Modelling high pathogenic avian influenza outbreaks in the commercial poultry industry.

Highly pathogenic avian influenza (HPAI) outbreaks are devastating to poultry industries and pose a risk to human health. There is concern that demand for free-range poultry products could increase the number of HPAI outbreaks by increasing the potential for low pathogenic avian influenza (LPAI) introduction to commercial flocks. We formulate stochastic mathematical models to understand how poultry-housing (barn, free-range and caged) within the meat and layer sectors interacts with a continuous low-level risk of introduction from wild birds, heterogeneity in virus transmission rates and virus mutation probabilities, to affect the risk of HPAI emergence - at both the shed and industry scales. For H5 and H7 viruses, restricted mixing in caged systems, free-range outdoor access and, particularly, production cycle length significantly influence HPAI risk between sectors of the chicken production industry. Results demonstrate how delay between virus mutation and detection, ensuing from the short production cycle, large shed sizes and industry reporting requirements, could mean that HPAI emerges in meat-production sheds but is undetected with few birds affected. We also find that the Australian HPAI outbreak history appears to be better explained by low LPAI introduction rates and low mutation probabilities, rather than extremely rare introduction and relatively high mutation probabilities.

Correction: Assessing the probability of introduction and spread of avian influenza (AI) virus in commercial Australian poultry operations using an expert opinion elicitation.

[This corrects the article DOI: 10.1371/journal.pone.0193730.].

Low Pathogenic Avian Influenza Exposure Risk Assessment in Australian Commercial Chicken Farms.

This study investigated the pathways of exposure to low pathogenic avian influenza (LPAI) virus among Australian commercial chicken farms and estimated the likelihood of this exposure occurring using scenario trees and a stochastic modeling approach following the World Organization for Animal Health methodology for risk assessment. Input values for the models were sourced from scientific literature and an on-farm survey conducted during 2015 and 2016 among Australian commercial chicken farms located in New South Wales and Queensland. Outputs from the models revealed that the probability of a first LPAI virus exposure to a chicken in an Australian commercial chicken farms from one wild bird at any point in time is extremely low. A comparative assessment revealed that across the five farm types (non-free-range meat chicken, free-range meat chicken, cage layer, barn layer, and free range layer farms), free-range layer farms had the highest probability of exposure (7.5 × 10-4; 5% and 95%, 5.7 × 10-4-0.001). The results indicate that the presence of a large number of wild birds on farm is required for exposure to occur across all farm types. The median probability of direct exposure was highest in free-range farm types (5.6 × 10-4 and 1.6 × 10-4 for free-range layer and free-range meat chicken farms, respectively) and indirect exposure was highest in non-free-range farm types (2.7 × 10-4, 2.0 × 10-4, and 1.9 × 10-4 for non-free-range meat chicken, cage layer, and barn layer farms, respectively). The probability of exposure was found to be lowest in summer for all farm types. Sensitivity analysis revealed that the proportion of waterfowl among wild birds on the farm, the presence of waterfowl in the range and feed storage areas, and the prevalence of LPAI in wild birds are the most influential parameters for the probability of Australian commercial chicken farms being exposed to avian influenza (AI) virus. These results highlight the importance of ensuring good biosecurity on farms to minimize the risk of exposure to AI virus and the importance of continuous surveillance of LPAI prevalence including subtypes in wild bird populations.

Low- and High-Pathogenic Avian Influenza H5 and H7 Spread Risk Assessment Within and Between Australian Commercial Chicken Farms.

This study quantified and compared the probability of avian influenza (AI) spread within and between Australian commercial chicken farms via specified spread pathways using scenario tree mathematical modeling. Input values for the models were sourced from scientific literature, expert opinion, and a farm survey conducted during 2015 and 2016 on Australian commercial chicken farms located in New South Wales (NSW) and Queensland. Outputs from the models indicate that the probability of no establishment of infection in a shed is the most likely end-point after exposure and infection of low-pathogenic avian influenza (LPAI) in one chicken for all farm types (non-free range meat chicken, free range meat chicken, cage layer, barn layer, and free range layer farms). If LPAI infection is established in a shed, LPAI is more likely to spread to other sheds and beyond the index farm due to a relatively low probability of detection and reporting during LPAI infection compared to high-pathogenic avian influenza (HPAI) infection. Among farm types, the median probability for HPAI spread between sheds and between farms is higher for layer farms (0.0019, 0.0016, and 0.0031 for cage, barn, and free range layer, respectively) than meat chicken farms (0.00025 and 0.00043 for barn and free range meat chicken, respectively) due to a higher probability of mutation in layer birds, which relates to their longer production cycle. The pathway of LPAI spread between sheds with the highest average median probability was spread via equipment (0.015; 5-95%, 0.0058-0.036) and for HPAI spread between farms, the pathway with the highest average median probability was spread via egg trays (3.70 × 10-5; 5-95%, 1.47 × 10-6-0.00034). As the spread model did not explicitly consider volume and frequency of the spread pathways, these results provide a comparison of spread probabilities per pathway. These findings highlight the importance of performing biosecurity practices to limit spread of the AI virus. The models can be updated as new information on the mechanisms of the AI virus and on the volume and frequency of movements shed-to-shed and of movements between commercial chicken farms becomes available.

Biosecurity practices on Australian commercial layer and meat chicken farms: Performance and perceptions of farmers.

This paper describes the level of adoption of biosecurity practices performed on Australian commercial chicken meat and layer farms and farmer-perceived importance of these practices. On-farm interviews were conducted on 25 free range layer farms, nine cage layer farms, nine barn layer farms, six free range meat chicken farms and 15 barn meat chicken farms in the Sydney basin bioregion and South East Queensland. There was a high level of treatment of drinking water across all farm types; town water was the most common source. In general, meat chicken farms had a higher level of adoption of biosecurity practices than layer farms. Cage layer farms had the shortest median distance between sheds (7.75m) and between sheds and waterbodies (30m). Equipment sharing between sheds was performed on 43% of free range meat chicken farms compared to 92% of free range layer farms. There was little disinfection of this shared equipment across all farm types. Footbaths and visitor recording books were used by the majority of farms for all farm types except cage layer farms (25%). Wild birds in sheds were most commonly reported in free range meat chicken farms (73%). Dogs and cats were kept across all farm types, from 56% of barn layer farms to 89% of cage layer farms, and they had access to the sheds in the majority (67%) of cage layer farms and on the range in some free range layer farms (44%). Most biosecurity practices were rated on average as 'very important' by farmers. A logistic regression analysis revealed that for most biosecurity practices, performing a practice was significantly associated with higher perceived farmer importance of that biosecurity practice. These findings help identify farm types and certain biosecurity practices with low adoption levels. This information can aid decision-making on efforts used to improve adoption levels.

Correction: Comparisons of management practices and farm design on Australian commercial layer and meat chicken farms: Cage, barn and free range.

[This corrects the article DOI: 10.1371/journal.pone.0188505.].

Modelling low pathogenic avian influenza introduction into the commercial poultry industry.

Outbreaks of highly pathogenic avian influenza (HPAI) in commercial poultry flocks are rare but highly disruptive to the industry. There is evidence that low pathogenic avian influenza (LPAI) can transfer from wild birds to domestic flocks, where it may mutate to HPAI, and the industry is concerned that an increasing demand for free-range produce may affect the risk of LPAI and HPAI outbreaks. In this paper we focus on LPAI introduction and establishment, and formulate a branching process model to compare risk between sectors and their contribution to overall industry-level risk. Our aim is to determine how heterogeneity in avian influenza viruses and the distinct population structures of each sector - caged, barn and free-range, meat and layer - interact with a continuous risk of virus introduction to affect outbreak probabilities. We show that free-range access is the most influential driver of LPAI outbreaks, with production cycle length having relatively little effect. We demonstrate that variation in virus transmission rates is particularly important when modelling avian influenza introduction to domestic poultry. Virus-free status is of interest for biosecurity and we distinguish how it differs from the usual probability of extinction, and discuss how production cycle length affects this difference. We also use the nonlinear relationship between shed size and risk to identify conditions for which shed size is most influential.

Assessing the probability of introduction and spread of avian influenza (AI) virus in commercial Australian poultry operations using an expert opinion elicitation.

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.

Comparisons of management practices and farm design on Australian commercial layer and meat chicken farms: Cage, barn and free range.

There are few published studies describing the unique management practices, farm design and housing characteristics of commercial meat chicken and layer farms in Australia. In particular, there has been a large expansion of free range poultry production in Australia in recent years, but limited information about this enterprise exists. This study aimed to describe features of Australian commercial chicken farms, with particular interest in free range farms, by conducting on-farm interviews of 25 free range layer farms, nine cage layer farms, nine barn layer farms, six free range meat chicken farms and 15 barn meat chicken farms in the Sydney basin bioregion and South East Queensland. Comparisons between the different enterprises (cage, barn and free range) were explored, including stocking densities, depopulation procedures, environmental control methods and sources of information for farmers. Additional information collected for free range farms include range size, range characteristics and range access. The median number of chickens per shed was greatest in free range meat chicken farms (31,058), followed by barn meat chicken (20,817), free range layer (10,713), barn layer (9,300) and cage layer farms (9,000). Sheds had cooling pads and tunnel ventilation in just over half of both barn and free range meat chicken farms (53%, n = 8) and was least common in free range layer farms (16%, n = 4). Range access in free range meat chicken farms was from sunrise to dark in the majority (93%, n = 14) of free range meat chicken farms. Over half of free range layer farms (56%, n = 14) granted range access at a set time each morning; most commonly between 9:00 to 10.00am (86%, n = 12), and chickens were placed back inside sheds when it was dusk.

Eliminating infectious diseases of livestock: a metapopulation model of infection control.

When novel disease outbreaks occur in livestock, policy makers must respond promptly to eliminate disease, and are typically called on to make control decisions before detailed analysis of disease parameters can be undertaken. We present a flexible metapopulation model of disease spread that incorporates variation in livestock density and includes occasional high-mixing locations or events, such as markets or race meetings. Using probability generating functions derived from this branching process model, we compare the likely success of reactive control strategies in eliminating disease spread. We find that the optimal vaccine strategy varies according to the disease transmission rate, with homogeneous vaccination most effective for low transmission rates, and heterogeneous vaccination preferable for high levels of transmission. Quarantine combines well with vaccination, with the chance of disease elimination enhanced even for vaccines with low efficacy. Control decisions surrounding horse race meetings were of particular concern during the 2007 outbreak of equine influenza in Australia. We show that this type of high-mixing event is a powerful spread mechanism, even when the proportion of time spent at such events is low. If such locations remain open, elimination will require a highly effective vaccine with high coverage. However, a policy of banning animals from quarantined regions from attending such events can provide an effective alternative if full closure of events is economically or politically untenable.

Host gastro-intestinal dynamics and the frequency of colicin production by Escherichia coli.

The production of antimicrobial compounds known as colicins has been shown to be an important mediator of competitive interactions among Escherichia coli genotypes. There is some understanding of the forces responsible for determining the frequency of colicin production in E. coli populations; however, this understanding cannot explain all of the observed variation. A survey of colicin production in E. coli isolated from native Australian mammals revealed that the frequency of colicin production in strains isolated from carnivores was significantly lower than the frequency of production in strains recovered from herbivores or omnivores. The intestine of Australian carnivores is tube-like and gut turnover rates are rapid compared with the turnover rates of the intestinal tracts of herbivores and omnivores, all of which possess a hindgut fermentation chamber. A mathematical model was developed in order to determine if variation in gut turnover rates could determine if a host was more likely to harbour a colicin-producing strain or a non-producer. The model predicted that a colicin producer was more likely to dominate in the gut of a host with lower gut turnover rates, and a non-producer to dominate in hosts with rapid gut turnover rates.

How much would closing schools reduce transmission during an influenza pandemic?

BACKGROUND: When deciding whether to close schools during an influenza pandemic, authorities must weigh the likely benefits against the expected social disruption. Although schools have been closed to slow the spread of influenza, there is limited evidence as to the impact on transmission of disease. METHODS: To assess the benefits of closing schools for various pandemic scenarios, we used a stochastic mathematical model of disease transmission fitted to attack rates from past influenza pandemics. We compared these benefits with those achieved by other interventions targeted at children. RESULTS: Closing schools can reduce transmission among children considerably, but has only a moderate impact on average transmission rates among all individuals (both adults and children) under most scenarios. Much of the benefit of closing schools can be achieved if schools are closed by the time that 2% of children are infected; if the intervention is delayed until 20% of children are infected, there is little benefit. Immunization of all school children provides only a slight improvement over closing schools, indicating that schools are an important venue for transmission between children. Relative attack rates in adults and children provide a good indication of the likely benefit of closing schools, with the greatest impact seen for infections with high attack rates in children. CONCLUSIONS: Closing schools is effective at reducing transmission between children but has only a moderate effect on average transmission rates in the wider population unless children are disproportionately affected.

The role of health care workers and antiviral drugs in the control of pandemic influenza.

Until a vaccine against the new strain becomes available, the response to newly emerged pandemic influenza will consist of the use of antiviral drugs and measures that limit exposure to infectious individuals. These first-line defence measures include isolating cases upon diagnosis, reducing close contacts, the use of personal protective equipment and hygiene, and using antiviral drugs for treatment and prophylaxis. There are significant 'costs' associated with control measures, so to justify such interventions it is important to assess their potential to reduce transmission. In this paper, we determine the effect that a number of different antiviral interventions have on the reproduction number of infectives and the probability that an imported infection fades out, and determine parameter scenarios for which these interventions are able to eliminate an emerging pandemic of influenza. We also assess the role that health care workers play in transmission and the extent to which providing them with antiviral prophylaxis and personal protective equipment modifies this role. Our results indicate that this class requires protection to avoid a greatly disproportionate contribution to early infective numbers, and for the maintenance of a stable health care system. Further, we show that the role children play in increasing transmission is moderate, in spite of closer mixing with other children.

Application of an ecological framework linking scales based on self-thinning.

Barnes and Roderick developed a generic, theoretical framework for vegetation modeling across scales. Inclusion of a self-thinning mechanism connects the individual to the larger-scale population and, being based on the conservation of mass, all mass flux processes are integral to the formulation. Significantly, disturbance (both regular and stochastic) and its impact at larger scales are included in the formulation. The purpose of this paper is to illustrate how this model can be used to predict patch and ecosystem dry mass, and consequently system carbon. Examples from pine plantations and mixed forests are considered, with these applications requiring estimates of system carrying capacity and the growth rates of individual plants. The results indicate that the model is relatively simple and straightforward to apply, and its predictions compare well with the data. A significant feature of this approach is that the impact of local scale data on the dynamics of larger patch and ecosystem scales can be determined explicitly, as we show by example. Further, the general formulation has an analytic solution based on characteristics of the individual, facilitating practical and predictive application.

Plant-herbivore models, where more grass means fewer grazers.

Classical theory has led us to believe that where more grazing is available herbivores will inflict heavier pressure on the grass, thus keeping its height low. This approach is hotly debated, although still widely accepted. Based on field data collected, van der Koppel et al. [van der Koppel, J., Huisman, J., van der Wal, R., Olff, H., 1996. Patterns of herbivory along a productivity gradient: an empirical and theoretical investigation. Ecology 77, 736-745] contest the standard plant-herbivore models, arguing that herbivores do not 'control' the plant growth entirely, and propose two differential equation models. In this paper we describe briefly how van der Koppel et al. (1996) derive their uncontrolled plant-herbivore interaction models, and then expand on the specific mathematical results cited in their paper to provide a global overview of the dynamics of such systems, for a broad range of parameter values.

An ecological framework linking scales across space and time based on self-thinning.

Scaling up from measurements made at small spatial and short temporal scales is a central challenge in the ecological and related sciences, where predictions at larger scales and over long time periods are required. It involves two quite distinct aspects: a formulation of a theoretical framework for calculating space-time averages, and an acquisition of data to support that framework. In this paper, we address the theoretical part of the question, and although our primary motivation was an understanding of carbon accounting our formulation is more general. To that end, we adopt a dynamical systems approach, and incorporate a new dynamical formulation of self-thinning. We show how to calculate rates of change for total (and average) plant dry mass, volume, and carbon, in terms of the properties of the individual plants. The results emphasize how local scale statistics (such as, variation in the size of individuals) lead to nonlinear variation at larger scales. Further, we describe how regular and stochastic disturbance can be readily incorporated into this framework. It is shown that stochastic disturbance at patch-scales, results in (to first approximation) regular disturbance at ecosystem scales, and hence can be formulated as such. We conclude that a dynamical formulation of self-thinning can be used as a generic framework for scaling ecological processes in space and time.

Coliform dynamics and the implications for source tracking.

In many parts of the world, coliform counts in recreational waters are unacceptably high. In an attempt to rectify this problem, programmes are under way to develop methods that will allow the sources of the faecal contamination thought to be responsible for these elevated counts to be identified. The success of these efforts depends on the validity of several assumptions that underlie many of the proposed methods. One of the critical assumptions is that the clonal composition of the coliform species being monitored in a water body reflects the clonal composition of the species in the host populations responsible for the faecal inputs into that water body. To determine the extent to which among-strain variation in a coliform species might invalidate this assumption, a series of simple mathematical models was proposed and analysed. The first series of models assumed that all cells of species were identical. The question posed was - is the density of a coliform species in a body of water linearly related to the rate at which cells of the species enter the water body via faecal production? The results of these models suggest that, over a wide range of conditions, cell densities in the water body are linearly related to the rate at which cells enter the water body as a result of faecal contamination. This outcome occurs whether or not cells are capable of division in the external environment. When the rate of cell division depends on the concentration of available nutrients then, when nutrient input rates are 'high' and rates of faecal contamination are 'low', this linear relationship does not hold. The second series of models assumed that the coliform species consists of different strains and that these strains differ in their performance in the external environment. The results of these multistrain models show that the relative abundance of strains in the external environment is unlikely to reflect their relative abundance in the faecal inputs to the environment. Consequently, statements such as - domestic animals are responsible for 30% and wildlife for 70% of the faecal inputs to a water body - may well be meaningless.