Automated Syndromic Surveillance using Intelligent Mobile Agents

Current syndromic surveillance systems utilize centralized databases that are neither scalable in storage space nor in computing power. Such systems are limited in the amount of syndromic data that may be collected and analyzed for the early detection of infectious disease outbreaks. However, with the increased prevalence of international travel, public health monitoring must extend beyond the borders of municipalities or states which will require the ability to store vasts amount of data and significant computing power for analyzing the data. Intelligent mobile agents may be used to create a distributed surveillance system that will utilize the hard drives and computer processing unit (CPU) power of the hosts on the agent network where the syndromic information is located. This thesis proposes the design of a mobile agent-based syndromic surveillance system and an agent decision model for outbreak detection. Simulation results indicate that mobile agents are capable of detecting an outbreak that occurs at all hosts the agent is monitoring. Further study of agent decision models is required to account for localized epidemics and variable agent movement rates.

Grantor dc:publisher

University of North Texas

Year dc:date

2007

Author dc:creator

• Miller, Paul

Contributors dc:contributor

• Mikler, Armin R.
• Jacob, Tom
• Goodman, Gary
• Dantu, Ram

• epidemiology
• automated
• agents
• health
• syndromic
• Public health surveillance -- Information resources management.
• Intelligent agents (Computer software)

Statement dc:rights

• Public
• Copyright
• Miller, Paul
• Copyright is held by the author, unless otherwise noted. All rights reserved.

Language dc:language

English

Identifier

oclc: 228503520
https://digital.library.unt.edu/ark:/67531/metadc5141/
ark: ark:/67531/metadc5141

OAI identifier oai:identifier

info:ark/67531/metadc5141