Assessing the impact of outdoor farming, farm size, and farm density on highly pathogenic avian influenza epidemics: A modelling study in the Netherlands.

In most cases of HPAI (highly pathogenic avian influenza) outbreaks, stamping-out operations are initiated by officially designating the affected premise, which is subsequently followed by depopulation of infected flocks. The primary objective of this study was to develop an evaluation method that correlates the level of biosecurity and the risk of having an HPAI outbreak in domestic poultry farms. A total of eight farms were selected nationwide, including layer farms, broiler farms, and an animal welfare type farm. The biosecurity level of the chosen poultry farms was assessed based on a total scoring index of 183 divided into three categories, general management (51), quarantine management (106), and sanitation management (26). Conclusively, the five layer farms (JS, GE, CS, HS, OE), scored higher overall scores compared to the animal welfare farm (CH) and broiler farms (JG, LB). In terms of scoring, which adds up to a total of 183 points, most layer farms scored between 130 and 157, while the two broiler farms and the welfare farm scored 45, 75 and 70, respectively. Next, an independent HPAI risk assessment of the farms was carried out. Regarding the correlation between biosecurity levels and HPAI risks, in the farms that presented a higher overall score in terms of biosecurity and outweighed the risks of HPAI, they tended to earn more points in the quarantine management category. The results of this study suggest that a viable system for evaluating biosecurity levels can establish strong correlations with the risk of having HPAI.

Highly Pathogenic Avian Influenza (HPAI) virus H5N1 and Low Pathogenic Avian Influenza (LPAI) virus H9N2 are endemic in Bangladesh and pose a threat to both poultry and human health. For effective avian influenza (AI) prevention and control, good knowledge of the factors influencing the epidemiology of avian influenza virus (AIV) circulation is crucial, but no in-depth investigations have thus far been conducted on poultry farms in Bangladesh.The overall aim of this research was to improve the understanding of the extent of H5 and H9 virus circulation on backyard, and commercial broiler and commercial layer chicken farms in Bangladesh and to identify risk factors associated with the presence of H5 and H9 virus. Furthermore, the research aimed to investigate the perceptions of chicken farmers to implement HPAI prevention and control measures in Bangladesh.Two cross-sectional studies were conducted in the Chittagong and Cox’s Bazaar districts of Bangladesh: 1) between February and April 2016 involving 144 backyard chicken farms in 42 villages, and 2) between February and April 2017 involving 106 commercial broiler and 113 commercial layer chicken farms. Blood samples, oropharyngeal swabs and cloacal swabs were collected from 576 chickens and 204 in-contact ducks on backyard farms, and from 954 broilers and 904 layers on commercial chicken farms. Questionnaires were used to collect data on farm-level and village-level risk factors for H5 and H9 seroprevalence and on farmer’s perceptions towards implementation of HPAI prevention and control measures.Although all sampled birds tested negative for H5 by RT-PCR, H5 seropositive chickens were detected in all three farming systems. The highest H5 seroprevalence was observed in ducks raised with chickens on backyard farms, 14.2% (95% CI: 10.0-19.8), compared to in-contact backyard chickens, 4.2% (95% CI: 2.8-6.1). H5 seroprevalence was lower in unvaccinated broiler chickens, 1.5% (95% CI: 0.9-2.5), than in unvaccinated layer chickens, 7.8% (95% CI: 6.1-9.8). H9 viral infection was detected by RT-PCR in 0.5% (95% CI: 0.2-1.3) and 0.6% (95% CI: 0.3-1.5) of chickens raised in broiler and layer farms, respectively and in 0.2% (95% CI: 0.0-1.2) of chickens on backyard farms suggesting a similar level of exposure to H9 virus is all farming systems. Backyard chickens and ducks showed similar H9 seroprevalence, 16.0% (95% CI: 13.2-19.2) and 15.7% (95% CI: 11.3-21.4) respectively, while it was 5.8% (95% CI: 4.3-7.6) in layers and 1.5% (95% CI: 0.9-2.5) in broilers. Over the course of a production cycle, H5 and H9 seroprevalence increased with the age of backyard and layer chickens. Clustering of H5 seropositivity in ducks was identified, highlighting that multiple ducks within a flock were H5 seropositive. This was in contrast to backyard and broiler and layer chickens, where only individual birds within flocks developed H5 antibodies.Using multilevel mixed modelling, farm- and village-level risk factors for AIV exposure for backyard farms were identified. For example, garbage around poultry house or on the farms (a farm-level risk factor) (OR for H5: 9.1, 95% CI: 1.7-48.8; OR for H9: 28.6, 95% CI:3.4-239.8) and crow abundance around garbage dumping places within villages (a village-level risk factor) (OR for H5:3.4, 95% CI: 1.1-10.8; OR for H9:13.1, 95% CI: 2.3-76.8) increased the odds for H5 and H9 seropositivity on backyard farms. Binomial logistic regression was used to identify farm-level risk factors for AIV exposure on commercial farms. For example, visits by workers from other commercial chicken farms during the current production cycle (OR for H5: 15.1, 95% CI: 2.8-80.8; OR for H9: 50.1, 95% CI:4.5- 552.7) increased the odds for seropositivity on broiler farms, while access of stray dogs to the sampled farm (OR for H5: 3.1, 95% CI: 1.1-9.1; OR for H9: 4.0, 95% CI:1.1-15.3) increased the odds for seropositivity on layer farms.Structural Equation Modelling was used to explore direct and indirect effects of farmers’ perceptions to implement HPAI prevention and control actions on their farms. Results highlighted that farmers working in different chicken production systems follow different decision-making processes. Perceived barriers to implement prevention and control measures (e.g. wearing protective equipment when handling chickens) refrained both broiler (β=-0.41, p<0.001) and backyard farmers (β=-0.52, p<0.001) to adopt interventions. Meanwhile perceived benefits (e.g. maintaining high biosecurity to reduce the risk of birds becoming sick) strongly influenced commercial broiler (β=0.44, p<0.001) and layer farmers’ (β=0.68, p<0.001), but not backyard farmers’ decisions. Information provided on HPAI control through media, meetings or via information campaigns played an important role in farmers’ decision making across all production systems.Overall, this project provided a holistic picture of the factors influencing the epidemiology of AIV circulation across diverse chicken production systems in Bangladesh. The project described AIV infection patterns, risk factors of infection and farmers perceptions to implement HPAI prevention and control measures. Results from this research project have been used to inform policy makers to develop recommendations and improve current AI prevention and control policies in Bangladesh.

The objective of this study was to elicit experts’ opinions and gather estimates on the perceived probability of introduction and spread of avian influenza (AI) virus in the Australian broiler and layer industry. Using a modified Delphi method and a 4-step elicitation process, 11 experts were asked to give initial individual estimates for the various pathways and practices in the presented scenarios using a questionnaire. Following this, a workshop was conducted to present group averages of estimates and discussion was facilitated to obtain final individual estimates. For each question, estimates for all experts were combined using a discrete distribution, with weights allocated representing the level of expertise. Indirect contact with wild birds either via a contaminated water source or fomites was considered the most likely pathway of introduction of low pathogenic avian influenza (LPAI) on poultry farms. Presence of a water body near the poultry farm was considered a potential pathway for introduction only when the operation type was free range and the water body was within 500m distance from the shed. The probability that LPAI will mutate to highly pathogenic avian influenza (HPAI) was considered to be higher in layer farms. Shared personnel, equipment and aerosol dispersion were the most likely pathways of shed to shed spread of the virus. For LPAI and HPAI spread from farm to farm, shared pick-up trucks for broiler and shared egg trays and egg pallets for layer farms were considered the most likely pathways. Findings from this study provide an insight on most influential practices on the introduction and spread of AI virus among commercial poultry farms in Australia, as elicited from opinions of experts. These findings will be used to support parameterization of a modelling study assessing the risk of AI introduction and spread among commercial poultry farms in Australia.

Consequences and global risks of highly pathogenic avian influenza outbreaks in poultry in the United Kingdom

Because of their inherent advantages, Geographic Information System (GIS) and Remote Sensing (RS) are extremely useful for dealing with geographically referenced information. In the study of epidemics, most data are geographically referenced, which makes GIS and RS the perfect even necessary tools for processing, analysis, representation of epidemic data. Comprehensively considering the data requirements in the study of highly pathogenic avian influenza (HPAI) coupled with the quality of the existing remotely sensed data in terms of the resolution of space, time and spectra, the data sensed by MODIS are chosen and the relevant methods and procedures of data processing from RS and GIS for some environmental factors are proposed. Through using spatial analysis functions and Exploratory Spatial Data Analysis (ESDA) of GIS, some results of relationship between HPAI occurrences and these potential factors are presented. The role played by bird migration is also preliminarily illustrated with some operations such as visualization, overlapping etc. provided by GIS. Through the work of this paper, we conclude: Firstly, the migration of birds causes the spread of HPAI all over the country in 2004-2005. Secondly, the migration of birds is the reason why the spread of HPAI is perturbed. That is, for some classic communicable diseases, their spread exhibits obvious spatial diffusion process. However, the spread of HPAI breaks this general rule. We think leap diffusion and time lag are the probable reasons for this kind of phenomena. Potential distribution of HPAI viruses (corresponding to the distribution of flyways and putative risk sources) is not completely consistent with the occurrences of HPAI. For this phenomenon, we think, in addition to the flyways of birds, all kinds of geographical, climatic factors also have important effect on the occurrences of HPAI. Through the case study of HPAI, we can see that GIS and RS can play very important roles in the study of epidemics.

Bangladesh first experienced highly pathogenic avian influenza (HPAI) in early 2007 and the National Reference Laboratory for Avian Influenza (NRL-AI) at BLRI diagnosed and confirmed the presence of H5 sub-type virus on 15 March 2007. Until May 2009, NRL-AI observed 323 H5 and three H9 positive cases. Gene sequencing of 25 isolates of 2007 and 2008 demonstrated 99.1 to 100% identical genetic structure, indicating a single introduction of the virus in Bangladesh. It also clearly demonstrated that Bangladeshi isolates belong to the sub-clade 2.2 popularly known as Qinghai lineage or Euro-Asian-African lineage with highest similarities to those from Kuwait, Mongolia, Russia and Afghanistan. The close similarities between the HPAI isolates of these countries with Bangladeshi isolates suggest that migratory birds might be responsible for initial introduction of HPAI in Bangladesh as the country has no poultry trade link with these countries. Identifying possible risk factors in the spread of HPAI in the country was examined through descriptive as well as case control studies. It was revealed that, in most cases, mortality in backyard chicken flocks preceded an outbreak on commercial farms. Poor management and breaches in biosecurity practices appeared to have significant role in the spread of HPAI. The poultry industry in Bangladesh faced a terrific financial loss in 2007 and 2008 due to the incursion of avian influenza, which was estimated to be at Taka 3858 crore (38580 million). The prices of broiler declined by about 28% while the price of eggs decreased by 26.5%, as more than one third of consumers refrained from consumption of broiler meat and eggs. As a result of this market collapse, many farm owners were compelled to abandon poultry production because of loss of capital.

To the Editor: Since 2003, highly pathogenic avian influenza (HPAI) H5N1 has shaken the world. In 10 countries, 258 confirmed cases in humans and 154 deaths have been reported (1). The number of countries with confirmed HPAI in poultry and wild birds jumps to 54 (2). Almost all persons infected with H5N1 have had close contact with sick or dead poultry by having butchered them, plucked them, or played (children) with them (3). Because H5N1 can potentially mutate or reassort into a strain capable of efficient human-to-human transmission, rapid elimination of the H5N1 virus in poultry and other risk-reduction interventions are thought to be essential for preventing further spread of HPAI (4). As a result, thousands of workers around the world have culled millions of domestic poultry (5). Preemptive culling creates a major concern with regard to compensation. In Nigeria, for example, affected farmers have yet to be compensated >50 million Nigerian Naira (>US$ 0.4 million) because of the ministry’s cash flow problems (6). On the other hand, US poultry farmers who participate in a US Department of Agriculture (USDA) program to prevent the spread of disease would be fully compensated for loss of poultry and equipment if even a low-pathogenic strain of avian influenza were found in the United States (7). This rule not only strengthens US protection against avian influenza but also minimizes any negative effect on the US poultry trade. As discussed by the World Bank (8), the situations of these 2 countries raise several questions: Who should pay the compensation? For what should compensation be paid? Who should be compensated? With regard to the first question, each country’s government is an exclusive funding source. However, in Nigeria, the amount of compensation overwhelms the government’s capacity. Some countries, like Australia, may get additional funding from alternative sources such as private sectors, regional economic groups, or international funds (9). Because national resources are often scarce, most developing countries must rely on international donors for a great deal of the funding for compensation programs. The response to the second question, extent of compensation, varies. In Nigeria, farmers are partially compensated for loss of poultry; however, in the United States, farmers who are part of the USDA program are fully compensated for loss of poultry and equipment. Setting the amount of compensation is difficult and can affect the outcome of culling efforts. In Thailand, to take advantage of the program in which compensation was perceived as high, some farmers reportedly moved infected poultry into previously uninfected areas. In Vietnam, where compensation was perceived as low, culling compliance was poor (8). The last question, who should be compensated, seems straightforward for the United States, where only farmers who participate in the USDA program would be fully compensated. However, H5N1 does not affect only farmers who sign up for such a program. And not all poultry are raised in commercial operations, especially in developing countries. In Thailand, for example, >80% of infected poultry are reportedly raised in backyards (10). Reasonable assumptions are that those backyard farmers do not honestly report dying poultry or that they rush sick and dying poultry to market, causing the disease to spread. Additional questions revolve around potential compensation for those who are involved in the poultry industry but who do not own poultry (e.g., poultry processing plant operators and their staff). Because each country’s needs and circumstances differ, building a coherent plan for tackling HPAI is difficult. However, each stakeholder should consider compensation as part of an overall package of prevention, preparedness, and response strategies toward controlling and preventing the spread of HPAI. Because H5N1 does not respect international boundaries, donors worldwide should step forward to support the most affected and vulnerable developing countries.

Highly pathogenic avian influenza (HPAI) outbreaks are a threat to human health and cause extremely large financial losses to the poultry industry due to containment measures. Determining the most effective control measures, especially the culling radius, to minimize economic impacts yet contain the spread of HPAI is of great importance. This study examines the factors influencing the probability of a farm being infected with HPAI during the 2016–2017 HPAI outbreak in Korea. Using a spatial random effects logistic model, only a few factors commonly associated with a higher risk of HPAI infection were significant. Interestingly, most density-related factors, poultry and farm, were not significantly associated with a higher risk of HPAI infection. The effective culling radius was determined to be two ranges: 0.5–2.2 km and 2.7–3.0 km. This suggests that the spatial heterogeneity, due to local characteristics and/or the characteristics of the HPAI virus(es) involved, should be considered to determine the most effective culling radius in each region. These findings will help strengthen biosecurity control measures at the farm level and enable authorities to quickly respond to HPAI outbreaks with effective countermeasures to suppress the spread of HPAI.

• Data-driven modelling for the transmission of HPAI in a poultry farm. • China mandatory vaccination plan effectively controls HPAI spread in poultry. • High virus transmissibility increases flock losses despite vaccination. • Comprehensive post-vaccination measures needed for optimal HPAI control. • Regular monitoring, vaccine evaluation, and biosecurity upgrades recommended. The prevalence of highly pathogenic avian influenza (HPAI) has become substantial obstacles for the poultry industry and international trade with potential threat to human health. We developed a data-driven transmission model for quantitatively assessing the risk of HPAI spread in a henhouse under the China national mandatory vaccination plan for prevention and control of HPAI in the poultry industry. With data from farm investigations and literature reviews, we calibrated our model to estimate some key model parameters. We employed the Latin Hypercubic Sampling (LHS) technique for multiple sampling during simulations and subsequently conducted statistical analysis to enhance the accuracy and reliability of our results. We found that within a designated single henhouse, regardless of whether the initial virus level is 5 or 500 times the minimum viral load required to trigger highly fatal avian influenza, when the virus transmission capacity is low (the basic reproductive number R 0 ∈ ( 2 , 3 ) ), implementing mandatory vaccination measures according to the policy-stipulated timings will keep the average overall mortality rate of chickens with maternal antibodies within the range of 0.08 %-0.42 %. However, if the virus transmission capacity doubles, the loss of chickens will increase by 3–5 times, but it will still be significantly lower than the normal mortality rate (5 %). Therefore, to optimize post-vaccination management and control, we recommend monitoring flock antibodies regularly, evaluating vaccine effectiveness promptly, strengthening pathogen surveillance, and upgrading farm biosecurity level. These measures are essential for safeguarding flock immunity, facilitating early virus detection, and mitigating the risk of HPAI transmission.

Highly Pathogenic Avian Influenza (HPAI) has a devastating impact on the economy especially the poultry industry and it jeopardizes food security and public health. The disease which was first reported in Nigeria in 2006, re-occurred in 2015, 2016, 2018 and 2019. Inspite of the efforts the federal government has put into eradicating Avian Influenza in the country, the re-occurrence of the disease points to challenges of control efforts by stakeholders. Biosecurity challenges confronting poultry farmers and live bird market operators were implicated in new outbreaks and spread of HPAI. A cross-sectional study was carried out by administering open ended questionnaires to poultry farmers and live bird marketers in 12 States that shared boundaries with States affected by HPAI in 2019. Using the thematic style of qualitative analysis and MS Excel 2016, data and information with common denominators and pattern were collated and grouped. The One Health approach was adopted for this study. This was achieved by evaluating the knowledge of the stakeholders on the spread of Avian Influenza (AI), the biosecurity challenges they faced, their recommended solutions and new preventive or control measures they were willing to implement in order to achieve biosecurity against AI outbreak. The live bird marketers' responses showed their major challenge to be poor commitment to implementing already known biosecurity rules (22%), followed by unavailability of funds (13%) and sanitation problems. The poultry farmers struggled with lack of funds for maintenance (32%), and lack of compliance to biosecurity by farm attendants (24%). Both groups jointly recommended alleviating strategies such as the improvement of stakeholder education, supportive financing and the strengthening of animal health legislations. These new insights would benefit the formulation and implementing effective probiosecurity strategies for the control of avian influenza.&#x0D; Keywords: Avian influenza, biosecurity, one health, poultry farms, Nigeria

Ongoing economic losses by and exposure of humans to highly pathogenic avian influenza (HPAI) in poultry flocks across Asia and parts of Africa and Europe motivate also outbreak-free countries such as Switzerland to invest in preparedness planning. Country-specific population data on between-farm contacts are required to anticipate probable patterns of pathogen spread. Information is scarce; in particular on how strongly small, non-commercial poultry farms are involved in between-farm contacts. We aimed to identify between-farm contacts of interest for HPAI spread at both commercial and non-commercial farms in a non-outbreak situation: whether or not commercial and non-commercial farms were involved in poultry and person movements and shared resources by company integration. Focus was on poultry movements for the purpose of purchase, sale and poultry show visits, their spatial dimension, their frequencies and the farm types they connected. Of the total 49,437 recorded poultry farms in Switzerland, 95% had less than 500 birds. The farm number resulted in densities of up to 8 poultry farms per km2 and a median number of 47 neighbour farms within a 3 km radius around the farms. Person movements and shared resources were identified in 78% of the surveyed farms (93% among commercials, 67% among non-commercials). Poultry trading movements over extensive spatial ranges were stated at 65% (79% among commercials, 55% among non-commercials). Movement frequencies depended on farm specialization and were higher for commercial than for non-commercial farms except for poultry show visits. Estimates however for the entire population revealed 3.5 times higher chances of a poultry purchase, and 14.6 times higher chances of exhibiting birds at poultry shows occurring in a given time by a farm smaller than 500 birds (non-commercial farm) than by a larger (commercial) farm. These findings indicate that both commercial and non-commercial farms are involved in neighbourhood and remote between-farm contacts relevant to HPAI spread. It is necessary to include all poultry farms, irrespective of their size and purpose in both livestock registration and disease surveillance systems, as well as in transmission models for poultry and zoonotic diseases.

The 3 patients infected with W135 in our study did not receive bivalent meningococcal vaccines. W135 meningococcal disease appears to be an emerging problem that should be investigated epidemiologically. These patients highlight the need for further epidemiologic surveillance to monitor changes in the incidence of meningococcal disease caused by W135 and for improved public health disease control strategies in the future.

International audience

Aeroecological processes, especially powered flight of animals, can rapidly connect biological communities across the globe. This can have profound consequences for evolutionary diversification, energy and nutrient transfers, and the spread of infectious diseases. The latter is of particular consequence for human populations, since migratory birds are known to host diseases which have a history of transmission into domestic poultry or even jumping to human hosts. In this chapter, we present a scenario under which a highly pathogenic avian influenza (HPAI) strain enters North America from East Asia via post-molting waterfowl migration. We use an agent-based model (ABM) to simulate the movement and disease transmission among 106 generalized waterfowl agents originating from ten molting locations in eastern Siberia, with the HPAI seeded in only ~102 agents at one of these locations. Our ABM tracked the disease dynamics across a very large grid of sites as well as individual agents, allowing us to examine the spatiotemporal patterns of change in virulence of the HPAI infection as well as waterfowl host susceptibility to the disease. We concurrently simulated a 12-station disease monitoring network in the northwest USA and Canada in order to assess the potential efficacy of these sites to detect and confirm the arrival of HPAI. Our findings indicated that HPAI spread was initially facilitated but eventually subdued by the migration of host agents. Yet, during the 90-day simulation, selective pressures appeared to have distilled the HPAI strain to its most virulent form (i.e., through natural selection), which was counterbalanced by the host susceptibility being conversely reduced (i.e., through genetic predisposition and acquired immunity). The monitoring network demonstrated wide variation in the utility of sites; some were clearly better at providing early warnings of HPAI arrival, while sites further from the disease origin exposed the selective dynamics which slowed the spread of the disease albeit with the result of passing highly virulent strains into southern wintering locales (where human impacts are more likely). Though the ABM presented had generalized waterfowl migration and HPAI disease dynamics, this exercise demonstrates the power of such simulations to examine the extremely large and complex processes which comprise aeroecology. We offer insights into how such models could be further parameterized to represent HPAI transmission risks as well as how ABMs could be applied to other aeroecological questions pertaining to individual-based connectivity.

Highly pathogenic avian influenza (HPAI) H5N1, clade 2.3.4.4b is a global threat causing widespread outbreaks on poultry farms and high mortality among wild bird populations in Canada. Wild aquatic birds are generally recognised as key reservoir hosts for avian influenza viruses (AIV's). Furthermore, environmental weather variables have been hypothesised to play a critical role in the spread and persistence of HPAI in the external environment. During the 2022 outbreak of HPAI (H5N1) in Canada, many positive farm cases were identified via epidemiological investigations as independent introductions, likely a result of virus spillover from the external environment into the poultry farm. We investigated the association between recently occurring meteorological and wild bird occurrence factors and the likelihood of poultry farms in British Columbia testing positive for HPAI (H5N1) due to independent viral introductions. Using a retrospective case-crossover analysis, this study produced a multivariable conditional logistic regression model examining weekly-averaged meteorological factors (n = 49) and a series of univariable models to examine wild bird occurrence across case and control periods for independent introductions of HPAI onto poultry farms in British Columbia (n = 48). During case periods, we found significant associations between the following variables: our multivariable analysis found elevated levels of precipitation and decreasing maximum relative humidity increased odds of independent introduction events. Additionally, the increasing occurrence of dabbling duck species groups, snow geese (Anser caerulescens) and short-billed gulls (Larus brachyrhynchus) during case periods was associated with increased odds of independent introduction events. This research identifies key weather variables and increased occurrence of specific waterbird species groups in the weeks directly prior to case occurrence as predictive factors for the occurrence of independent introductions of HPAI (H5N1) onto British Columbia poultry farms. These findings help us better understand the weather and wild bird factors likely associated with the risk of spillover of HPAI to poultry farms in this region.