Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/70132
Title: Influenza monitoring and simulation system
Authors: Loh, Russell Weibin
Keywords: DRNTU::Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence
Issue Date: 2017
Abstract: While Singapore encounter frequent seasons of Influenza in a yearly basis, Singapore lacks of an integrated system that is able to allow medical personnel or the government to effectively share and monitor the situation of Influenza. In this project, a system was developed to facilitate efficient recording of data and monitoring of influenza outbreak in Singapore. Simple data collection and displaying of information was developed using ASP.NET framework. The agent-based simulation model which consume large amount of computer resources due to large amount of memory and computation is developed and performed using Microsoft SQL Server, which allowed the management of the data of 5607300 agents to be efficient. The amount of time that were required to perform computation were significantly reduced as the use of advance techniques of databases such as set operations and indexes were leveraged, which allowed data to be accessed without the need to go through millions of loops or building special indexes, and hence more efficient. The developed system facilitate efficient recording by reducing actions that are more likely to cause human error through reducing typing of data and replacing it with selection of data. The system manipulate the data that is recorded to provide users with meaningful patterns and trends through the use of interactive map and charts. This empowers decision makers with information to contain or reduce the negative impact of influenza outbreaks. Furthermore, an agent-based simulation model that is based on the characteristics of Singapore was designed and developed. The agent-based model was benchmarked and compared against the Susceptible-Infectious-Recovered model that is used as the basis of many influenza pandemic models, which revealed that the agent-based model was able to more accurately simulate the impact of influenza pandemic in Singapore. While the system had met all the objectives that was set for this project, the accuracy of the simulator was limited due many dynamic factors, such as how human contact rate diminish over the period of influenza pandemic. It is hence recommended that future work that is similar to this project should identify other dynamic factors that influences influenza pandemic impact.
URI: http://hdl.handle.net/10356/70132
Rights: Nanyang Technological University
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Student Reports (FYP/IA/PA/PI)

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