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 ‘‘20:800 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.
