title: Maximum linkage space-time permutation scan statistics for disease outbreak detection.
abstract: BACKGROUND
  In disease surveillance, the prospective space-time permutation scan statistic is commonly used for the early detection of disease outbreaks. The scanning window that defines potential clusters of diseases is cylindrical in shape, which does not allow incorporating into the cluster shape potential factors that can contribute to the spread of the disease, such as information about roads, landscape, among others. Furthermore, the cylinder scanning window assumes that the spatial extent of the cluster does not change in time. Alternatively, a dynamic space-time cluster may indicate the potential spread of the disease through time. For instance, the cluster may decrease over time indicating that the spread of the disease is vanishing.
  
  
  METHODS
  This paper proposes two irregularly shaped space-time permutation scan statistics. The cluster geometry is dynamically created using a graph structure. The graph can be created to include nearest-neighbor structures, geographical adjacency information or any relevant prior information regarding the contagious behavior of the event under surveillance.
  
  
  RESULTS
  The new methods are illustrated using influenza cases in three New England states, and compared with the cylindrical version. A simulation study is provided to investigate some properties of the proposed arbitrary cluster detection techniques.
  
  
  CONCLUSIONS
  We have successfully developed two new space-time permutation scan statistics methods with irregular shapes and improved computational performance. The results demonstrate the potential of these methods to quickly detect disease outbreaks with irregular geometries. Future work aims at performing intensive simulation studies to evaluate the proposed methods using different scenarios, number of cases, and graph structures.
authors.affiliation: Department of Production Engineering, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil. macosta@ufmg.br.
authors.forename: Marcelo A
authors.initials: MA
authors.lastname: Costa
authors.forename: Martin
authors.initials: M
authors.lastname: Kulldorff
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D004196:Disease Outbreaks / Q000706:statistics & numerical data*; D005260:Female; D006801:Humans; D007251:Influenza, Human / Q000175:diagnosis / Q000453:epidemiology*; D008297:Male; D008875:Middle Aged; D009010:Monte Carlo Method; D009511:New England / Q000453:epidemiology; D011159:Population Surveillance / Q000379:methods*; D011446:Prospective Studies; D062211:Spatio-Temporal Analysis; D055815:Young Adult