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Scan Statistics for Space-Time Cluster Detection
Stockholms universitet, Naturvetenskapliga fakulteten, Matematiska institutionen.ORCID-id: 0000-0002-0927-7183
2018 (engelsk)Licentiatavhandling, med artikler (Annet vitenskapelig)
Abstract [en]

Scan statistics are used by public health agencies to detect and localize disease outbreaks. This thesis provides an overview of scan statistics in the context of prospective disease surveillance and outbreak detection, presents a novel scan statistic to deal with the type of zero-abundant data that is often encountered in these settings, and—perhaps most importantly—implements this and other scan statistics in a freely available and open source R package. Additionally, Markov processes and time series methods are frequently used in many disease surveillance methods. The last part of this thesis presents some computationally efficient methods for density evaluation and simulation of irregularly sampled AR(1) processes, that may be useful when implementing surveillance methods based on these types of processes.

sted, utgiver, år, opplag, sider
Stockholm: Stockholm University, 2018.
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Identifikatorer
URN: urn:nbn:se:su:diva-160465OAI: oai:DiVA.org:su-160465DiVA, id: diva2:1252664
Presentation
2018-10-25, Sal 22, Kräftriket hus 5, Stockholm, 10:00 (engelsk)
Opponent
Veileder
Tilgjengelig fra: 2018-10-12 Laget: 2018-10-02 Sist oppdatert: 2018-10-12bibliografisk kontrollert

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