A Knowledge Driven Agent-Based Semantic Model for Epidemic Surveillance

Authored by Madiha Sahar, Nadra Guizani, Saleh M Basalamah, Muhammad N Ayyaz, Maaz Ahmad, Tajammal Mustafa, A Ghafoor

Date Published: 2015

DOI: 10.1142/s1793351x15500087

Sponsors: United States National Science Foundation (NSF) US Defense Threat Reduction Agency

Platforms: Java

Model Documentation: Other Narrative Flow charts Pseudocode

Model Code URLs: Model code not found

Abstract

In this paper we propose a probabilistic approach to synthesize an agent-based heterogeneous semantic model depicting population interaction and analyzing the spatio-temporal dynamics of an airborne epidemic, such as influenza, in a metropolitan area. The methodology is generic in nature and can generate a baseline population for cities for which detailed population summary tables are not available. The joint probabilities of population demographics are estimated using the International Public Use Microsimulation Data (IPUMS) sample set. Agents are assigned various activities based on several characteristics. The agent-based model for the city of Lahore, Pakistan is synthesized and a rule based disease spread model of influenza is simulated. The simulation results are visualized to produce semantic analysis for the spatio-temporal dynamics of the epidemic. The results show that the proposed model can be used by officials and medical experts to simulate an outbreak.

Tags

transmission Populations Infectious-disease Influenza-virus