Belinda Barnes

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.

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.

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.

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.

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.

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.

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.

The Impact of Dispersion in the Number of Secondary Infections on the Probability of an Epidemic

Heterogeneity in communities is a key factor to consider when modelling the transmission of an infection or implementing strategies to control its transmission. Heterogeneity in the level of infectiousness of individuals can arise in a number of ways. For example, it can arise through the structure of a community with, say, households, through the nature of a disease that may include superspreaders and others who are hardly infectious (like SARS), or through mixing and behaviour patterns that can be altered by interventions. Lloyd-Smith et al. (Lloyd-Smith, Schreiber, Kopp and Getz (2005)) observed that, under certain specific assumptions, greater heterogeneity, leads to a greater probability of disease elimination. In this paper we explore the impact of heterogeneity on the probability of disease elimination more generally. We show that, for many commonly arising distributions in ecology and epidemiology, an increase in heterogeneity, when the mean is fixed, leads to a reduction in the probability of a local outbreak. This result has important consequences for health care strategies, such as choosing strategies that increase heterogeneity for the same mean level of infectivity thereby delaying, or possibly preventing, an outbreak. However, while broadly true, including for most offspring distributions common to epidemic and ecological situations, the result is not in general true as we show by counter-examples for each of the types of heterogeneity considered. We conjecture a general principle determining when the result holds, but it remains an open question precisely when greater heterogeneity leads to an increase in the probability of extinction.

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.