Risk for highly pathogenic avian influenza H5N1 virus infection in chickens in small-scale commercial farms, in a high-risk area, Bangladesh, 2008

Village and farm-level risk factors for avian influenza infection on backyard chicken farms in Bangladesh

A cross-sectional study was conducted with 144 small-scale poultry farmers across 42 Bangladeshi villages to explore risk factors associated with avian influenza H5 and H9 seropositivity on backyard chicken farms. Using mixed-effects logistic regression with village as random effect, we identified crow abundance in garbage dumping places and presence of migratory wild birds within villages to be associated with higher odds of H5 and H9 seropositivity. At farm-level, garbage around poultry houses was also associated with higher odds of H5 and H9 seropositivity. In addition, specific trading practices (such as, purchase of chickens from live bird markets (LBM) and neighboring farms to raise them on their own farms, frequency of visits to LBM, purchase of poultry at LBM for consumption) and contact of backyard chickens with other animals (such as, feeding of different poultry species together, using pond water as drinking source for poultry, access of feral and wild animals to poultry houses) were associated with higher odds of H5 or H9 seropositivity. Resource-constrained small-scale poultry farmers should be able to address risk factors identified in this study without requiring large investments into poultry management, thereby reducing the likelihood of avian influenza virus transmission and ultimately occurrence of avian influenza outbreaks.

Farm-Level Risk Factors Associated With Avian Influenza A (H5) and A (H9) Flock-Level Seroprevalence on Commercial Broiler and Layer Chicken Farms in Bangladesh

A cross-sectional study was conducted to identify farm-level risk factors associated with avian influenza A H5 and H9 virus exposure on commercial chicken farms in Bangladesh. For broiler farms, both H5 and H9 seropositivity were associated with visits by workers from other commercial chicken farms [odds ratio (OR) for H5 = 15.1, 95% confidence interval (CI): 2.8-80.8; OR for H9 = 50.1, 95% CI: 4.5-552.7], H5 seropositivity was associated with access of backyard ducks (OR = 21.5, 95% CI: 2.3-201.1), and H9 seropositivity with a number of farm employees (OR = 9.4, 95% CI: 1.1-80.6). On layer farms, both H5 and H9 seropositivity were associated with presence of stray dogs (OR for H5 = 3.1, 95% CI: 1.1-9.1; OR for H9 = 4.0, 95% CI: 1.1-15.3), H5 seropositivity with hatcheries supplying chicks (OR = 0.0, 95% CI: 0.0-0.3), vehicles entering farms (OR = 5.8, 95% CI: 1.5-22.4), number of farm employees (OR = 5.8, 95% CI: 1.2-28.2), and burying of dead birds near farms (OR = 4.6, 95% CI: 1.2-17.3); H9 seropositivity with traders supplying feed (OR = 5.9, 95% CI: 1.0-33.9), visits conducted of other commercial poultry farms (OR = 4.7, 95% CI: 1.1-20.6), number of spent layers sold (OR = 24.0, 95% CI: 3.7-155.0), and frequency of replacing chicken droppings (OR = 28.3, 95% CI: 2.8-284.2). Policies addressing these risk factors will increase the effectiveness of prevention and control strategies reducing the risk of avian influenza on commercial chicken farms.

Machine learning-driven dynamic risk prediction for highly pathogenic avian influenza at poultry farms in Republic of Korea: Daily risk estimation for individual premises

Highly pathogenic avian influenza (HPAI) is a fatal zoonotic disease that damages the poultry industry and endangers human lives via exposure to the pathogen. A risk assessment model that precisely predicts high-risk groups and occurrence of HPAI infection is essential for effective biosecurity measures that minimize the socio-economic losses of massive outbreaks. However, the conventional risk prediction approaches have difficulty incorporating the broad range of factors associated with HPAI infections at poultry holdings. Therefore, it is difficult to accommodate the complexity of the dynamic transmission mechanisms and generate risk estimation on a real-time basis. We proposed a continuous risk prediction framework for HPAI occurrences that used machine learning algorithms (MLAs). This integrated environmental, on-farm biosecurity, meteorological, vehicle movement tracking, and HPAI wild bird surveillance data to improve accuracy and timeliness. This framework consisted of (i) the generation of 1788 predictors from six types of data and reconstructed them with an outcome variable into a data mart based on a temporal assumption (i.e. infected period and day-ahead forecasting); (ii) training of the predictors with the temporally rearranged outcome variable that corresponded to HPAI H5N6 infected state at each individual farm on daily basis during the 2016-2017 HPAI epidemic using three different MLAs [Random Forest, Gradient Boosting Machine (GBM), and eXtreme Gradient Boosting]; (iii) predicting the daily risk of HPAI infection during the 2017-2018 HPAI epidemic using the pre-trained MLA models for each farm across the country. The models predicted the high risk to 8-10 out of 19 infected premises during the infected period in advance. The GBM MLAs outperformed the 7-day forecasting of HPAI prediction at individual poultry holdings, with an area under the curve (AUC) of receiver operating characteristic of 0.88. Therefore, this approach enhances the flexibility and timing of interventions against HPAI outbreaks at poultry farms.

A Review of Avian Influenza A Virus Associations in Synanthropic Birds

Avian influenza A viruses (IAV) have received significant attention due to the threat they pose to human, livestock, and wildlife health. In this review, we focus on what is known about IAV dynamics in less common avian species that may play a role in trafficking IAVs to poultry operations. Specifically, we focus on synanthropic bird species. Synanthropic species, otherwise known as peridomestic, are species that are ecologically associated with humans and anthropogenically modified landscapes, such as agricultural and urban areas. Aquatic birds such as waterfowl and shorebirds are the species most commonly associated with avian IAVs, and are generally considered the reservoir or maintenance hosts in the natural ecology of these viruses. Waterfowl and shorebirds are occasionally associated with poultry facilities, but are uncommon or absent in many areas, especially large commercial operations. In these cases, spillover hosts that share resources with both maintenance hosts and target hosts such as poultry may play an important role in introducing wild bird viruses onto farms. Consequently, our focus here is on what is known about IAV dynamics in synanthropic hosts that are commonly found on both farms and in nearby habitats, such as fields, lakes, wetlands, or riparian areas occupied by waterfowl or shorebirds.

A Decade of Avian Influenza in Bangladesh: Where Are We Now?

Highly pathogenic avian influenza (HPAI) has been a public health threat in Bangladesh since the first reported outbreak in poultry in 2007. The country has undertaken numerous efforts to detect, track, and combat avian influenza viruses (AIVs). The predominant genotype of the H5N1 viruses is clade 2.3.2.1a. The persistent changing of clades of the circulating H5N1 strains suggests probable mutations that might have been occurring over time. Surveillance has provided evidence that the virus has persistently prevailed in all sectors and caused discontinuous infections. The presence of AIV in live bird markets has been detected persistently. Weak biosecurity in the poultry sector is linked with resource limitation, low risk perception, and short-term sporadic interventions. Controlling avian influenza necessitates a concerted multi-sector ‘One Health’ approach that includes the government and key stakeholders.

Avian influenza A (H5N1) outbreaks in different poultry farm types in Egypt: the effect of vaccination, closing status and farm size

Background

The Avian Influenza A (H5N1) virus is endemic in poultry in Egypt. The winter of 2014/2015 was particularly worrying as new clusters of HPAI A (H5N1) virus emerged, leading to an important number of AI A (H5N1) outbreaks in poultry farms and sporadic human cases. To date, few studies have investigated the distribution of HPAI A (H5N1) outbreaks in Egypt in relation to protective / risk factors at the farm level, a gap we intend to fill. The aim of the study was to analyse passive surveillance data that were based on observation of sudden and high mortality of poultry or drop in duck or chicken egg production, as a basis to better understand and discuss the risk of HPAI A (H5N1) presence at the farm level in large parts of the Nile Delta.

Results

The probability of HPAI A (H5N1) presence was associated with several characteristics of the farms. Vaccination status, absence of windows/openings in the farm and the number of birds per cycle of production were found to be protective factors, whereas the presence of a duck farm with significant mortality or drop in egg production in the village was found to be a risk factor.

Conclusions

Results demonstrate the key role of several prevention and biosecurity measures to reduce HPAI A (H5N1) virus circulation, which could promote better poultry farm biosecurity in Egypt.

Electronic supplementary material

The online version of this article (10.1186/s12917-018-1519-8) contains supplementary material, which is available to authorized users.

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.

Epidemiological Risk Factors for Animal Influenza A Viruses Overcoming Species Barriers

Drivers and risk factors for Influenza A virus transmission across species barriers are poorly understood, despite the ever present threat to human and animal health potentially on a pandemic scale. Here we review the published evidence for epidemiological risk factors associated with influenza viruses transmitting between animal species and from animals to humans. A total of 39 papers were found with evidence of epidemiological risk factors for influenza virus transmission from animals to humans; 18 of which had some statistical measure associated with the transmission of a virus. Circumstantial or observational evidence of risk factors for transmission between animal species was found in 21 papers, including proximity to infected animals, ingestion of infected material and potential association with a species known to carry influenza virus. Only three publications were found which presented a statistical measure of an epidemiological risk factor for the transmission of influenza between animal species. This review has identified a significant gap in knowledge regarding epidemiological risk factors for the transmission of influenza viruses between animal species.

The risk factors for avian influenza on poultry farms: a meta-analysis

BACKGROUND

Avian influenza is a severe threat both to humans and poultry, but so far, no systematic review on the identification and evaluation of the risk factors of avian influenza infection has been published. The objective of this meta-analysis is to provide evidence for decision-making and further research on AI prevention through identifying the risk factors associated with AI infection on poultry farms.

METHODS

The results from 15 selected studies on risk factors for AI infections on poultry farms were analyzed quantitatively by meta-analysis.

RESULTS

Open water source (OR=2.89), infections on nearby farms (OR=4.54), other livestock (OR=1.90) and disinfection of farm (OR=0.54) have significant association with AI infection on poultry farms. The subgroup analysis results indicate that there exist different risk factors for AI infections in different types of farms.

CONCLUSIONS

The main risk factors for AI infection in poultry farms are environmental conditions (open water source, infections on nearby farms), keeping other livestock on the same farm and no disinfection of the farm.

Intervention strategies to reduce the risk of zoonotic infection with avian influenza viruses: scientific basis, challenges and knowledge gaps

A range of measures has been recommended and used for the control and prevention of avian influenza. These measures are based on the assessment of local epidemiological situations, field observations and other scientific information. Other non-technical factors are (or in some cases should be) taken into account when developing and recommending control measures. The precise effects under field conditions of most individual interventions applied to control and prevent avian influenza have not been established or subjected to critical review, often because a number of measures are applied simultaneously without controls. In most cases, the combination of measures used results in control or elimination of the virus although there are some countries where this has not been the case. In others, especially those with low poultry density, it is not clear whether the link between the adoption of a set of measures and the subsequent control of the disease is causative. This article discusses the various measures recommended, with particular emphasis on stamping out and vaccination, examines how these measures assist in preventing zoonotic infections with avian influenza viruses and explores gaps in knowledge regarding their effectiveness.

The spread of highly pathogenic avian influenza (subtype H5N1) clades in Bangladesh, 2010 and 2011

Since the global spread of highly pathogenic avian influenza H5N1 during 2005-2006, control programs have been successfully implemented in most affected countries. HPAI H5N1 was first reported in Bangladesh in 2007, and since then 546 outbreaks have been reported to the OIE. The disease has apparently become endemic in Bangladesh. Spatio-temporal information on 177 outbreaks of HPAI H5N1 occurring between February 2010 and April 2011 in Bangladesh, and 37 of these outbreaks in which isolated H5N1 viruses were phylogenetically characterized to clade, were analyzed. Three clades were identified, 2.2 (21 cases), 2.3.4 (2 cases) and 2.3.2.1 (14 cases). Clade 2.2 was identified throughout the time period and was widely distributed in a southeast-northwest orientation. Clade 2.3.2.1 appeared later and was generally confined to central Bangladesh in a north-south orientation. Based on a direction test, clade 2.2 viruses spread in a southeast-to-northwest direction, whereas clade 2.3.2.1 spread west-to-east. The magnitude of spread of clade 2.3.2.1 was greater relative to clade 2.2 (angular concentration 0.2765 versus 0.1860). In both cases, the first outbreak(s) were identified as early outliers, but in addition, early outbreaks (one each) of clade 2.2 were also identified in central Bangladesh and in northwest Bangladesh, a considerable distance apart. The spread of highly pathogenic avian influenza H5N1 in Bangladesh is characterized by reported long-distance translocation events. This poses a challenge to disease control efforts. Increased enforcement of biosecurity and stronger control of movements between affected farms and susceptible farms, and better surveillance and reporting, is needed. Although the movement of poultry and equipment appears to be a more likely explanation for the patterns identified, the relative contribution of trade and the market chain versus wild birds in spreading the disease needs further investigation.

Genetic diversity and phylogenetic analysis of highly pathogenic avian influenza (HPAI) H5N1 viruses circulating in Bangladesh from 2007-2011

Highly pathogenic avian influenza (HPAI) H5N1 virus has been endemic in Bangladesh since its first isolation in February 2007. Phylogenetic analysis of the haemagglutinin (HA) gene of HPAI H5N1 viruses demonstrated that 25 Bangladeshi isolates including two human isolates from 2007-2011 along with some isolates from neighbouring Asian countries (India, Bhutan, Myanmar, Nepal, China and Vietnam) segregate into two distinct clades (2.2 and 2.3). There was clear evidence of introduction of clade 2.3.2 and 2.3.4 viruses in 2011 in addition to clade 2.2 viruses that had been in circulation in Bangladesh since 2007. The data clearly demonstrated the movement of H5N1 strains between Asian countries included in this study due to migration of wild birds and/or illegal movement of poultry across borders. Interestingly, the two human isolates were closely related to the clade 2.2 Bangladeshi chicken isolates indicating that they have originated from chickens. Furthermore, comparative amino acid sequence analysis revealed several substitutions (including 189R>K and 282I>V) in HA protein of some clade 2.2 Bangladeshi viruses including the human isolates, suggesting there was antigenic drift in clade 2.2.3 viruses that were circulating between 2008 and 2011. Overall, the data imply genetic diversity among circulating viruses and multiple introductions of H5N1 viruses with an increased risk of human infections in Bangladesh, and establishment of H5N1 virus in wild and domestic bird populations, which demands active surveillance.