title: Disease outbreak detection through clique covering on a weighted ICPC-coded graph.
abstract: Even after a decade of increased research into the problem of detecting disease outbreaks, we lack a system that can limit the number of patients affected by a potential epidemic by recognising its existence at an early stage. In this paper we suggest the use of a weighted graph representing symptoms with an exceptionally high prevalence. Cliques with high weighted edges in such a graph will represent groups of symptoms that occur together more often than usual. As a result each clique will represent the main symptoms of a disease with a high incidence rate. This will make it easier to diagnose the nature of an outbreak, to reach the affected patients at an early stage and to distinguish between outbreaks occurring simultaneously.
authors.affiliation: Department of Computer Science, University of Tromsø, Norway. klaske@cs.uit.no
authors.forename: Klaske
authors.initials: K
authors.lastname: van Vuurden
authors.forename: Gunnar
authors.initials: G
authors.lastname: Hartvigsen
authors.forename: Johan Gustav
authors.initials: JG
authors.lastname: Bellika
mesh_terms: D000465:Algorithms; D003196:Computer Graphics; D020000:Decision Support Systems, Clinical; D004196:Disease Outbreaks; D042241:Early Diagnosis; D005873:Giardiasis / Q000175:diagnosis / Q000453:epidemiology; D006801:Humans; D015994:Incidence; D009664:Norway; D011159:Population Surveillance; D040442:Public Health Informatics