title: Connecting network properties of rapidly disseminating epizoonotics.
abstract: BACKGROUND
  To effectively control the geographical dissemination of infectious diseases, their properties need to be determined. To test that rapid microbial dispersal requires not only susceptible hosts but also a pre-existing, connecting network, we explored constructs meant to reveal the network properties associated with disease spread, which included the road structure.
  
  
  METHODS
  Using geo-temporal data collected from epizoonotics in which all hosts were susceptible (mammals infected by Foot-and-mouth disease virus, Uruguay, 2001; birds infected by Avian Influenza virus H5N1, Nigeria, 2006), two models were compared: 1) 'connectivity', a model that integrated bio-physical concepts (the agent's transmission cycle, road topology) into indicators designed to measure networks ('nodes' or infected sites with short- and long-range links), and 2) 'contacts', which focused on infected individuals but did not assess connectivity.
  
  
  RESULTS
  THE CONNECTIVITY MODEL SHOWED FIVE NETWORK PROPERTIES: 1) spatial aggregation of cases (disease clusters), 2) links among similar 'nodes' (assortativity), 3) simultaneous activation of similar nodes (synchronicity), 4) disease flows moving from highly to poorly connected nodes (directionality), and 5) a few nodes accounting for most cases (a "20:80" pattern). In both epizoonotics, 1) not all primary cases were connected but at least one primary case was connected, 2) highly connected, small areas (nodes) accounted for most cases, 3) several classes of nodes were distinguished, and 4) the contact model, which assumed all primary cases were identical, captured half the number of cases identified by the connectivity model. When assessed together, the synchronicity and directionality properties explained when and where an infectious disease spreads.
  
  
  CONCLUSIONS
  Geo-temporal constructs of Network Theory's nodes and links were retrospectively validated in rapidly disseminating infectious diseases. They distinguished classes of cases, nodes, and networks, generating information usable to revise theory and optimize control measures. Prospective studies that consider pre-outbreak predictors, such as connecting networks, are recommended.
authors: Center for Global Health, Health Sciences Center, University of New Mexico, Albuquerque, New Mexico, United States of America. alrivas@unm.edu
authors: Ariel L
authors: AL
authors: Rivas
authors: Folorunso O
authors: FO
authors: Fasina
authors: Almira L
authors: AL
authors: Hoogesteyn
authors: Steven N
authors: SN
authors: Konah
authors: José L
authors: JL
authors: Febles
authors: Douglas J
authors: DJ
authors: Perkins
authors: James M
authors: JM
authors: Hyman
authors: Jeanne M
authors: JM
authors: Fair
authors: James B
authors: JB
authors: Hittner
authors: Steven D
authors: SD
authors: Smith
mesh_terms: D000818:Animals; D004196:Disease Outbreaks; D016021:Epidemiologic Studies; D040362:Geographic Information Systems; D006801:Humans; D015047:Zoonoses / Q000453:epidemiology*