Novel vaccine acceptance in emerging infectious diseases: a case study of Hendra virus vaccine uptake in Australia

Background: The development of a horse vaccine against Hendra virus has been hailed as a good example of a One Health approach to the control of human disease. Although there is little doubt that this is true, it is clear from the underwhelming uptake of the vaccine by horse owners to date (approximately 10%) that realisation of a One Health approach requires more than just a scientific solution. As emerging infectious diseases may often be linked to the development and implementation of novel vaccines this presentation will discuss factors influencing their uptake; using Hendra virus in Australia as a case study. Methods: This presentation will draw on data collected from the Horse owners and Hendra virus: A Longitudinal cohort study To Evaluate Risk (HHALTER) study. The HHALTER study is a mixed methods research study comprising a two-year survey-based longitudinal cohort study and qualitative interview study with horse owners in Australia. The HHALTER study has investigated and tracked changes in a broad range of issues around early uptake of vaccination, horse owner uptake of other recommended disease risk mitigation strategies, and attitudes to government policy and disease response. Interviews provide further insights into attitudes towards risk and decision-making in relation to vaccine uptake. A combination of quantitative and qualitative data analysis will be reported. Results: Data collected from more than 1100 horse owners shortly after vaccine introduction indicated that vaccine uptake and intention to vaccinate was associated with a number of risk perception factors and financial cost factors. In addition, concerns about side effects and veterinarians refusing to treat unvaccinated horses were linked to uptake. Across the study period vaccine uptake in the study cohort increased to more than 50%, however, concerns around side effects, equine performance and breeding impacts, delays to full vaccine approvals, and attempts to mandate vaccination by horse associations and event organisers have all impacted acceptance. Conclusion: Despite being provided with a safe and effective vaccine for Hendra virus that can protect horses and break the transmission cycle of the virus to humans, Australian horse owners have been reluctant to commit to it. General issues pertinent to novel vaccines, combined with challenges in the implementation of the vaccine have led to issues of mistrust and misconception with some horse owners. Moreover, factors such as cost, booster dose schedules, complexities around perceived risk, and ulterior motives attributed to veterinarians have only served to polarise attitudes to vaccine acceptance.

Longitudinal cohort study of horse owners

This report summarises the findings of a three-year mixed methods research study designed to capture factors that influence horse owner Hendra virus (HeV) risk mitigation practices. The research project focuses on horse owners; their knowledge, attitudes, and risk mitigation practices, i.e. uptake of vaccination, property management, and biosecurity practices. A flexible research methodology enabled the tracking of core subject areas over time whilst also responding to new or evolving shifts in the HeV landscape, e.g. new HeV cases, event management, and issues arising in the vaccine roll-out. By tracking relationships within the data and engaging with stakeholders and the horse owner population, it is hoped that findings from the study will help to identify important linkages and effective strategies for communication/information and policy implementation.

Estimation in a multiplicative mixed model involving a genetic relationship matrix.

Genetic models partitioning additive and non-additive genetic effects for populations tested in replicated multi-environment trials (METs) in a plant breeding program have recently been presented in the literature. For these data, the variance model involves the direct product of a large numerator relationship matrix A, and a complex structure for the genotype by environment interaction effects, generally of a factor analytic (FA) form. With MET data, we expect a high correlation in genotype rankings between environments, leading to non-positive definite covariance matrices. Estimation methods for reduced rank models have been derived for the FA formulation with independent genotypes, and we employ these estimation methods for the more complex case involving the numerator relationship matrix. We examine the performance of differing genetic models for MET data with an embedded pedigree structure, and consider the magnitude of the non-additive variance. The capacity of existing software packages to fit these complex models is largely due to the use of the sparse matrix methodology and the average information algorithm. Here, we present an extension to the standard formulation necessary for estimation with a factor analytic structure across multiple environments.

Participatory adaptation and mitigation strategies for climate change on mixed farms

The project uses participatory methods to engage primary producers and advisers in central Queensland, southern Queensland, and north east New South Wales on-farm trials and demonstrations to adapt mixed farming systems to changed climate conditions. The focus is adaptation to climate change but will support abatement of greenhouse gas emissions by building soil carbon, better managing soil nitrogen and soil organic carbon. Data will be collected and integrated with data from Round 1 of the Climate Change Research Program to extend industry understanding beyond a general awareness of ‘climate change’. Nitrous oxide and soil carbon data will help farmers/advisers understand the implications of climate change and develop adaptation strategies for a more sustainable, climate sensitive future.

Prediction of breeding values for average fruit weight in mango using a multivariate individual mixed model

Mango is an important horticultural fruit crop and breeding is a key strategy to improve ongoing sustainability. Knowledge of breeding values of potential parents is important for maximising progress from breeding. This study successfully employed a mixed linear model methods incorporating a pedigree to predict breeding values for average fruit weight from highly unbalanced data for genotypes planted over three field trials and assessed over several harvest seasons. Average fruit weight was found to be under strong additive genetic control. There was high correlation between hybrids propagated as seedlings and hybrids propagated as scions grafted onto rootstocks. Estimates of additive genetic correlation among trials ranged from 0.69 to 0.88 with correlations among harvest seasons within trials greater than 0.96. These results suggest that progress from selection for broad adaptation can be achieved, particularly as no repeatable environmental factor that could be used to predict G x E could be identified. Predicted breeding values for 35 known cultivars are presented for use in ongoing breeding programs.

Development and testing of allometric equations for estimating above-ground biomass of mixed-species environmental plantings

To quantify the impact that planting indigenous trees and shrubs in mixed communities (environmental plantings) have on net sequestration of carbon and other environmental or commercial benefits, precise and non-biased estimates of biomass are required. Because these plantings consist of several species, estimation of their biomass through allometric relationships is a challenging task. We explored methods to accurately estimate biomass through harvesting 3139 trees and shrubs from 22 plantings, and collating similar datasets from earlier studies, in non-arid (>300mm rainfallyear-1) regions of southern and eastern Australia. Site-and-species specific allometric equations were developed, as were three types of generalised, multi-site, allometric equations based on categories of species and growth-habits: (i) species-specific, (ii) genus and growth-habit, and (iii) universal growth-habit irrespective of genus. Biomass was measured at plot level at eight contrasting sites to test the accuracy of prediction of tonnes dry matter of above-ground biomass per hectare using different classes of allometric equations. A finer-scale analysis tested performance of these at an individual-tree level across a wider range of sites. Although the percentage error in prediction could be high at a given site (up to 45%), it was relatively low (<11%) when generalised allometry-predictions of biomass was used to make regional- or estate-level estimates across a range of sites. Precision, and thus accuracy, increased slightly with the level of specificity of allometry. Inclusion of site-specific factors in generic equations increased efficiency of prediction of above-ground biomass by as much as 8%. Site-and-species-specific equations are the most accurate for site-based predictions. Generic allometric equations developed here, particularly the generic species-specific equations, can be confidently applied to provide regional- or estate-level estimates of above-ground biomass and carbon. © 2013 Elsevier B.V.