Connecting Network Properties of Rapidly Disseminating Epizoonotics

Abstract

BackgroundTo 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.MethodsUsing 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.ResultsThe 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.ConclusionsGeo-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.

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“…For example, many disease outbreaks will be dependent on the geography of a regions through vectors or other geographically determinants [ 42 – 44 ]. For H5N1, proximity to the highway network and epidemic nodes appeared to promote epidemic dispersal in Nigeria [ 20 , 45 ].…”

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“…Then the graph of Figure 3 where the links are roads connecting groups of H5N1 cases is not a contact network, rather it is a "connection" network. According to [1], the integration of bio geo temporal data and the connection network generates information usable to optimize control measures and to predict the outbreaks. In Figure 3 the actual sources are nodes 1, 2, 3, 4, 5, 6, 7 (that are considered more or less of the same age).…”

“…A useful tool in the analysis of epidemic spreading phenomena is the contact network, that represents individuals as vertices and contacts as edges connecting appropriate nodes. More generally, contact networks are understood as a set of heterogeneous relations among nodes and links, where nodes are individuals and links the relations [1,2]. Today the epidemic spreading paradigm is applied to the malware diffusion [3,4], the cascading effect in power grids [4], outbreaks of severe infective diseases [3] and rumor spreading in social networks [5,6,7].…”

“…The particular problem we are interested in is the identification of the nodes who started the spreading of a disease (or of an information) on a network. This information could be extremely useful to reduce a present or prevent a future outbreak, but from both the mathematical and the practical point of view has been regard as a difficult task [1,2,3,4].…”