Search

Significance In 2016/2017, highly pathogenic avian influenza (HPAI) virus of the subtype H5 spilled over into wild birds and caused the largest known HPAI epidemic in Europe, affecting poultry and wild birds. During its spread, the virus frequently exchanged genetic material (reassortment) with cocirculating low-pathogenic avian influenza viruses. To determine where and when these reassortments occurred, we analyzed Eurasian avian influenza viruses and identified a large set of H5 HPAI reassortants. We found that new genetic material likely came from wild birds across their migratory range and from domestic ducks not only in China, but also in central Europe. This knowledge is important to understand how the virus could adapt to wild birds and become established in wild bird populations., Highly pathogenic avian influenza (HPAI) viruses of the H5 A/goose/Guangdong/1/96 lineage can cause severe disease in poultry and wild birds, and occasionally in humans. In recent years, H5 HPAI viruses of this lineage infecting poultry in Asia have spilled over into wild birds and spread via bird migration to countries in Europe, Africa, and North America. In 2016/2017, this spillover resulted in the largest HPAI epidemic on record in Europe and was associated with an unusually high frequency of reassortments between H5 HPAI viruses and cocirculating low-pathogenic avian influenza viruses. Here, we show that the seven main H5 reassortant viruses had various combinations of gene segments 1, 2, 3, 5, and 6. Using detailed time-resolved phylogenetic analysis, most of these gene segments likely originated from wild birds and at dates and locations that corresponded to their hosts’ migratory cycles. However, some gene segments in two reassortant viruses likely originated from domestic anseriforms, either in spring 2016 in east China or in autumn 2016 in central Europe. Our results demonstrate that, in addition to domestic anseriforms in Asia, both migratory wild birds and domestic anseriforms in Europe are relevant sources of gene segments for recent reassortant H5 HPAI viruses. The ease with which these H5 HPAI viruses reassort, in combination with repeated spillovers of H5 HPAI viruses into wild birds, increases the risk of emergence of a reassortant virus that persists in wild bird populations yet remains highly pathogenic for poultry.

Recent studies have clearly shown that bats are the reservoir hosts of a wide diversity of novel viruses with representatives from most of the known animal virus families. In many respects bats make ideal reservoir hosts for viruses: they are the only mammals that fly, thus assisting in virus dispersal; they roost in large numbers, thus aiding transmission cycles; some bats hibernate over winter, thus providing a mechanism for viruses to persist between seasons; and genetic factors may play a role in the ability of bats to host viruses without resulting in clinical disease. Within the broad diversity of viruses found in bats are some important neurological pathogens, including rabies and other lyssaviruses, and Hendra and Nipah viruses, two recently described viruses that have been placed in a new genus, Henipaviruses in the family Paramyxoviridae. In addition, bats can also act as alternative hosts for the flaviviruses Japanese encephalitis and St Louis encephalitis viruses, two important mosquito-borne encephalitogenic viruses, and bats can assist in the dispersal and over-wintering of these viruses. Bats are also the reservoir hosts of progenitors of SARS and MERS coronaviruses, although other animals act as spillover hosts. This chapter presents the physiological and ecological factors affecting the ability of bats to act as reservoirs of neurotropic viruses, and describes the major transmission cycles leading to human infection.

Previous introductions of highly pathogenic avian influenza virus (HPAIV) to the EU were most likely via migratory wild birds. A mathematical model has been developed which indicated that virus amplification and spread may take place when wild bird populations of sufficient size within EU become infected. Low pathogenic avian influenza virus (LPAIV) may reach similar maximum prevalence levels in wild bird populations to HPAIV but the risk of LPAIV infection of a poultry holding was estimated to be lower than that of HPAIV. Only few non‐wild bird pathways were identified having a non‐negligible risk of AI introduction. The transmission rate between animals within a flock is assessed to be higher for HPAIV than LPAIV. In very few cases, it could be proven that HPAI outbreaks were caused by intrinsic mutation of LPAIV to HPAIV but current knowledge does not allow a prediction as to if, and when this could occur. In gallinaceous poultry, passive surveillance through notification of suspicious clinical signs/mortality was identified as the most effective method for early detection of HPAI outbreaks. For effective surveillance in anseriform poultry, passive surveillance through notification of suspicious clinical signs/mortality needs to be accompanied by serological surveillance and/or a virological surveillance programme of birds found dead (bucket sampling). Serosurveillance is unfit for early warning of LPAI outbreaks at the individual holding level but could be effective in tracing clusters of LPAIV‐infected holdings. In wild birds, passive surveillance is an appropriate method for HPAIV surveillance if the HPAIV infections are associated with mortality whereas active wild bird surveillance has a very low efficiency for detecting HPAIV. Experts estimated and emphasised the effect of implementing specific biosecurity measures on reducing the probability of AIV entering into a poultry holding. Human diligence is pivotal to select, implement and maintain specific, effective biosecurity measures., This publication is linked to the following EFSA Journal article: http://onlinelibrary.wiley.com/doi/10.2903/j.efsa.2017.5018/full This publication is linked to the following EFSA Supporting Publications article: http://onlinelibrary.wiley.com/doi/10.2903/sp.efsa.2017.EN-1282/full, http://onlinelibrary.wiley.com/doi/10.2903/sp.efsa.2017.EN-1283/full, http://onlinelibrary.wiley.com/doi/10.2903/sp.efsa.2017.EN-1284/full, http://onlinelibrary.wiley.com/doi/10.2903/sp.efsa.2017.EN-1285/full, http://onlinelibrary.wiley.com/doi/10.2903/sp.efsa.2017.EN-1286/full, http://onlinelibrary.wiley.com/doi/10.2903/sp.efsa.2017.EN-1287/full

Reservoir hosts of novel pathogens are often identified or suspected as such on the basis of serological assay results, prior to the isolation of the pathogen itself. Serological assays might therefore be used outside of their original, validated scope in order to infer seroprevalences in reservoir host populations, until such time that specific diagnostic assays can be developed. This is particularly the case in wildlife disease research. The absence of positive and negative control samples and gold standard diagnostic assays presents challenges in determining an appropriate threshold, or 'cutoff', for the assay that enables differentiation between seronegative and seropositive individuals. Here, multiple methods were explored to determine an appropriate cutoff for a multiplexed microsphere assay that is used to detect henipavirus antibody binding in fruit bat plasma. These methods included calculating multiples of 'negative' control assay values, receiver operating characteristic curve analyses, and Bayesian mixture models to assess the distribution of assay outputs for classifying seropositive and seronegative individuals within different age classes. As for any diagnostic assay, the most appropriate cutoff determination method and value selected must be made according to the aims of the study. This study is presented as an example for others where reference samples, and assays that have been characterised previously, are absent.

One quarter of all mammal species are considered threatened with extinction (IUCN 2007). The rate of loss of biodiversity is accelerating because increasing pressure from an expanding human population is shrinking natural habitat and over-exploiting wild animal populations. Although processes such as habitat loss and over-harvesting are usually identified as the major drivers of extinction, recent evidence suggests that disease can also be a significant threat to endangered species (Lyles and Dobson 1993; Daszak and Cunningham 1999; Daszak et al. 2000b; de Castro and Bolker 2004; Choisy and Rohani 2006; Lips et al. 2006; Smith et al. 2006). Disease has already been documented as a cause of extinction of a land snail (Partula turgida) (Cunningham and Dazsak 1998), and several amphibian species (Schloegel et al. 2006; Skerrat et al. 2007). Diseases are also known to cause significant population declines, as illustrated by the impact of canine distemper virus in black-footed ferrets (Mustela nigripes) (Williams et al. 1988) and lions (Panthera leo) (Roelke-Parker et al. 1996), rabies virus in African wild dogs (Lycaon pictus) (Woodroffe and Ginsberg 1999), Ebola virus in apes (Leroy et al. 2004), squirrelpox virus in red squirrels (Sciurus vulgaris) (Rushton et al. 2006) and transmissible facial tumour disease in Tasmanian devils (Sarcophilus harrisii) (Pearse and Swift 2006).

A systematic review of the published literature was undertaken, to explore the ability of different types of model to help identify the relative importance of different drivers leading to the development of zoonoses hotspots. We estimated that out of 373 papers we included in our review, 108 papers touched upon the objective of 'Assessment of interventions and intervention policies', 75 addressed the objective of 'Analysis of economic aspects of disease outbreaks and interventions', 67 the objective of 'Prediction of future outbreaks', but only 37 broadly addressed the objective of 'Sensitivity analysis to identify criteria leading to enhanced risk'. Most models of zoonotic diseases are currently capturing outbreaks over relatively short time and largely ignoring socio-economic drivers leading to pathogen emergence, spill-over and spread. In order to study long-term changes we need to understand how socio-economic and climatic changes affect structure of livestock production and how these in turn affect disease emergence and spread. Models capable of describing this processes do not appear to exist, although some progress has been made in linking social and economical aspects of livestock production and in linking economics to disease dynamics. Henceforth we conclude that a new modelling framework is required that expands and formalises the 'one world, one health' strategy, enabling its deployment in the re-thinking of prevention and control strategies. Although modelling can only provide means to identify risks associated with socio-economic changes, it can never be a substitute for data collection. Finally, we note that uncertainty analysis and uncertainty communication form a key element of modelling process and yet are rarely addressed.

Bats, including African straw-coloured fruit bats (Eidolon helvum), have been highlighted as reservoirs of many recently emerged zoonotic viruses. This common, widespread and ecologically important species was the focus of longitudinal and continent-wide studies of the epidemiological and ecology of Lagos bat virus, henipaviruses and Achimota viruses. Here we present a spatial, morphological, demographic, genetic and serological dataset encompassing 2827 bats from nine countries over an 8-year period. Genetic data comprises cytochrome b mitochondrial sequences (n=608) and microsatellite genotypes from 18 loci (n=544). Tooth-cementum analyses (n=316) allowed derivation of rare age-specific serologic data for a lyssavirus, a henipavirus and two rubulaviruses. This dataset contributes a substantial volume of data on the ecology of E. helvum and its viruses and will be valuable for a wide range of studies, including viral transmission dynamic modelling in age-structured populations, investigation of seasonal reproductive asynchrony in wide-ranging species, ecological niche modelling, inference of island colonisation history, exploration of relationships between island and body size, and various spatial analyses of demographic, morphometric or serological data.