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MotivationThe potentially low precision associated with the geographic origin of sampled sequences represents an important limitation for spatially explicit (i.e. continuous) phylogeographic inference of fast-evolving pathogens such as RNA viruses. A substantial proportion of publicly available sequences is geo-referenced at broad spatial scale such as the administrative unit of origin, rather than more precise locations (e.g. geographic coordinates). Most frequently, such sequences are either discarded prior to continuous phylogeographic inference or arbitrarily assigned to the geographic coordinates of the centroid of their administrative area of origin for lack of a better alternative.ResultsWe here implement and describe a new approach that allows to incorporate heterogeneous prior sampling probabilities over a geographic area. External data, such as outbreak locations, are used to specify these prior sampling probabilities over a collection of sub-polygons. We apply this new method to the analysis of highly pathogenic avian influenza H5N1 clade data in the Mekong region. Our method allows to properly include, in continuous phylogeographic analyses, H5N1 sequences that are only associated with large administrative areas of origin and assign them with more accurate locations. Finally, we use continuous phylogeographic reconstructions to analyse the dispersal dynamics of different H5N1 clades and investigate the impact of environmental factors on lineage dispersal velocities.Availability and implementationOur new method allowing heterogeneous sampling priors for continuous phylogeographic inference is implemented in the open-source multi-platform software package BEAST 1.10.Supplementary informationSupplementary data are available at Bioinformatics online.

EU Member States should ensure that they implement adequate health surveillance schemes in all aquaculture farming areas, as appropriate for the type of production. This study presents the results of applying the FAO's Surveillance Evaluation Tool (SET) to assess the Spanish disease surveillance system for farmed fish species, which although applied previously in livestock production, is applied here to aquaculture for the first time. Overall, there were important score differences between trout and marine fish (seabass and seabream) surveillance, which were higher for trout in the following areas: Institutional (70.8% versus 50.0%), Laboratory (91.7% versus 47.2%), and Surveillance activities (75.3% versus 61.3%). For other categories, the values were lower and no significant differences were found. However, most surveillance efforts focused only on trout, for which there are EU and WOAH listed (notifiable) diseases. In contrast, for seabream and seabass, for which there are no listed diseases, it was considered that surveillance efforts should, nevertheless, be in place and should focus on the identification of abnormal mortalities and emerging diseases, for which there are as yet no standardized harmonised methodologies. [ABSTRACT FROM AUTHOR]

The third and fourth epidemic waves of avian influenza A(H7N9) human infections in China showed an apparent reduction in incidence compared to the spring 2013 and winter 2013-14 epidemic waves. [...]

This literature review provides an overview of use of environmental samples (ES) such as faeces, water, air, mud and swabs of surfaces in avian influenza (AI) surveillance programs, focussing on effectiveness, advantages and gaps in knowledge. ES have been used effectively for AI surveillance since the 1970s. Results from ES have enhanced understanding of the biology of AI viruses in wild birds and in markets, of links between human and avian influenza, provided early warning of viral incursions, allowed assessment of effectiveness of control and preventive measures, and assisted epidemiological studies in outbreaks, both avian and human. Variation exists in the methods and protocols used, and no internationally recognized guidelines exist on the use of ES and data management. Few studies have performed direct comparisons of ES versus live bird samples (LBS). Results reported so far demonstrate reliance on ES will not be sufficient to detect virus in all cases when it is present, especially when the prevalence of infection/contamination is low. Multiple sample types should be collected. In live bird markets, ES from processing/selling areas are more likely to test positive than samples from bird holding areas. When compared to LBS, ES is considered a cost‐effective, simple, rapid, flexible, convenient and acceptable way of achieving surveillance objectives. As a non‐invasive technique, it can minimize effects on animal welfare and trade in markets and reduce impacts on wild bird communities. Some limitations of environmental sampling methods have been identified, such as the loss of species‐specific or information on the source of virus, and taxonomic‐level analyses, unless additional methods are applied. Some studies employing ES have not provided detailed methods. In others, where ES and LBS are collected from the same site, positive results have not been assigned to specific sample types. These gaps should be remedied in future studies. [ABSTRACT FROM AUTHOR]

Combining epidemiological information, genetic characterization and geomapping in the analysis of influenza can contribute to a better understanding and description of influenza epidemiology and ecology, including possible virus reassortment events. Furthermore, integration of information such as agroecological farming system characteristics can provide new knowledge on risk factors of influenza emergence and spread. Integrating viral characteristics into an animal disease information system is therefore expected to provide a unique tool to trace-and-track particular virus strains; generate clade distributions and spatiotemporal clusters; screen for distribution of viruses with specific molecular markers; identify potential risk factors; and analyze or map viral characteristics related to vaccines used for control and/or prevention. For this purpose, a genetic module was developed within EMPRES-i (FAO’s global animal disease information system) linking epidemiological information from influenza events with virus characteristics and enabling combined analysis. An algorithm was developed to act as the interface between EMPRES-i disease event data and publicly available influenza virus sequences in OpenfluDB. This algorithm automatically computes potential links between outbreak event and sequences, which are subsequently manually validated by experts. Subsequently, other virus characteristics such as antiviral resistance can then be associated to outbreak data. To visualize such characteristics on a geographic map, shape files with virus characteristics to overlay on other EMPRES-i map layers (e.g. animal densities) can be generated. The genetic module allows export of associated epidemiological and sequence data for further analysis. FAO has made this tool available for scientists and policy makers. Contributions are expected from users to improve and validate the number of linked influenza events and isolate information as well as the quality of information. Possibilities to interconnect with other influenza sequence databases or to expand the genetic module to other viral diseases (e.g. foot and mouth disease) are being explored. [ABSTRACT FROM AUTHOR]

Middle East respiratory syndrome coronavirus (MERS-CoV) is an emerging zoonotic pathogen discovered in 2012. The purpose of this scoping review was to summarize the empirical evidence for MERS-CoV in animals in order to map knowledge gaps and to extract data for modelling disease transmission in dromedary camels. A review protocol was developed a priori, and a systematic search, data extraction and summary were conducted using the Arksey and O'Malley framework. Ninety-nine publications were identified for full review out of 1,368 unique records. Of these publications, 71 were articles in scientific journals. Ninety of the studies were observational and the remaining nine were experimental. We summarize characteristics of animal studies including study design, study population and outcomes of interest for future transmission modelling in the reservoir population. The majority of field studies reported measures of prevalence, while experimental studies provided estimates of transmission parameters that pertain to the natural course of disease., (© 2018 Blackwell Verlag GmbH.)

Combining epidemiological information, genetic characterization and geomapping in the analysis of influenza can contribute to a better understanding and description of influenza epidemiology and ecology, including possible virus reassortment events. Furthermore, integration of information such as agroecological farming system characteristics can provide new knowledge on risk factors of influenza emergence and spread. Integrating viral characteristics into an animal disease information system is therefore expected to provide a unique tool to trace-and-track particular virus strains; generate clade distributions and spatiotemporal clusters; screen for distribution of viruses with specific molecular markers; identify potential risk factors; and analyze or map viral characteristics related to vaccines used for control and/or prevention. For this purpose, a genetic module was developed within EMPRES-i (FAO's global animal disease information system) linking epidemiological information from influenza events with virus characteristics and enabling combined analysis. An algorithm was developed to act as the interface between EMPRES-i disease event data and publicly available influenza virus sequences in OpenfluDB. This algorithm automatically computes potential links between outbreak event and sequences, which are subsequently manually validated by experts. Subsequently, other virus characteristics such as antiviral resistance can then be associated to outbreak data. To visualize such characteristics on a geographic map, shape files with virus characteristics to overlay on other EMPRES-i map layers (e.g. animal densities) can be generated. The genetic module allows export of associated epidemiological and sequence data for further analysis. FAO has made this tool available for scientists and policy makers. Contributions are expected from users to improve and validate the number of linked influenza events and isolate information as well as the quality of information. Possibilities to interconnect with other influenza sequence databases or to expand the genetic module to other viral diseases (e.g. foot and mouth disease) are being explored. Database OpenfluDB URL: http://openflu.vital-it.ch Database EMPRES-i URL: http://EMPRES-i.fao.org/.