Connecting Network Properties of Rapidly Disseminating Epizoonotics

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.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.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.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

Changes in the hydrological characteristics of Chabihau coastal wetlands, Yucatan, Mexico, associated with hurricane Isidore impact

183-192This paper describes changes in the hydrologic behavior of Chabihau coastal lagoon, Yucatan, Mexico, associated with the impact of Hurricane Isidore (2002) and the construction of hydraulic infrastructure in coastal highways, through the spatiotemporal analysis of the water physicochemical variables, from the 1999 flood season to the 2005 dry season. The coastal wetlands were subdivided into three areas: San Crisanto swamp in the west, Chabihau lagoon in the center, and Santa Clara swamp in the east. After the hurricane impact and construction of bridges in the coastal dune, stronger tide´s ebb into the Chabihau lagoon was recorded, changing it from a hyperhaline system to an euryhaline one. On the other hand, changes to hyperhaline conditions were observed in Santa Clara swamp during dry and flood seasons. After the hurricane, negative redox values were recorded throughout the entire Chabihau wetlands, in addition to a reduction in dissolved oxygen and pH, during both dry and flood seasons. This situation determined dominance of reductive processes in the three areas, with low temporal variability. If the salinization process continues in the Santa Clara swamp, changes may occur in the structure and composition of the mangrove forest

Self-organization of nickel nanoparticles dispersed in acetone: From separate nanoparticles to three-dimensional superstructures

Sonochemical synthesis of monodisperse nickel nanoparticles (Ni-NPs) by reduction of Ni acetylacetonate in the presence of polyvinylpyrrolidone stabilizer is reported. The Ni-NPs size is readily controlled to 5 nanometer diameter with a standard deviation of less than 5%. The as-prepared Ni-NPs sample was dispersed in acetone, for 4 weeks. For structural analysis was not applied to a magnetic field or heat treatment as key methods to direct the assembly. The transition from separate Ni-NPs into self-organization of three dimensions (3D) superstructures was studied by electron microscopy. Experimental analysis suggests that the translation and rotation movement of the Ni-NPs are governed by magnetic frustration which promotes the formation of different geometric arrangements in two dimensions (2D). The formation of 3D superstructures is confirmed from scanning electron microscopy revealing a layered domain that consists of staking of several monolayers having multiple well-defined supercrystalline domains, enabling their use for optical, electronic and sensor applications

Self-organization of nickel nanoparticles dispersed in acetone: from separate nanoparticles to three-dimensional superstructures

Sonochemical synthesis of monodisperse nickel nanoparticles (Ni-NPs) by reduction of Ni acetylacetonate in the presence of polyvinylpyrrolidone stabilizer is reported. The Ni-NPs size is readily controlled to 5 nanometer diameter with a standard deviation of less than 5%. The as-prepared Ni-NPs sample was dispersed in acetone, for 4 weeks. For structural analysis was not applied to a magnetic field or heat treatment as key methods to direct the assembly. The transition from separate Ni-NPs into self-organization of three dimensions (3D) superstructures was studied by electron microscopy. Experimental analysis suggests that the translation and rotation movement of the Ni-NPs are governed by magnetic frustration which promotes the formation of different geometric arrangements in two dimensions (2D). The formation of 3D superstructures is confirmed from scanning electron microscopy revealing a layered domain that consists of staking of several monolayers having multiple well-defined supercrystalline domains, enabling their use for optical, electronic and sensor applications

Differentiation of epidemic cases, detection of network properties, and estimation of long-range connectivity in the AI epizoonotic.

<p>Low-scale data revealed that one primary AI <i>case</i> was located close to but outside the connecting structure defined by <i>epidemic nodes</i> (A). In contrast, at or after TC II, most cases were found within epidemic nodes (B). Two <i>clusters</i> of <i>cases</i> were observed (red polygons, B). Some <i>epidemic nodes</i> displayed a much higher proportion of cases than average nodes, e.g., two nodes (nodes # 1 and 2, red pentagon, B) accounted for 46 (or 71%) of all within-node cases. Four <i>road intersection areas</i>, out of 16 (or 25%) included 80% (52/65) of all within-node <i>cases</i> (C). To estimate long-range connectivity, all pairs of epidemic cases were connected with Euclidean lines, conforming a graph of N * (N –1)/2 lines, where N = epidemic case (an infected farm), or (113 * 112)/2 = 6328 <i>infective links</i> (D).</p

Detection of ‘along-road’ disease clusters and empirical determination of <i>epidemic nodes</i>.

<p>Maps show high-scale geographical data of the 2001 Uruguayan FMD (A) and the 2006 Nigerian AI H5N1 (B) epizoonotics. Low-scale data revealed that epidemic cases not only displayed spatial auto-correlation but also clustered along the road network (C, D).The radii of <i>epidemic nodes</i> (the smallest circles that included one or more highway intersections[s] and epidemic cases, at any viral transmission cycle [TC] except TC I) were 7.5 -km (FMD, E) and 31-km long (AI, F). In both epizoonotics, >57% of all cases occurred within epidemic nodes (A, B, E, F).</p

Relationships between pre- and post-outbreak variables in FMD.

<p>Because some TC I and TC II epidemic nodes overlapped, they were merged. Merging resulted in a total of 9 (one in TC I, 8 in TC II) node clusters (A). The hypothesis that the number of infective links crossing each node cluster preceded case occurrence was supported by the data: the correlation between <i>infective link density</i> (number of infective links crossing epidemic nodes, per sq km, observed at TC I and TC II) and within-node <i>case density</i> (cases reported by epidemic day 60, expressed on a per sq km basis) was positive and significant (<i>r</i> = .75, <i>P</i><0.02, B). <i>Early</i> variables (<i>infective links</i> observed in the first 10% of the epidemic progression [days 1–6] predicted <i>late</i> outcomes (within-node case density, observed in the last 90% of the epidemic [days 7–60]).</p

Differentiation of AI epidemic nodes based on AI infective links.

<p>After overlapping <i>epidemic nodes</i> were merged, they were distinguished according to the number of <i>infective links</i> that crossed their surfaces (A). The <i>density of infective links/node</i> was so high in nodes # 1–4 that the color used to identify each node’s circle is not observed: only the color of the crossing (overlaying) <i>infective links</i> is noticed in such nodes. The density of <i>infective links/epidemic node</i> (infective links/sq km) decayed by a factor greater than 5 between node #1 and the following set of nodes (nodes # 2 to 4), by a factor of ∼3 between nodes # 2–4 and the set that included nodes #5 and 6, and by a factor of ∼2 between nodes # 5 and 6 and the remaining nodes. A significant positive correlation was found between the <i>infective link density/sq km</i> and the <i>case density/sq km</i> (<i>r</i> = .98, <i>P</i><0.001, B). An enlarged view of one AI epidemic node (red box, A), is shown in C.</p

Comparison between connectivity and contact models–the AI epizoonotic.

<p>The AI dispersal process was similar to that of the FMD epidemic diffusion: after transmission cycle (TC) I, the connectivity model captured twice as many cases than the contact model (A, B). The length of road segments found within the area determined by the connectivity model was three times longer and less fragmented than the road structure captured by the contact model (C, D).</p

Three cost-benefit perspectives.

<p>The AI data allowed the generation of three sets of metrics, potentially applicable in cost-benefit analyses. 1) While the <i>spatial statistical</i> (SS) model identified <i>6 disease clusters</i> (the 6 <i>epidemic nodes</i>, of which two partially overlapped, which are seen, within the red pentagon, as 4 circles or ovals, of different colors), because the SS approach does not offer information on directionality, control measures should consider every <i>epidemic node</i>, i.e., the overall ‘cost’ of an intervention would be equal to the sum of the areas of the 6 original epidemic nodes included in the red pentagon. 2) If a <i>Network Theory</i> (NT) perspective were considered, only a <i>single cluster</i> would be observed (the area included within the red pentagon, which is defined by nodes and edges [road segments]). The NT model may generate several cost-benefit metrics. 3) A <i>bio-geo-temporal</i> analysis can integrate both SS advantages (a small area) and NT advantages (identification of the most influential node, based on analysis of network properties). The bio-geo-temporal model can generate the lowest ‘cost’ (smallest area to be intervened per each prevented case). Calculations are reported in the text.</p