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SUMMARYOutbreaks of vesicular stomatitis, serotype New Jersey, during epidemics in the United States and northern Mexico, 1982–5, were examined by backward trajectories of winds to investigate spread and possible sources. The outbreaks selected for analysis did not involve introduction of disease by infected animals. The findings indicate that wind could have been responsible for carrying infection from northern Mexico to Arizona and New Mexico and thence to Colorado and Utah and on to Wyoming, Idaho and Montana. The results of these analyses are consistent with the findings from T1 RNAse fingerprinting of virus isolates from outbreaks during the epidemics. The arrival of the trajectories was associated with the passage of a front and rain or passage of a front alone or rain alone. At the time of the trajectories temperatures of 10 °C and higher were recorded at heights up to 2500–3500 m.Introduction by airborne particles would appear unlikely as it would have required a source of at least 105infectious units per minute per animal. Vesicular stomatitis virus had been isolated fromSimuliumandCulicoidesduring the epidemic with amounts of virus fromSimuliumsufficient to suggest biological transmission. The possibility ofSimuliuminfected with vesicular stomatitis virus being carried downwind to introduce disease is discussed in relation to the behaviour ofSimuliumand the pathogenesis of vesicular stomatitis in large animals.
SUMMARYOutbreaks of vesicular stomatitis, serotype New Jersey, during epidemics in the United States and northern Mexico, 1982–5, were examined by backward trajectories of winds to investigate spread and possible sources. The outbreaks selected for analysis did not involve introduction of disease by infected animals. The findings indicate that wind could have been responsible for carrying infection from northern Mexico to Arizona and New Mexico and thence to Colorado and Utah and on to Wyoming, Idaho and Montana. The results of these analyses are consistent with the findings from T1 RNAse fingerprinting of virus isolates from outbreaks during the epidemics. The arrival of the trajectories was associated with the passage of a front and rain or passage of a front alone or rain alone. At the time of the trajectories temperatures of 10 °C and higher were recorded at heights up to 2500–3500 m.Introduction by airborne particles would appear unlikely as it would have required a source of at least 105infectious units per minute per animal. Vesicular stomatitis virus had been isolated fromSimuliumandCulicoidesduring the epidemic with amounts of virus fromSimuliumsufficient to suggest biological transmission. The possibility ofSimuliuminfected with vesicular stomatitis virus being carried downwind to introduce disease is discussed in relation to the behaviour ofSimuliumand the pathogenesis of vesicular stomatitis in large animals.
Predicting the drivers of incursion and expansion of vector‐borne diseases as part of early‐warning strategies (EWS) is a major challenge for geographically extensive diseases where spread is mediated by spatial heterogeneity in climate and other environmental drivers. Geospatial data on these environmental drivers are increasingly available affording opportunities for application to a predictive disease ecology paradigm provided the data can be synthesized and harmonized with fine‐scale, highly resolved data on vector and host responses to their environment. Here, we apply a multi‐scale big data–model integration approach using human‐guided machine learning to objectively evaluate the importance of a large suite of spatially distributed environmental variables (>400) to develop EWS for vesicular stomatitis (VS), a common viral vector‐borne vesicular disease affecting livestock throughout the Americas. Two temporally and phylogenetically distinct events were used to develop disease occurrence–environment relationships in incursion (2004) and expansion years (2005), and then to test those relationships (2014, 2015) at two scales: (1) local and (2) landscape to regional. Our results show that VS occurrence at a local scale of individual landowners was related to conditions that can be monitored (rainfall, temperatures, streamflow) or modified (vegetation). On‐site green vegetation during the month of occurrence and higher rainfall four months prior combined with either cool daytime (expansion) or nighttime (incursion) temperatures one month prior were indicators of VS occurrence. Distance to running water (incursion) and host density based on neighboring ranches (expansion) with infected animals were also important in individual years. At landscape‐to‐regional scales, conditions that favor specific VSV biological vectors were indicated, either black flies in incursion years or biting midges in expansion years. Changes in viral genetic lineage were less important to patterns in VS occurrence than factors affecting the host–vector–environment interactions. In combination with our onset map based on latitude, elevation, and long‐term annual precipitation, this year‐ and scale‐specific information can be used to develop strategies to minimize effects of future VS events. This big data approach coupled with expert knowledge and machine learning can be applied to other emerging diseases for improvement in understanding, prediction, and management of vector‐borne diseases.
Vesicular stomatitis (VS) is an arthropod-borne viral disease that negatively impacts domestic livestock and wildlife hosts, and economically impacts both private animal owners and the commercial livestock industry. Previous phylogenetic studies, based on partial P gene sequences, suggested that outbreak cycles of the virus (VSV) exhibit a two-phase dynamic (i.e., incursion and expansion). A single viral lineage from endemic areas of Mexico introduced into the southern United States during an incursion year (2004), can overwinter, and then expand throughout the western United States during the subsequent spring and summer seasons (2005). Our objective was to build on this past research using full-length viral genomic sequences from Mexico and the United States from the same outbreak, and a large suite of geospatial data to identify the environmental factors that influence VSV evolution in the United States and potentially drive the incursion-expansion dynamics. Our phylogeographic analysis confirmed that a single VS New Jersey virus (VSNJV) lineage initiated the 2004 incursion year outbreak was subject to decreasing genetic divergence during the 2004-2006 outbreak cycle, and likely overwintered between the 2004-2006 outbreak seasons. However, rather than a simple geographic relationship, viral genetic sublineages or patristic groups identified as part of our study, were found to be associated with seasonally varying evaporative demand, soil moisture, and precipitation. Our results suggest a functional role for these environmental factors in shaping the evolution and ecology of VSNJV. We speculate a nexus to insect-vector switching and possible adaptation to local environmental conditions to help explain the observed incursion-expansion dynamic in the United States in the 2004-2006 outbreak. Our approach of linking the phylogeography of a virus with the ecology of insect vectors can be applied to other vector-borne diseases.
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