finest representations of space and timetermed spatial and temporal granularities (STGs)in a parsimonious ABM affect speed, intensity, and spatial spread of a synthetic disease. Specifically, we found disease spread faster and more intensely as spatial granularity is coarsened, whereas disease spread slower and less intensely as temporal granularity is coarsened in a parsimonious ABM. Our study is the first to use the same epidemic ABM to examine the influence of STGs. Our results demonstrate that STGs influence ABM dynamics including early disease burnout and that an interrelationship exists between the coarsening of STGs and the speed and intensity at which disease spreads. Our parsimonious ABM is extended based on a structured community model and we found STGs also influence ABM dynamics in a more realistic context that includes hierarchical movement. Broadly, our study serves as a basis for further inquiry toward the influence of space-time representations on more realistic models that include multiscale mobility, routine movements (e.g., commuting), and heterogeneous population distributions.

Tags

Dynamics networks Disease transmission Epidemic Pandemic influenza United-states Infectious-diseases Conceptual-framework Mathematical-models Dependence