Risk factors for avian influenza virus contamination of live poultry markets in Zhejiang, China during the 2015-2016 human influenza season

Lagging effects and prediction of pollutants and their interaction modifiers on influenza in northeastern China

BACKGROUND

Previous studies have typically explored the daily lagged relations between influenza and meteorology, but few have explored seasonally the monthly lagged relationship, interaction and multiple prediction between influenza and pollution. Our specific objectives are to evaluate the lagged and interaction effects of pollution factors and construct models for estimating influenza incidence in a hierarchical manner.

METHODS

Our researchers collect influenza case data from 2005 to 2018 with meteorological and contaminative factors in Northeast China. We develop a generalized additive model with up to 6 months of maximum lag to analyze the impact of pollution factors on influenza cases and their interaction effects. We employ LASSO regression to identify the most significant environmental factors and conduct multiple complex regression analysis. In addition, quantile regression is taken to model the relation between influenza morbidity and specific percentiles (or quantiles) of meteorological factors.

RESULTS

The influenza epidemic in Northeast China has shown an upward trend year by year. The excessive incidence of influenza in Northeast China may be attributed to the suspected primary air pollutant, NO2, which has been observed to have overall low levels during January, March, and June. The Age 15-24 group shows an increase in the relative risk of influenza with an increase in PM2.5 concentration, with a lag of 0-6 months (ERR 1.08, 95% CI 0.10-2.07). In the quantitative analysis of the interaction model, PM10 at the level of 100-120 μg/m3, PM2.5 at the level of 60-80 μg/m3, and NO2 at the level of 60 μg/m3 or more have the greatest effect on the onset of influenza. The GPR model behaves better among prediction models.

CONCLUSIONS

Exposure to the air pollutant NO2 is associated with an increased risk of influenza with a cumulative lag effect. Prioritizing winter and spring pollution monitoring and influenza prediction modeling should be our focus.

Mixed selling of different poultry species facilitates emergence of public-health-threating avian influenza viruses

Live poultry markets (LPMs) are regarded as hubs for avian influenza virus (AIV) transmission in poultry and are a major risk factor in human AIV infections. We performed an AIV surveillance study at a wholesale LPM, where different poultry species were sold in separate stalls, and nine retail LPMs, which received poultry from the wholesale LPM but where different poultry species were sold in one stall, in Guangdong province from 2017 to 2019. A higher AIV isolation rate was observed at the retail LPMs than the wholesale LPM. H9N2 was the dominant AIV subtype and was mainly present in chickens and quails. The genetic diversity of H9N2 viruses was greater at the retail LPMs, where a complex system of two-way transmission between different poultry species had formed. The isolated H9N2 viruses could be classed into four genotypes: G57 and the three novel genotypes, NG164, NG165, and NG166. The H9N2 AIVs isolated from chickens and quails at the wholesale LPM only belonged to the G57 and NG164 genotypes, respectively. However, the G57, NG164, and NG165 genotypes were identified in both chickens and quails at the retail LPMs. We found that the replication and transmission of the NG165 genotype were more adaptive to both poultry and mammalian models than those of its precursor genotype, NG164. Our findings revealed that mixed poultry selling at retail LPMs has increased the genetic diversity of AIVs, which might facilitate the emergence of novel viruses that threaten public health.

Surveillance of the spread of avian influenza virus type A in live bird markets in Tripoli, Libya, and determination of the associated risk factors

Background and Aim: Studies on avian influenza virus (AIV) in Libya are few and limited. This study aimed to determine the presence of AIV in live bird markets (LBMs) in Tripoli and determine the risk factors associated with AIV spread. Materials and Methods: In total, 269 cloacal swabs were randomly collected from different bird species in 9 LBMs located in Tripoli and its surrounding regions. The target species were ducks, geese, local chickens, Australian chickens, Brahma chickens, turkeys, pigeons, quails, peacock broiler chicks, and pet birds. Total RNA was extracted from the swab samples and used for real-time polymerase chain reaction to detect AIV type A. Results: Of the 269 samples, 28 (10.41% of total samples) were positive for AIV type A. The LBMs with positive samples were Souq Aljumaa, Souq Alkhamees, Souq Althulatha, and Souq Tajoura. The highest percentage (35.71%) of AIV was recorded in Souq Aljumaa. Positive results for AIV type A were obtained primarily in three species of birds: Ducks (14/65; highest percentage: 21.5%), local chickens (12/98; 12.24%), and geese (2/28; 7.14%). Furthermore, the following three risk factors associated with the spread of AIV type A were identified: Time spent by breeders/vendors at the market (odds ratio [OR] = 11.181; 95% confidence interval [CI] = 3.827–32.669), methods used for disposing dead birds (OR = 2.356; 95% CI = 1.005–5.521), and last visited LBM (OR = 0.740; 95% CI = 0.580–0.944). Restricting the movement of poultry vendors from one market to another may protect against AIV spread. Conclusion: The findings of this study indicate the high risk of AIV spread in LBMs and highlight the need for continuous surveillance of LBMs across the country.

Risk Prediction of Three Different Subtypes of Highly Pathogenic Avian Influenza Outbreaks in Poultry Farms: Based on Spatial Characteristics of Infected Premises in South Korea

From 2003 to 2017, highly pathogenic avian influenza (HPAI) epidemics, particularly H5N1, H5N8, and H5N6 infections in poultry farms, increased in South Korea. More recently, these subtypes of HPAI virus resurged and spread nationwide, heavily impacting the entire poultry production and supply system. Most outbreaks in poultry holdings were concentrated in the southwestern part of the country, accounting for 58.3% of the total occurrences. This geographically persistent occurrence demanded the investigation of spatial risk factors related to the HPAI outbreak and the prediction of the risk of emerging HPAI outbreaks. Therefore, we investigated 12 spatial variables for the three subtypes of HPAI virus-infected premises [(IPs), 88 H5N1, 339 H5N8, and 335 H5N6 IPs]. Then, two prediction models using statistical and machine learning algorithm approaches were built from a case-control study on HPAI H5N8 epidemic, the most prolonged outbreak, in 339 IPs and 626 non-IPs. Finally, we predicted the risk of HPAI H5N1 and H5N6 occurrence at poultry farms using a Bayesian logistic regression and machine learning algorithm model [extreme gradient boosting (XGBoost) model] built on the case-control study. Several spatial variables showed similar distribution between two subtypes of IPs, although there were distinct heterogeneous distributions of spatial variables among the three IP subtypes. The case-control study indicated that the density of domestic duck farms and the minimum distance to live bird markets were leading risk factors for HPAI outbreaks. The two prediction models showed high predictive performance for H5N1 and H5N6 occurrences [an area under the curve (AUC) of receiver operating characteristic of Bayesian model > 0.82 and XGBoost model > 0.97]. This finding emphasizes that spatial characteristics of the poultry farm play a vital role in the occurrence and forecast of HPAI outbreaks. Therefore, this finding is expected to contributing to developing prevention and control strategies.

Estimation of Avian Influenza Viruses in Water Environments of Live Poultry Markets in Changsha, China, 2014 to 2018

In routine surveillance for avian influenza viruses (AIVs) in the environments of live poultry markets (LPMs), certain samples were positive for AIVs type A while negative for subtypes (e.g., H5, H7, and H9). However, little attention has been paid to these unsubtyped AIVs samples. To reveal the dynamic distribution and molecular characteristics of AIVs, especially the unsubtyped AIVs, we reported and analyzed 1969 samples collected from the water environments of LPMs in Changsha, China, from January 2014 to November 2018. Our results revealed that 1504 (76.38%) samples were positive for AIV type A. Of these samples, the predominant hemagglutinin (HA) subtype was H9, followed by H5 and H7 (P < 0.05). The positive rate of H5 subtype in water environmental samples exhibited seasonality, which reached a peak in each winter-spring season from January 2014 to March 2017. The positive rates of AIVs (including type A, subtype H9, and mixed subtype H5/H7/H9) in non-central-city regions were higher than that in the central-city regions (P < 0.05). Notably, 161 unsubtyped AIVs samples were detected during the routine surveillance. However, subtyping with the commercial kit further identified eight different HA and seven different neuraminidase subtypes. Analyses unraveled that further subtyped AIVs H1, H6, and H11 had only one basic amino acid (R or K) at the cleavage site and residues Q²²⁶ and G²²⁸ at the receptor-binding associated sites. Overall, in addition to H5, H7, and H9 subtypes, we should also pay attention to unsubtyped AIVs samples during the routine surveillance for AIVs in the environments of LPMs.

Wild bird trade at live poultry markets potentiates risks of avian influenza virus introductions in Iran

Wild aquatic birds are the main natural host reservoir of avian influenza viruses (AIV). Migratory aquatic birds can translocate AI viruses over wide geographic distances. AIV may be transmitted reciprocally at the wild bird–poultry interface, increasing viral variability and potentially driving the zoonotic potential of these viruses. A cross-sectional study on AIV and several further avian viral pathogens conducted in 396 trapped migratory aquatic birds traded at live bird markets (LBM) in northern Iran identified 11 AIV-positive cases. The 10 identified H9N2 viral sequences fell into wild bird H9 lineage Y439; in addition, an H10N3 virus of Eurasian lineage was detected. Ten samples contained low viral loads of avian coronavirus but could not be further characterized. Although traditional trading of live-trapped wild birds provides income for hunters, particularly during fall migration periods, it increases the risk of introducing new AIV strains from the natural reservoir to poultry kept at LBMs and, potentially, to traders and customers. Banning these birds from poultry trading lines would lower such risks considerably.

Pathogen change of avian influenza virus in the live poultry market before and after vaccination of poultry in southern China

BACKGROUND

The fifth wave of H7N9 avian influenza virus caused a large number of human infections and a large number of poultry deaths in China. Since September 2017, mainland China has begun to vaccinate poultry with H5 + H7 avian influenza vaccine. We investigated the avian influenza virus infections in different types of live poultry markets and samples before and after genotype H5 + H7 vaccination in Nanchang, and analyzed the changes of the HA subtypes of AIVs.

METHODS

From 2016 to 2019, we monitored different live poultry markets and collected specimens, using real-time reverse transcription polymerase chain reaction (RT-PCR) technology to detect the nucleic acid of type A avian influenza virus in the samples. The H5, H7 and H9 subtypes of influenza viruses were further classified for the positive results. The χ² test was used to compare the differences in the separation rates of different avian influenza subtypes.

RESULTS

We analyzed 5,196 samples collected before and after vaccination and found that the infection rate of AIV in wholesale market (21.73%) was lower than that in retail market (24.74%) (P < 0.05). Among all the samples, the positive rate of sewage samples (33.90%) was the highest (P < 0.001). After vaccination, the positive rate of H5 and H7 subtypes decreased, and the positive rate of H9 subtype and untypable HA type increased significantly (P < 0.001). The positive rates of H9 subtype in different types of LPMs and different types of samples increased significantly (P < 0.01), and the positive rates of untypable HA type increased significantly in all environmental samples (P < 0.05).

CONCLUSIONS

Since vaccination, the positive rates of H5 and H7 subtypes have decreased, but the positive rates of H9 subtypes have increased to varying degrees in different testing locations and all samples. This results show that the government should establish more complete measures to achieve long-term control of the avian influenza virus.

Risk Areas for Influenza A(H5) Environmental Contamination in Live Bird Markets, Dhaka, Bangladesh

We evaluated the presence of influenza A(H5) virus environmental contamination in live bird markets (LBMs) in Dhaka, Bangladesh. By using Bernoulli generalized linear models and multinomial logistic regression models, we quantified LBM-level factors associated with market work zone–specific influenza A(H5) virus contamination patterns. Results showed higher environmental contamination in LBMs that have wholesale and retail operations compared with retail-only markets (relative risk 0.69, 95% 0.51–0.93; p = 0.012) and in March compared with January (relative risk 2.07, 95% CI 1.44–2.96; p<0.001). Influenza A(H5) environmental contamination remains a public health problem in most LBMs in Dhaka, which underscores the need to implement enhanced biosecurity interventions in LBMs in Bangladesh.

Genetic Characterization of Highly Pathogenic Avian Influenza A(H5N8) Virus in Pakistani Live Bird Markets Reveals Rapid Diversification of Clade 2.3.4.4b Viruses

The highly pathogenic (HPAI) avian influenza A(H5N1) viruses have undergone reassortment with multiple non-N1-subtype neuraminidase genes since 2008, leading to the emergence of H5Nx viruses. H5Nx viruses established themselves quickly in birds and disseminated from China to Africa, the Middle East, Europe and North America. Multiple genetic clades have successively evolved through frequent mutations and reassortment, posing a continuous threat to domestic poultry and causing substantial economic losses. Live bird markets are recognized as major sources of avian-to-human infection and for the emergence of zoonotic influenza. In Pakistan, the A(H5N1) virus was first reported in domestic birds in 2007; however, avian influenza surveillance is limited and there is a lack of knowledge on the evolution and transmission of the A(H5) virus in the country. We collected oropharyngeal swabs from domestic poultry and environmental samples from six different live bird markets during 2018–2019. We detected and sequenced HPAI A(H5N8) viruses from two chickens, one quail and one environmental sample in two markets. Temporal phylogenetics indicated that all novel HPAI A(H5N8) viruses belonged to clade 2.3.4.4b, with all eight genes of Pakistan A(H5N8) viruses most closely related to 2017 Saudi Arabia A(H5N8) viruses, which were likely introduced via cross-border transmission from neighboring regions approximately three months prior to virus detection into domestic poultry. Our data further revealed that clade 2.3.4.4b viruses underwent rapid lineage expansion in 2017 and acquired significant amino acid mutations, including mutations associated with increased haemagglutinin affinity to human α-2,6 receptors, prior to the first human A(H5N8) infection in Russian poultry workers in 2020. These results highlight the need for systematic avian influenza surveillance in live bird markets in Pakistan to monitor for potential A(H5Nx) variants that may arise from poultry populations.

A framework for the risk prediction of avian influenza occurrence: An Indonesian case study

Avian influenza viruses can cause economically devastating diseases in poultry and have the potential for zoonotic transmission. To mitigate the consequences of avian influenza, disease prediction systems have become increasingly important. In this study, we have proposed a framework for the prediction of the occurrence and spread of avian influenza events in a geographical area. The application of the proposed framework was examined in an Indonesian case study. An extensive list of historical data sources containing disease predictors and target variables was used to build spatiotemporal and transactional datasets. To combine disparate sources, data rows were scaled to a temporal scale of 1-week and a spatial scale of 1-degree × 1-degree cells. Given the constructed datasets, underlying patterns in the form of rules explaining the risk of occurrence and spread of avian influenza were discovered. The created rules were combined and ordered based on their importance and then stored in a knowledge base. The results suggested that the proposed framework could act as a tool to gain a broad understanding of the drivers of avian influenza epidemics and may facilitate the prediction of future disease events.

A literature review of the use of environmental sampling in the surveillance of avian influenza viruses

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.

Genetic diversity of the H5N1 viruses in live bird markets, Indonesia

Background

The live bird market (LBM) plays an important role in the dynamic evolution of the avian influenza H5N1 virus.

Objectives

The main objective of this study was to monitor the genetic diversity of the H5N1 viruses in LBMs in Indonesia.

Methods

Therefore, the disease surveillance was conducted in the area of Banten, West Java, Central Java, East Java, and Jakarta Province, Indonesia from 2014 to 2019. Subsequently, the genetic characterization of the H5N1 viruses was performed by sequencing all 8 segments of the viral genome.

Results

As a result, the H5N1 viruses were detected in most of LBMs in both bird' cloacal and environmental samples, in which about 35% of all samples were positive for influenza A and, subsequently, about 52% of these samples were positive for H5 subtyping. Based on the genetic analyses of 14 viruses isolated from LBMs, genetic diversities of the H5N1 viruses were identified including clades 2.1.3 and 2.3.2 as typical predominant groups as well as reassortant viruses between these 2 clades.

Conclusions

As a consequence, zoonotic transmission to humans in the market could be occurred from the exposure of infected birds and/or contaminated environments. Moreover, new virus variants could emerge from the LBM environment. Therefore, improving pandemic preparedness raised great concerns related to the zoonotic aspect of new influenza variants because of its high adaptivity and efficiency for human infection.

Avian Influenza Virus Detection Rates in Poultry and Environment at Live Poultry Markets, Guangdong, China

We report the use of environmental samples to assess avian influenza virus activity in chickens at live poultry markets in China. Results of environmental and chicken samples correlate moderately well. However, collection of multiple environmental samples from holding, processing, and selling areas is recommended to detect viruses expected to have low prevalence.

Design of fluorinated sialic acid analog inhibitor to H5 hemagglutinin of H5N1 influenza virus through molecular dynamics simulation study

Influenza epidemics and pandemics are caused by influenza A virus. The cell surface protein of hemagglutinin and neuraminidase is responsible for viral infection and release of progeny virus on the host cell membrane. Now 18 hemagglutinin and 11 neuraminidase subtypes are identified. The avian influenza virus of H5N1 is an emergent threat to public health issues. To control the influenza viral infection it is necessary to develop antiviral inhibitors and vaccination. In the present investigation we carried out 50 ns Molecular Dynamics simulation on H5 hemagglutinin of Influenza A virus H5N1 complexed with fluorinated sialic acid by substituting fluorine atoms at any two hydroxyls of sialic acid by considering combinatorial combination. The binding affinity between the protein-ligand complex system is investigated by calculating pair interaction energy and MM-PBSA binding free energy. All the complex structures are stabilized by hydrogen bonding interactions between the H5 protein and the ligand fluorinated sialic acid. It is concluded from all the analyses that the fluorinated complexes enhance the inhibiting potency against H5 hemagglutinin and the order of inhibiting potency is SIA-F9 ≫ SIA-F2 ≈ SIA-F7 ≈ SIA-F2F4 ≈ SIA-F2F9 ≈ SIA-F7F9 > SIA-F7F8 ≈ SIA-F2F8 ≈ SIA-F8F9 > SIA-F4 ≈ SIA-F4F7 ≈ SIA-F4F8 ≈ SIA-F8 ≈ SIA-F2F7 ≈ SIA > SIA-F4F9. This study suggests that one can design the inhibitor by using the mono fluorinated models SIA-F9, SIA-F2 and SIA-F7 and difluorinated models SIA-F2F4, SIA-F2F9 and SIA-F7F9 to inhibit H5 of H5N1 to avoid Influenza A viral infection.Communicated by Ramaswamy H. Sarma.

Combating Global Antibiotic Resistance: Emerging One Health Concerns in Lower- and Middle-Income Countries

Antibiotic misuse in lower- and middle-income countries (LMICs) contributes to the development of antibiotic resistance that can disseminate globally. Strategies specific to LMICs that seek to reduce antibiotic misuse by humans, but simultaneously improve antibiotic access, have been proposed. However, most approaches to date have not considered the growing impact of animal and environmental reservoirs of antibiotic resistance, which threaten to exacerbate the antibiotic resistance crisis in LMICs. In particular, current strategies do not prioritize the impacts of increased antibiotic use for terrestrial food-animal and aquaculture production, inadequate food safety, and widespread environmental pollution. Here, we propose new approaches that address emerging, One Health challenges.

A review of knowledge discovery process in control and mitigation of avian influenza

In the last several decades, avian influenza virus has caused numerous outbreaks around the world. These outbreaks pose a significant threat to the poultry industry and also to public health. When an avian influenza (AI) outbreak occurs, it is critical to make informed decisions about the potential risks, impact, and control measures. To this end, many modeling approaches have been proposed to acquire knowledge from different sources of data and perspectives to enhance decision making. Although some of these approaches have shown to be effective, they do not follow the process of knowledge discovery in databases (KDD). KDD is an iterative process, consisting of five steps, that aims at extracting unknown and useful information from the data. The present review attempts to survey AI modeling methods in the context of KDD process. We first divide the modeling techniques used in AI into two main categories: data-intensive modeling and small-data modeling. We then investigate the existing gaps in the literature and suggest several potential directions and techniques for future studies. Overall, this review provides insights into the control of AI in terms of the risk of introduction and spread of the virus.

Prevalence of Avian Influenza A(H5) and A(H9) Viruses in Live Bird Markets, Bangladesh

We conducted a cross-sectional study in live bird markets (LBMs) in Dhaka and Chittagong, Bangladesh, to estimate the prevalence of avian influenza A(H5) and A(H9) viruses in different types of poultry and environmental areas by using Bayesian hierarchical logistic regression models. We detected these viruses in nearly all LBMs. Prevalence of A(H5) virus was higher in waterfowl than in chickens, whereas prevalence of A(H9) virus was higher in chickens than in waterfowl and, among chicken types, in industrial broilers than in cross-breeds and indigenous breeds. LBMs with >1 wholesaler were more frequently contaminated by A(H5) virus than retail-only LBMs. Prevalence of A(H9) virus in poultry and level of environmental contamination were also higher in LBMs with >1 wholesaler. We found a high level of circulation of both avian influenza viruses in surveyed LBMs. Prevalence was influenced by type of poultry, environmental site, and trading.

Transmission risk of avian influenza virus along poultry supply chains in Guangdong, China

OBJECTIVES

Avian influenza viruses (AIVs) poise significant risk to human health and the poultry industry. We evaluated the transmission risk along the poultry supply chain.

METHODS

During October 2015 and July 2016, four rounds of cross-sectional surveys were performed to characterize AIV spread in farms, transport vehicles, slaughterhouses, wholesale and retail live poultry markets (LPMs). Poultry cloacal and oral swabs, environmental swabs, bioaerosol samples and human sera were collected. Poultry and environmental samples were tested for AIVs by rRT-PCR, further subtyped by next generation sequencing. Previous human H9N2 infections were identified by hemagglutination inhibition and microneutralization tests. Logistic regression was fitted to compare AIV transmission risk in different settings.

RESULTS

AIVs was detected in 23.9% (424/1771) of the poultry and environmental samples. AIV detection rates in farms, transport vehicles, wholesale and retail LPMs were 4.5%, 11.1%, 30.3% and 51.2%, respectively. 5.2%, 8.3% and 12.8% of the poultry workers were seropositive in farms, wholesale and retail LPMs, respectively. The regression analysis showed that virus detection and transmission risk to human increased progressively along the poultry supply chain.

CONCLUSIONS

Strengthening control measures at every level along the poultry supply chain, using a one health approach, is crucial to control AIV circulation.

Comparison of Avian Influenza Virus Contamination in the Environment Before and After Massive Poultry H5/H7 Vaccination in Zhejiang Province, China

Background

Information regarding comparison of the environmental prevalence of avian influenza virus (AIVs), before and after massive poultry vaccinations, is limited. Our study aimed to detect differences in the prevalence of AIVs type A and subtypes H5, H7, and H9 before and after the September 2017 massive poultry vaccination, across different sampling places and types.

Methods

We collected 55 130 environmental samples from 11 cities in Zhejiang Province (China) between March 2013 and December 2018. Multivariate logistic regression analyses were conducted to determine the prevalence of AIV type A and subtypes H5, H7, and H9 across different sampling places and types, before and after massive poultry vaccination.

Results

After the vaccination, contamination risk of AIV type A (adjusted odds ratio [aOR] = 1.08; 95% confidence interval [CI], 1.03–1.14) and subtype H9 (aOR = 1.58; 95% CI, 1.48–1.68) increased, and that of subtype H7 (aOR = 0.12; 95% CI, 0.10–0.14) decreased. Statistically significant decreased risk for H7 subtype contamination and increased risk for H9 subtype contamination were observed in backyard poultry flocks, live poultry markets, and slaughtering/processing plants. Swabs from poultry cages and slaughtering tables showed a statistically significant increased risk for H5 subtype contamination. The prevalence of H7 subtype decreased statistically significantly, whereas that of H9 subtype increased across the 5 sample types (poultry cages swabs, slaughtering table swabs, poultry feces, poultry drinking water, and poultry sewage).

Conclusions

Despite the sharp decrease in H7 subtype prevalence, reduction measures for AIV circulation are still imperative, given the high type A prevalence and the increase in H9 subtype contamination across different sampling places and types.

Optimising the detectability of H5N1 and H5N6 highly pathogenic avian influenza viruses in Vietnamese live-bird markets

Live bird markets (LBMs) are major targets for avian influenza virus (AIV) surveillance programmes. While sampling the LBM environment has become a widely used alternative to the labour-intensive sampling of live poultry, the design of surveillance programmes and the interpretation of their results are compromised by the lack of knowledge about the effectiveness of these sampling strategies. We used latent class models and a unique empirical dataset collated in Vietnamese LBMs to estimate the sensitivity and specificity of five different sample types for detecting AIVs subtypes H5N1 and H5N6: oropharyngeal duck samples, solid and liquid wastes, poultry drinking water and faeces. Results suggest that the sensitivity of environmental samples for detecting H5N1 viruses is equivalent to that of oropharyngeal duck samples; however, taking oropharyngeal duck samples was estimated to be more effective in detecting H5N6 viruses than taking any of the four environmental samples. This study also stressed that the specificity of the current surveillance strategy in LBMs was not optimal leading to some false positive LBMs. Using simulations, we identified 42 sampling strategies more parsimonious than the current strategy and expected to be highly sensitive for both viruses at the LBM level. All of these strategies involved the collection of both environmental and oropharyngeal duck samples.

Risk factors for avian influenza virus in backyard poultry flocks and environments in Zhejiang Province, China: a cross-sectional study

BACKGROUND

Human infection of avian influenza virus (AIV) remains a great concern. Although live poultry markets are believed to be associated with human infections, ever more infections have been reported in rural areas with backyard poultry, especially in the fifth epidemic of H7N9. However, limited information is available on backyard poultry infection and surrounding environmental contamination.

METHODS

Two surveillance systems and a field survey were used to collect data and samples in Zhejiang Province. In total, 4538 samples were collected by surveillance systems and 3171 from the field survey between May 2015 and May 2017, while 352 backyard poultry owners were interviewed in May 2017 by questionnaire to investigate factors influencing the prevalence of avian influenza A virus and other AIV subtypes. RT-PCR was used to test the nucleic acids of viruses. ArcGIS 10.1 software was used to generate maps. Univariate and logistic regression analyses were conducted to identify risk factors for AIV infection.

RESULTS

Of the 428 poultry premises observed by the surveillance system, 53 (12.38%) were positive for influenza A virus. Of the 352 samples from poultry premises observed by field survey, 13 (3.39%) were positive for influenza A virus. The prevalence of AIV was unevenly distributed and the dominant subtype differed among cities. Eastern (Shaoxing and Ningbo) and southern (Wenzhou) cities exhibited a higher prevalence of AIV (16.33, 8.94, and 7.30% respectively). Contamination of AIV subtypes was most severe in January, especially in 2016 (23.26%, 70/301). The positive rate of subtype H5/H7/H9 was 2.53% (115/4538). Subtype H5 was the least prevalent, while subtypes H7 and H9 had similar positivity rates (1.50 and 1.32% respectively). Poultry flocks and environmental samples had a similar prevalence of AIV (4.46% vs 5.06%). The type of live birds was a risk factor and the sanitary condition of the setting was a protective factor against influenza A contamination.

CONCLUSIONS

AIV subtypes were prevalent in backyard poultry flocks and surrounding environments in Zhejiang Province. The types of live birds and sanitary conditions of the environment were associated with influenza A contamination. These findings shine a light on the characteristics of contamination of AIV subtypes and emphasize the importance of reducing AIV circulation in backyard poultry settings.

Risk factors associated with highly pathogenic avian influenza subtype H5N8 outbreaks on broiler duck farms in South Korea

Highly Pathogenic Avian Influenza (HPAI) subtype H5N8 outbreaks occurred in poultry farms in South Korea in 2014 resulting in significant damage to the poultry industry. Between 2014 and 2016, the pandemic disease caused significant economic loss and social disruption. To evaluate the risk factors for HPAI infection in broiler duck farms, we conducted a retrospective case-control study on broiler duck farms. Forty-three farms with confirmed laboratories on premises were selected as the case group, and 43 HPAI-negative farms were designated as the control group. Control farms were matched based on farm location and were within a 3-km radius from the case premises. Spatial and environmental factors were characterized by site visit and plotted through a geographic information system (GIS). Univariable and multivariable logistic regression models were developed to assess possible risk factors associated with HPAI broiler duck farm infection. Four final variables were identified as risk factors in a final multivariable logistic model: "Farms with ≥7 flocks" (odds ratio [OR] = 6.99, 95% confidence interval [CI] 1.34-37.04), "Farm owner with ≥15 years of raising poultry career" (OR = 7.91, 95% CI 1.69-37.14), "Presence of any poultry farms located within 500 m of the farm" (OR = 6.30, 95% CI 1.08-36.93) and "Not using a faecal removal service" (OR = 27.78, 95% CI 3.89-198.80). This highlights that the HPAI H5N8 outbreaks in South Korea were associated with farm owner education, number of flocks and facilities and farm biosecurity. Awareness of these factors may help to reduce the spread of HPAI H5N8 across broiler duck farms in Korea during epidemics. Greater understanding of the risk factors for H5N8 may improve farm vulnerability to HPAI and other subtypes and help to establish policies to prevent re-occurrence. These findings are relevant to global prevention recommendations and intervention protocols.

Risk Factors for Avian Influenza H9 Infection of Chickens in Live Bird Retail Stalls of Lahore District, Pakistan 2009-2010

This study was conducted to identify risk factors associated with AIV infections in live bird retail stalls (LBRS) in Lahore District, Pakistan. A cross-sectional survey of LBRS was conducted from December 2009-February 2010 using two-stage cluster sampling based on probability proportional to size. A total of 280 oropharyngeal swab sample pools were collected from 1400 birds in 8 clusters and tested by qRT-PCR for the matrix (M) gene of type A influenza virus and HA gene subtypes H9, H5 and H7. Thirty-four (34) samples were positive for the M gene, of which 28 were also positive for H9. No sample was found positive for H5 or H7. Data for 36 potential risk factors, collected by questionnaire, were analyzed by survey-weighted logistic regression and prevalence odds ratios (OR) for associated risk factors were calculated. A final multivariable model identified three risk factors for H9 infection in LRBS, namely obtaining birds from mixed sources (OR 2.28, CI_95%: 1.4–3.7), keeping birds outside cages (OR 3.10, CI_95%: 1.4–7.0) and keeping chicken breeds other than broilers (OR 6.27, CI_95%: 1.7–23.2). Sourcing birds from dealers/wholesalers, keeping birds inside cages and avoiding mixing different breeds in cages could reduce the risk of H9 infections in LRBS.

Development and application of a triplex real-time PCR assay for the simultaneous detection of avian influenza virus subtype H5, H7 and H9

Avian influenza virus (AIV), especially subtypes H5, H7 and H9, has contributed to enormous economic losses and poses a potential pandemic threat to global human public health. Early screening of suspected cases is key to controlling the spread of AIVs. In this study, an accurate, rapid, and triplex real-time polymerase chain reaction (PCR) assay was developed for the simultaneous detection of AIV subtypes H5, H7 and H9. The sensitivity of the real-time PCR was at least 100 times higher than that of the conventional PCR, with a detection limit of 50 copies and an EID₅₀ of 1 (50% egg infections dose) for the H5, H7, and H9 subtypes. The lack of cross-reaction with other avian respiratory viruses suggested that the real-time PCR assay was highly specific. The reproducibility of the assay was confirmed using plasmids containing targets genes. Furthermore, 362 clinical field samples were evaluated. Subtypes H5, H7 and H9 were detected in 102 (28.18%) samples by real-time PCR and in 35 (9.67%) samples by conventional virus isolation. These results indicate that the triplex real-time PCR assay has good sensitivity, specificity and reproducibility and that it might be useful for laboratory surveillance and rapid diagnosis of the H5, H7 and H9 subtypes of influenza A viruses.

Epidemiology of avian influenza A H7N9 virus in human beings across five epidemics in mainland China, 2013-17: an epidemiological study of laboratory-confirmed case series

BACKGROUND

The avian influenza A H7N9 virus has caused infections in human beings in China since 2013. A large epidemic in 2016-17 prompted concerns that the epidemiology of the virus might have changed, increasing the threat of a pandemic. We aimed to describe the epidemiological characteristics, clinical severity, and time-to-event distributions of patients infected with A H7N9 in the 2016-17 epidemic compared with previous epidemics.

METHODS

In this epidemiological study, we obtained information about all laboratory-confirmed human cases of A H7N9 virus infection reported in mainland China as of Feb 23, 2017, from an integrated electronic database managed by the China Center for Disease Control and Prevention (CDC) and provincial CDCs. Every identified human case of A H7N9 virus infection was required to be reported to China CDC within 24 h via a national surveillance system for notifiable infectious diseases. We described the epidemiological characteristics across epidemics, and estimated the risk of death, mechanical ventilation, and admission to the intensive care unit for patients admitted to hospital for routine clinical practice rather than for isolation purpose. We estimated the incubation periods, and time delays from illness onset to hospital admission, illness onset to initiation of antiviral treatment, and hospital admission to death or discharge using survival analysis techniques.

FINDINGS

Between Feb 19, 2013, and Feb 23, 2017, 1220 laboratory-confirmed human infections with A H7N9 virus were reported in mainland China, with 134 cases reported in the spring of 2013, 306 in 2013-14, 219 in 2014-15, 114 in 2015-16, and 447 in 2016-17. The 2016-17 A H7N9 epidemic began earlier, spread to more districts and counties in affected provinces, and had more confirmed cases than previous epidemics. The proportion of cases in middle-aged adults increased steadily from 41% (55 of 134) to 57% (254 of 447) from the first epidemic to the 2016-17 epidemic. Proportions of cases in semi-urban and rural residents in the 2015-16 and 2016-17 epidemics (63% [72 of 114] and 61% [274 of 447], respectively) were higher than those in the first three epidemics (39% [52 of 134], 55% [169 of 306], and 56% [122 of 219], respectively). The clinical severity of individuals admitted to hospital in the 2016-17 epidemic was similar to that in the previous epidemics.

INTERPRETATION

Age distribution and case sources have changed gradually across epidemics since 2013, while clinical severity has not changed substantially. Continued vigilance and sustained intensive control efforts are needed to minimise the risk of human infection with A H7N9 virus.

FUNDING

The National Science Fund for Distinguished Young Scholars.