Keywords
agent-based modelling, urban scale models, institutional level models
Start Date
1-7-2010 12:00 AM
Abstract
This work extends ongoing development of a framework for modelling the spread of contact-transmission infectious diseases within urban environments, where modelling is augmented with surveillance data and diseases of interest are primarily influenza-like illnesses (ILI). The data sources include socio-ecological elements, in which physical environments (community, neighbourhood) coupled with census data allow inferences relative to targeted populations. The framework is built upon Agent Based Modeling (ABM), with emphasis on urban scale modelling integrated with institutional models of hospital emergency departments. The scenarios presented here include modeling an outbreak of a pH1N1, surges at Hospital emergency departments, demographic data integration, and a preliminary framework for modeling “crowdinforming”. Challenges include the inherent complexity associated with modeling social dynamics with as much fidelity as one can afford, the requirements of using real data to the extent possible or available, and the intuition associated with computer based experiments and statistical inferencing. Dynamic social system modeling and simulation are conjectured to be computationally irreducible in the Wolfram sense of the principle; as such, the ABM approach is a viable methodology to aid in providing guidance into policy and decision support systems.
Modelling the Spread of Influenza-like Illnesses in an Urban Environment
This work extends ongoing development of a framework for modelling the spread of contact-transmission infectious diseases within urban environments, where modelling is augmented with surveillance data and diseases of interest are primarily influenza-like illnesses (ILI). The data sources include socio-ecological elements, in which physical environments (community, neighbourhood) coupled with census data allow inferences relative to targeted populations. The framework is built upon Agent Based Modeling (ABM), with emphasis on urban scale modelling integrated with institutional models of hospital emergency departments. The scenarios presented here include modeling an outbreak of a pH1N1, surges at Hospital emergency departments, demographic data integration, and a preliminary framework for modeling “crowdinforming”. Challenges include the inherent complexity associated with modeling social dynamics with as much fidelity as one can afford, the requirements of using real data to the extent possible or available, and the intuition associated with computer based experiments and statistical inferencing. Dynamic social system modeling and simulation are conjectured to be computationally irreducible in the Wolfram sense of the principle; as such, the ABM approach is a viable methodology to aid in providing guidance into policy and decision support systems.
