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1.
J Infect Dis ; 213(4): 502-8, 2016 Feb 15.
Artículo en Inglés | MEDLINE | ID: mdl-25995194

RESUMEN

BACKGROUND: Using a novel combination of whole-genome sequencing (WGS) analysis and geographic metadata, we traced the origins of Salmonella Bareilly isolates collected in 2012 during a widespread food-borne outbreak in the United States associated with scraped tuna imported from India. METHODS: Using next-generation sequencing, we sequenced the complete genome of 100 Salmonella Bareilly isolates obtained from patients who consumed contaminated product, from natural sources, and from unrelated historically and geographically disparate foods. Pathogen genomes were linked to geography by projecting the phylogeny on a virtual globe and produced a transmission network. RESULTS: Phylogenetic analysis of WGS data revealed a common origin for outbreak strains, indicating that patients in Maryland and New York were infected from sources originating at a facility in India. CONCLUSIONS: These data represent the first report fully integrating WGS analysis with geographic mapping and a novel use of transmission networks. Results showed that WGS vastly improves our ability to delimit the scope and source of bacterial food-borne contamination events. Furthermore, these findings reinforce the extraordinary utility that WGS brings to global outbreak investigation as a greatly enhanced approach to protecting the human food supply chain as well as public health in general.


Asunto(s)
Brotes de Enfermedades , Enfermedades Transmitidas por los Alimentos/epidemiología , Infecciones por Salmonella/epidemiología , Salmonella enterica/clasificación , Salmonella enterica/aislamiento & purificación , Animales , Enfermedades Transmitidas por los Alimentos/microbiología , Genoma Bacteriano , Genotipo , Humanos , India , Epidemiología Molecular , Tipificación Molecular , Filogeografía , Infecciones por Salmonella/microbiología , Salmonella enterica/genética , Análisis de Secuencia de ADN , Atún/microbiología , Estados Unidos/epidemiología
2.
Stud Health Technol Inform ; 216: 766-70, 2015.
Artículo en Inglés | MEDLINE | ID: mdl-26262155

RESUMEN

Traditionally, epidemiologists have counted cases and groups of symptoms. Modeling on these data consists of predicting expansion or contraction in the number of cases over time in epidemic curves or compartment models. Geography is considered a variable when these data are presented in choropleth maps. These approaches have significant drawbacks if the cases counted are not accurately diagnosed. For example, most regional public health authorities count influenza like illnesses (ILI). Cases of these diseases are designated as ILI if the patient exhibits fever, respiratory symptoms, and perhaps gastrointestinal symptoms. Several molecular epidemiological studies have shown that there are many pathogens that cause these symptoms and the relative proportions of these pathogens change over time and space. One way to bridge the gap between syndromic and genetic surveillance of infectious diseases is to compare signals of symptoms to pathogens recorded in molecular databases. We present a web-based workflow application that uses chief complaints found in the public Twitter feed as a syndromic surveillance tool and connects outbreak signals in these data to pathogens historically known to circulate in the same area. For the pathogen(s) of interest, we provide Genbank links to metadata and sequences in a workflow for phylogeographic analysis and visualization. The visualizations provide information on the geographic traffic of the spread of the pathogens and places that are hubs for their transport.


Asunto(s)
Enfermedades Transmisibles/epidemiología , Enfermedades Transmisibles/genética , Epidemiología Molecular/métodos , Filogeografía/métodos , Medios de Comunicación Sociales/estadística & datos numéricos , Flujo de Trabajo , Humanos , Procesamiento de Lenguaje Natural , Vigilancia de la Población/métodos , Prevalencia , Evaluación de Síntomas/métodos , Evaluación de Síntomas/estadística & datos numéricos
3.
Cladistics ; 31(6): 679-691, 2015 Dec.
Artículo en Inglés | MEDLINE | ID: mdl-34753271

RESUMEN

Viruses of influenza A subtype H7 can be highly pathogenic and periodically infect humans. For example, there have been numerous outbreaks of H7 in the Americas and Europe since 1996. More recently, a reassortant H7N9 has emerged among humans and birds during 2013-2014 in China, Taiwan and Hong Kong. This H7N9 genome consists of genetic segments that assort with H7 and H9 viruses previously circulating in chickens and wild birds in China and ducks in Korea. Epidemic risk modellers have used agricultural, climatic and demographic data to predict that the virus will spread to northern Vietnam via poultry. To shed light on the traffic of H7 viruses in general, we examine genetic segments of influenza that have assorted with many strains of H7 viruses dating back to 1902. We focus on use cases from the United States, Italy and China. We apply a novel metric, betweenness, an associated phylogenetic visualization technique, transmission networks, and compare these with another technique, route mapping. In contrast to traditional views, our results illustrate that segments that assort with H7 viruses are spread frequently between the Americas and Eurasia. In summary, genetic segments that historically assort with H7 influenza viruses have been spread from China to: Australia, Czech Republic, Denmark, Egypt, Germany, Hong Kong, Italy, Japan, Mongolia, the Netherlands, New Zealand, Pakistan, South Africa, South Korea, Spain, Sweden, the UK, the US, and Vietnam.

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