Simulating epidemics in rural areas and optimizing preplanned quarantine areas using a clustering heuristic

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Show simple item record Anderson, Joseph Edward 2009-05-18T20:39:02Z 2009-05-18T20:39:02Z 2009-05-18T20:39:02Z
dc.description.abstract With the present threat of bioterrorist attacks and new natural disease strains developing, efficient and effective countermeasures must be in place in case of an epidemic outbreak. The best strategy is to stop the attack or natural phenomenon before it happens, but governments and individual citizens must have measures in place to limit the spread of a biological threat or infectious disease if it is ever introduced into society. The objective of this research is to know, before an outbreak, the best quarantine areas. Quarantines force similar individuals together and can be mathematically modeled as clustering people into distinct groups. In order to effectively determine the clustering solution to use as a quarantine plan, this research developed a simulation core that is highly adaptable to different disease types and different contact networks. The input needed for the simulation core is the characteristics of the disease as well as the contact network of the area to be modeled. Clustering is a mathematical problem that groups entities based on their similarities while keeping dissimilar entities in separate groups. Clustering has been widely used by civilian and military researchers to provide quality solutions to numerous problems. This research builds a mathematical model to find clusters from a community’s contact network. These clusters are then the preplanned quarantine areas. To find quality clusters a Clustering Heuristic using Integer Programming (CHIP) is developed. CHIP is a large neighborhood, hill-climbing heuristic and some computational results verify that it quickly generates good clustering solutions. CHIP is an effective heuristic to group people into clusters to be used as quarantine areas prior to the development of a disease or biological attack. Through a small computational study, CHIP is shown to produce clustering solutions that are about 25% better than the commonly used K-means clustering heuristic. CHIP provides an effective tool to combat the spread of an infectious disease or a biological terroristic attack and serves as a potential deterrent to possible terrorist attacks due to the fact that it would limit their destructive power. CHIP leads to the next level of preparation that could save countless lives in the event of an epidemic. en
dc.language.iso en_US en
dc.publisher Kansas State University en
dc.subject Integer Programming en
dc.subject Clustering en
dc.subject Epidemics en
dc.title Simulating epidemics in rural areas and optimizing preplanned quarantine areas using a clustering heuristic en
dc.type Thesis en Master of Science en
dc.description.level Masters en
dc.description.department Department of Industrial & Manufacturing Systems Engineering en
dc.description.advisor Todd W. Easton en
dc.subject.umi Engineering, Industrial (0546) en
dc.subject.umi Health Sciences, Public Health (0573) en 2009 en May en

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