Search

Background: African swine fever (ASF) is a highly contagious and devastating pig disease that has caused extensive global economic losses. Understanding ASF virus (ASFV) transmission dynamics within a herd is necessary in order to prepare for and respond to an outbreak in the United States. Although the transmission parameters for the highly virulent ASF strains have been estimated in several articles, there are relatively few studies focused on moderately virulent strains. Using an approximate Bayesian computation algorithm in conjunction with Monte Carlo simulation, we have estimated the adequate contact rate for moderately virulent ASFV strains and determined the statistical distributions for the durations of mild and severe clinical signs using individual, pig-level data. A discrete individual based disease transmission model was then used to estimate the time to detect ASF infection based on increased mild clinical signs, severe clinical signs, or daily mortality. Results: Our results indicate that it may take two weeks or longer to detect ASF in a finisher swine herd via mild clinical signs or increased mortality beyond levels expected in routine production. A key factor contributing to the extended time to detect ASF in a herd is the fairly long latently infected period for an individual pig (mean 4.5, 95% P.I., 2.4 - 7.2 days). Conclusion: These transmission model parameter estimates and estimated time to detection via clinical signs provide valuable information that can be used not only to support emergency preparedness but also to inform other simulation models of evaluating regional disease spread. [ABSTRACT FROM AUTHOR]

Copyright of Avian Diseases is the property of American Association of Avian Pathologists, Inc. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Avian Diseases is the property of American Association of Avian Pathologists, Inc. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Avian Diseases is the property of American Association of Avian Pathologists, Inc. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Understanding the amount of virus shed at the flock level by birds infected with low pathogenicity avian influenza virus (LPAIV) over time can help inform the type and timing of activities performed in response to a confirmed LPAIV-positive premises. To this end, we developed a mathematical model which allows us to estimate viral shedding by 10,000 turkey toms raised in commercial turkey production in the United States, and infected by H7 LPAIV strains. We simulated the amount of virus shed orally and from the cloaca over time, as well as the amount of virus in manure. In addition, we simulated the threshold cycle value (Ct) of pooled oropharyngeal swabs from birds in the infected flock tested by real-time reverse transcription polymerase chain reaction. The simulation model predicted that little to no shedding would occur once the highest threshold of seroconversion was reached. Substantial amounts of virus in manure (median 1.5 × 10 8 and 5.8 × 10 9 ; 50% egg infectious dose) were predicted at the peak. Lastly, the model results suggested that higher Ct values, indicating less viral shedding, are more likely to be observed later in the infection process as the flock approaches recovery. [ABSTRACT FROM AUTHOR]

The article discusses a study which investigated the taxonomic, geographic and temporal variation in avian influenza prevalence among shorebirds on the Pacific coast of the U.S. Real-time reverse transcriptase-polymerase chain reaction and virus isolation were used to test shorebirds for avian influenza virus. Virus was detected from Dunlin, Western Sandpiper, Long-billed Dowitcher and American Avocet. Diagnostic tests suggested poor functioning of traditional virus isolation methods. Since most diagnostic reagents and procedures are optimised for identifying viruses in poultry, the ability to identify the full range of influenza A subtypes is limited.

Abstract: Public gene sequence databases have become important research tools to understand viruses and other organisms. Evidence suggests that the identifying information for some of the sequences in these databases might not belong to the sequences they are associated with. We developed two tests to conduct a comprehensive analysis of all published sequences of the hemaglutinin and neuramidase genes of avian influenza viruses (AIVs) to identify sequences that may have been misclassified. One test identified sequence pairs with highly similar nucleotide sequences despite a difference of several years between their sampling dates. Another test, which was applied to samples sequenced and deposited more than once, detected sequences with more nucleotide differences to their own than to their closest relatives. All sequences identified as misclassified were further traced to relevant publications to assess the likelihood of contamination and determine if any conclusions were associated with the use of these sequences. Our results suggested that among 4040 published gene sequences examined, approximately 0.8% might be misclassified and that publications using these sequences may include inaccurate statements. Findings from this report suggest that using laboratory-adapted strains and handling multiple samples simultaneously increases the risk of contamination. The tests reported here may be useful for screening new submissions to public sequence databases. [Copyright &y& Elsevier]

Avian influenza viruses can pose serious risks to agricultural production, human health, and wildlife. An understanding of viruses in wild reservoir species across time and space is important to informing surveillance programs, risk models, and potential population impacts for vulnerable species. Although it is recognized that influenza A virus prevalence peaks in reservoir waterfowl in late summer through autumn, temporal and spatial variation across species has not been fully characterized. We combined two large influenza databases for North America and applied spatiotemporal models to explore patterns in prevalence throughout the annual cycle and across the continental United States for 30 waterfowl species. Peaks in prevalence in late summer through autumn were pronounced for dabbling ducks in the genera Anas and Spatula, but not Mareca. Spatially, areas of high prevalence appeared to be related to regional duck density, with highest predicted prevalence found across the upper Midwest during early fall, though further study is needed. We documented elevated prevalence in late winter and early spring, particularly in the Mississippi Alluvial Valley. Our results suggest that spatiotemporal variation in prevalence outside autumn staging areas may also represent a dynamic parameter to be considered in IAV ecology and associated risks., (© 2022. The Author(s).)

Background: Avian influenza (AI) is an infectious viral disease that affects several species and has zoonotic potential. Due to its associated health and economic repercussions, minimizing AI outbreaks is important. However, most control measures are generic and mostly target pathways important for the conventional poultry farms producing chickens, turkeys, and eggs and may not target other pathways that may be specific to the upland game bird sector. The goal of this study is to provide evidence to support the development of novel strategies for sector-specific AI control by comparing and contrasting practices and potential pathways for spread in upland game bird farms with those for conventional poultry farms in the United States. Farm practices and processes, seasonality of activities, geographic location and inter-farm distance were analyzed across the sectors. All the identified differences were framed and discussed in the context of their associated pathways for virus introduction into the farm and subsequent between-farm spread., Results: Differences stemming from production systems and seasonality, inter-farm distance and farm densities were evident and these could influence both fomite-mediated and local-area spread risks. Upland game bird farms operate under a single, independent owner rather than being contracted with or owned by a company with other farms as is the case with conventional poultry. The seasonal marketing of upland game birds, largely driven by hunting seasons, implies that movements are seasonal and customer-vendor dynamics vary between industry groups. Farm location analysis revealed that, on average, an upland game bird premises was 15.42 km away from the nearest neighboring premises with birds compared to 3.74 km for turkey premises. Compared to turkey premises, the average poultry farm density in a radius of 10 km of an upland game bird premises was less than a half, and turkey premises were 3.8 times (43.5% compared with 11.5%) more likely to fall within a control area during the 2015 Minnesota outbreak., Conclusions: We conclude that the existing differences in the seasonality of production, isolated geographic location and epidemiological seclusion of farms influence AI spread dynamics and therefore disease control measures should be informed by these and other factors to achieve success.

Premovement active surveillance for low pathogenicity avian influenza (LPAI) may be a useful risk management tool for producers during high-risk periods, such as during an LPAI outbreak, or in areas where there is a recognized high risk for LPAI spread. The effectiveness of three active-surveillance protocols in mitigating LPAI spread risk related to the movement of spent broiler breeders to processing was evaluated in this study. Each protocol differed in the amount of real-time reverse transcription polymerase chain reaction (RRT-PCR) and serology testing conducted. The protocols were evaluated with the use of disease transmission and active surveillance simulation models parametrized specifically for broiler breeders to estimate the probability of detecting a current or past infection and the mean proportion of infectious birds at the time of sampling in houses where the infection remains undetected at the time of movement after exposure to the virus. The two values were estimated considering flock infection for 1-28 days prior to the day of scheduled movement. A distribution for the adequate contact rate, a parameter that controls the rate of within-house spread in the disease transmission model, was estimated for this study by a novel forward simulation approach with the use of serology data from three LPAI-infected broiler breeder flocks in the United States. The estimated distribution suggests that the lower contact-rate estimates from previously published studies were not a good fit for the serology results observed in these U.S. flocks, though considerable uncertainty remains in the parameter estimate. The results for the probability of detection and mean proportion of infectious, undetected birds suggest that RRT-PCR testing is most beneficial during the early stages of infection postexposure, and serology testing is most beneficial during the later stages of infection, results that are expected to hold for flocks outside the United States as well. Thus, protocols that combine RRT-PCR and serology testing can offer a more balanced approach with good performance over the disease course in a flock.

Oropharyngeal and cloacal swabs were collected from poultry sold in two live bird market (LBM) systems to estimate the prevalence of low pathogenicity avian influenza virus (LPAIV) shedding during the summer and fall of 2005. Random sampling was conducted in three LBMs in Minnesota where 50 birds were sampled twice weekly for 4 wk, and in three LBMs in a California marketing system. A stratified systematic sampling method was used to collect samples from Southern California LBMs, where LPAIV was detected during routine surveillance. No LPAIV was detected in the LBM system in Minnesota where realtime reverse transcription-PCR (RT-PCR) was conducted on oropharyngeal samples. RT-PCR was performed on swabs taken from 290 of 14,000, 65 of 252, and 60 of 211 birds at the three Southern California LBMs. The number of samples collected was based on the number of birds, age of the birds, and number of species present in the LBM. Virus isolation, subtyping, and sequencing of the hemagglutinin, neuraminidase, and other internal protein genes was performed on AIV-positive samples. The estimated prevalence of LPAIV in California was 0.345% in an LBM/supply farm with multiple ages of Japanese quail, 3% in an LBM with multiple ages and strains of chickens present, and 49.8% in an LBM with multiple species, multiple strains, and multiple ages. The positive virus samples were all LPAIV H6N2 and closely related to viruses isolated from Southern California in 2001 and 2004. Little or no comingling of poultry may contribute to little or no LPAIV detection in the LBMs.