Veterinary Biosciences - Research Publications
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ItemPerformance Evaluation and Validation of Air Samplers To Detect Aerosolized Coxiella burnetii(AMER SOC MICROBIOLOGY, 2022-10-26)Coxiella burnetii, the etiological agent of Q fever, is an intracellular zoonotic pathogen transmitted via the respiratory route. Once released from infected animals, C. burnetii can travel long distances through air before infecting another host. As such, the ability to detect the presence of C. burnetii in air is important. In this study, three air samplers, AirPort MD8, BioSampler, and the Coriolis Micro, were assessed against a set of predetermined criteria in the presence of three different aerosolized C. burnetii concentrations. Two liquid collection media, phosphate-buffered saline (PBS) and alkaline polyethylene glycol (Alk PEG), were tested with devices requiring a collection liquid. Samples were tested by quantitative polymerase chain reaction assay (qPCR) targeting the single-copy com1 gene or multicopy insertion element IS1111. All air samplers performed well at detecting airborne C. burnetii across the range of concentrations tested. At high nebulized concentrations, AirPort MD8 showed higher, but variable, recovery probabilities. While the BioSampler and Coriolis Micro recovered C. burnetii at lower concentrations, the replicates were far more repeatable. At low and intermediate nebulized concentrations, results were comparable in the trials between air samplers, although the AirPort MD8 had consistently higher recovery probabilities. In this first study validating air samplers for their ability to detect aerosolized C. burnetii, we found that while all samplers performed well, not all samplers were equal. It is important that these results are further validated under field conditions. These findings will further inform efforts to detect airborne C. burnetii around known point sources of infection. IMPORTANCE Coxiella burnetii causes Q fever in humans and coxiellosis in animals. It is important to know if C. burnetii is present in the air around putative sources as it is transmitted via inhalation. This study assessed air samplers (AirPort MD8, BioSampler, and Coriolis Micro) for their efficacy in detecting C. burnetii. Our results show that all three devices could detect aerosolized bacteria effectively; however, at high concentrations the AirPort performed better than the other two devices, showing higher percent recovery. At intermediate and low concentrations AirPort detected at a level higher than or similar to that of other samplers. Quantification of samples was hindered by the limit of quantitation of the qPCR assay. Compared with the other two devices, the AirPort was easier to handle and clean in the field. Testing air around likely sources (e.g., farms, abattoirs, and livestock saleyards) using validated sampling devices will help better estimate the risk of Q fever to nearby communities.
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ItemA cross-sectional survey of risk factors for the presence of Coxiella burnetii in Australian commercial dairy goat farms(WILEY, 2022-07)The largest Australian farm-based outbreak of Q fever originated from a dairy goat herd. We surveyed commercial dairy goat farms across Australia by testing bulk tank milk (BTM) samples using a commercial indirect enzyme-linked immunosorbent assay and two quantitative polymerase chain reactions (PCRs). Of the 66 commercial dairy goat herds on record, managers from 61 herds were contacted and 49 provided BTM samples. Five of the surveyed herds were positive on at least one of the diagnostic tests, thus herd-level apparent prevalence was 10% (95% confidence interval [CI] 4 to 22). True prevalence was estimated to be 3% (95% credible interval: 0 to 18). Herd managers completed a questionnaire on herd management, biosecurity and hygiene practices and risk factors were investigated using multivariable logistic regression. Herds with >900 milking does (the upper quartile) were more likely to be Coxiella burnetii positive (odds ratio = 6.75; 95% CI 1.65 to 27.7) compared with farms with ≤900 milking does. The odds of BTM positivity increased by a factor of 2.53 (95% CI 1.51 to 4.22) for each order of magnitude increase in the number of goats per acre. C. burnetii was not detected in samples from the majority of the Australian dairy goat herds suggesting there is an opportunity to protect the industry and contain this disease with strengthened biosecurity practices. Intensification appeared associated with an increased risk of positivity. Further investigation is required to discriminate the practices associated with an increased risk of introduction to disease-free herds, from practices associated with maintenance of C. burnetii infection in infected dairy goat herds.
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ItemRisk factors for campylobacteriosis in Australia: outcomes of a 2018-2019 case-control study(BMC, 2022-06-30)BACKGROUND: We aimed to identify risk factors for sporadic campylobacteriosis in Australia, and to compare these for Campylobacter jejuni and Campylobacter coli infections. METHODS: In a multi-jurisdictional case-control study, we recruited culture-confirmed cases of campylobacteriosis reported to state and territory health departments from February 2018 through October 2019. We recruited controls from notified influenza cases in the previous 12 months that were frequency matched to cases by age group, sex, and location. Campylobacter isolates were confirmed to species level by public health laboratories using molecular methods. We conducted backward stepwise multivariable logistic regression to identify significant risk factors. RESULTS: We recruited 571 cases of campylobacteriosis (422 C. jejuni and 84 C. coli) and 586 controls. Important risk factors for campylobacteriosis included eating undercooked chicken (adjusted odds ratio [aOR] 70, 95% CI 13-1296) or cooked chicken (aOR 1.7, 95% CI 1.1-2.8), owning a pet dog aged < 6 months (aOR 6.4, 95% CI 3.4-12), and the regular use of proton-pump inhibitors in the 4 weeks prior to illness (aOR 2.8, 95% CI 1.9-4.3). Risk factors remained similar when analysed specifically for C. jejuni infection. Unique risks for C. coli infection included eating chicken pâté (aOR 6.1, 95% CI 1.5-25) and delicatessen meats (aOR 1.8, 95% CI 1.0-3.3). Eating any chicken carried a high population attributable fraction for campylobacteriosis of 42% (95% CI 13-68), while the attributable fraction for proton-pump inhibitors was 13% (95% CI 8.3-18) and owning a pet dog aged < 6 months was 9.6% (95% CI 6.5-13). The population attributable fractions for these variables were similar when analysed by campylobacter species. Eating delicatessen meats was attributed to 31% (95% CI 0.0-54) of cases for C. coli and eating chicken pâté was attributed to 6.0% (95% CI 0.0-11). CONCLUSIONS: The main risk factor for campylobacteriosis in Australia is consumption of chicken meat. However, contact with young pet dogs may also be an important source of infection. Proton-pump inhibitors are likely to increase vulnerability to infection.
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ItemValidation of an Indirect Immunofluorescence Assay and Commercial Q Fever Enzyme-Linked Immunosorbent Assay for Use in Macropods(AMER SOC MICROBIOLOGY, 2022-07-20)Kangaroos are considered to be an important reservoir of Q fever in Australia, although there is limited knowledge on the true prevalence and distribution of coxiellosis in Australian macropod populations. Serological tests serve as useful surveillance tools, but formal test validation is needed to be able to estimate true seroprevalence rates, and few tests have been validated to screen wildlife species for Q fever. In this study, we modified and optimized a phase-specific indirect immunofluorescence assay (IFA) for the detection of IgG antibodies against Coxiella burnetii in macropod sera. The assay was validated against the commercially available ID Screen Q fever indirect multispecies enzyme-linked immunosorbent assay (ELISA) kit (IDVet, Grabels, France) to estimate the diagnostic sensitivity and specificity of each assay, using Bayesian latent class analysis. A direct comparison of the two tests was performed by testing 303 serum samples from 10 macropod populations from the east coast of Australia and New Zealand. The analysis indicated that the IFA had relatively high diagnostic sensitivity (97.6% [95% credible interval [CrI], 88.0 to 99.9]) and diagnostic specificity (98.5% [95% CrI, 94.4 to 99.9]). In comparison, the ELISA had relatively poor diagnostic sensitivity (42.1% [95% CrI, 33.7 to 50.8]) and similar diagnostic specificity (99.2% [95% CrI, 96.4 to 100]) using the cutoff values recommended by the manufacturer. The estimated true seroprevalence of C. burnetii exposure in the macropod populations included in this study ranged from 0% in New Zealand and Victoria, Australia, up to 94.2% in one population from New South Wales, Australia.
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ItemIdentifying scenarios and risk factors for Q fever outbreaks using qualitative analysis of expert opinion(WILEY, 2022-06)Q fever is an important zoonotic disease perceived to be an occupational hazard for those working with livestock. Outbreaks involving large numbers of people are uncommon, but the increasing case incidence coupled with changing environmental and industry conditions that promote transmission of Q fever has raised concerns that large and serious outbreaks could become more frequent. The aim of this study was to use expert opinion to better understand how large Q fever outbreaks might occur in an Australian context and to document factors believed to be drivers of disease transmission. Focus groups were conducted with human and animal health professionals across several Australian states. All discussions were recorded, transcribed verbatim and imported into NVIVO for thematic analysis. Four anthropogenic risk factors (disease awareness, industry practices, land use, human behaviour) and three ecological risk factors (physical environment, agent dissemination, animal hosts) emerged from the data. Analysis of expert opinions pointed to the existence of numerous scenarios in which Q fever outbreaks could occur, many of which depict acquisition in the wider community outside of traditional at-risk occupations. This perception of the expansion of Q fever from occupational-acquisition to community-acquisition is driven by greater overarching economic, political and socio-cultural influences that govern the way in which people live and work. Findings from this study highlight that outbreaks are complex phenomena that involve the convergence of diverse elements, not just that of the pathogen and host, but also the physical, political and socioeconomic environments in which they interact. A review of the approaches to prevent and manage Q fever outbreaks will require a multisectorial approach and strengthening of community education, communication and engagement so that all stakeholders become an integrated part of outbreak mitigation and response.
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ItemNo Preview AvailableUsing farmer observations for animal health syndromic surveillance: Participation and performance of an online enhanced passive surveillance system(ELSEVIER, 2021-03)The challenge of animal health surveillance is to provide the information necessary to appropriately inform disease prevention and control activities within the constraints of available resources. Syndromic surveillance of farmers' disease observations can improve animal health data capture from extensive livestock farming systems, especially where data are not otherwise being systematically collected or when data on confirmed aetiological diagnoses are unavailable at the disease level. As it is rarely feasible to recruit a truly random sample of farmers to provide observational reports, directing farmer sampling to align with the surveillance objectives is a reasonable and practical approach. As long as potential bias is recognised and managed, farmers who will report reliably can be desirable participants in a surveillance system. Thus, one early objective of a surveillance program should be to identify characteristics associated with reporting behaviour. Knowledge of the demographic and managerial characteristics of good reporters can inform efforts to recruit additional farms into the system or aid understanding of potential bias of system reports. We describe the operation of a farmer syndromic surveillance system in Victoria, Australia, over its first two years from 2014 to 2016. Survival analysis and classification and regression tree analysis were used to identify farm level factors associated with 'reliable' participation (low non-response rates in longitudinal reporting). Response rate and timeliness were not associated with whether farmers had disease to report, or with different months of the year. Farmers keeping only sheep were the most reliable and timely respondents. Farmers < 43 years of age had lower response rates than older farmers. Farmers with veterinary qualifications and those working full-time on-farm provided less timely reports than other educational backgrounds and farmers who worked part-time on-farm. These analyses provide a starting point to guide recruitment of participants for surveillance of farmers' observations using syndromic surveillance, and provide examples of strengths and weaknesses of syndromic surveillance systems for extensively-managed livestock. Once farm characteristics associated with reliable participation are known, they can be incorporated into surveillance system design in accordance with the objectives of the system.
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ItemNo Preview AvailableThe prevalence and risk factors for Coxiella burnetii on commercial dairy goat farms in Australia(OXFORD UNIV PRESS, 2021-09-01)Abstract Background Despite the potentially important role that intensively managed dairy goats play in the spread of Q fever, the prevalence of Coxiella burnetii among dairy goat herds in Australia is largely unknown. The aim of this cross-sectional study was to estimate the prevalence of coxiellosis-positive dairy goat herds in Australia and to identify risk factors associated with coxiellosis positivity. Methods Owners or managers of commercial dairy goat herds were contacted and asked to complete a questionnaire about risk factors for coxiellosis and to provide a bulk tank milk (BTM) sample. BTM samples were tested using an enzyme-linked immunosorbent assay (ELISA) and real-time quantitative polymerase chain reaction (RT-PCR) targeting the Com1 and IS1111 sections of the C. burnetii genome. Questionnaire responses from coxiellosis positive and coxiellosis negative herds were compared using frequency cross-tabulations and multivariable logistic regression. Results Herd managers from 49 of the 61 commercial dairy goat herds in Australia took part in the study. Of this group, three BTM samples were found to be both ELISA and RT-PCR positive. Two BTM samples were ELISA positive but RT-PCR negative. There were 10 (95% CI 4.4 to 22) C. burnetii positive herds per 100 herds at risk. Conclusions The prevalence of coxiellosis among commercial dairy goat farms in Australia is relatively low. Key messages The Australian dairy goat industry should focus on biosecurity measures and risk management plans to reduce the probability of C. burnetii introduction.
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ItemSpatiotemporal and risk analysis of H5 highly pathogenic avian influenza in Vietnam, 2014-2017(ELSEVIER, 2020-05)The aim of this study was to describe the spatiotemporal distribution of H5 HPAI outbreak reports for the period 2014-2017 and to identify factors associated with H5 HPAI outbreak reports. Throughout the study period, a total of 139 outbreaks of H5 HPAI in poultry were reported, due to either H5N1 (96 outbreaks) or H5N6 (43 outbreaks) subtype viruses. H5N1 HPAI outbreaks occurred in all areas of Vietnam while H5N6 HPAI outbreaks were only reported in the northern and central provinces. We counted the number of H5N1 and H5N6 outbreak report-positive districts per province over the four-year study period and calculated the provincial-level standardized morbidity ratio for H5N1 and H5N6 outbreak reports as the observed number of positive districts divided by the expected number. A mixed-effects, zero-inflated Poisson regression model was developed to identify risk factors for outbreak reports of each H5N1 and H5N6 subtype virus. Spatially correlated and uncorrelated random effects terms were included in this model to identify areas of the country where outbreak reports occurred after known risk factors had been accounted-for. The presence of an outbreak report in a province in the previous 6-12 months increased the provincial level H5N1 outbreak report risk by a factor of 2.42 (95% Bayesian credible interval [CrI] 1.27-4.60) while 1000 bird increases in the density of chickens decreased provincial level H5N6 outbreak report risk by a factor of 0.65 (95% CrI 0.38 to 0.97). We document distinctly different patterns in the spatial and temporal distribution of H5N1 and H5N6 outbreak reports. Most of the variation in H5N1 report risk was accounted-for by the fixed effects included in the zero-inflated Poisson model. In contrast, the amount of unaccounted-for risk in the H5N6 model was substantially greater than the H5N1 model. For H5N6 we recommend that targeted investigations should be carried out in provinces with relatively large spatially correlated random effect terms to identify likely determinants of disease. Similarly, investigations should be carried out in provinces with relatively low spatially correlated random effect terms to identify protective factors for disease and/or reasons for failure to report.
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ItemThe forecasting of dynamical Ross River virus outbreaks: Victoria, Australia(ELSEVIER, 2020-03)Ross River virus (RRV) is Australia's most epidemiologically important mosquito-borne disease. During RRV epidemics in the State of Victoria (such as 2010/11 and 2016/17) notifications can account for up to 30% of national RRV notifications. However, little is known about factors which can forecast RRV transmission in Victoria. We aimed to understand factors associated with RRV transmission in epidemiologically important regions of Victoria and establish an early warning forecast system. We developed negative binomial regression models to forecast human RRV notifications across 11 Local Government Areas (LGAs) using climatic, environmental, and oceanographic variables. Data were collected from July 2008 to June 2018. Data from July 2008 to June 2012 were used as a training data set, while July 2012 to June 2018 were used as a testing data set. Evapotranspiration and precipitation were found to be common factors for forecasting RRV notifications across sites. Several site-specific factors were also important in forecasting RRV notifications which varied between LGA. From the 11 LGAs examined, nine experienced an outbreak in 2011/12 of which the models for these sites were a good fit. All 11 LGAs experienced an outbreak in 2016/17, however only six LGAs could predict the outbreak using the same model. We document similarities and differences in factors useful for forecasting RRV notifications across Victoria and demonstrate that readily available and inexpensive climate and environmental data can be used to predict epidemic periods in some areas. Furthermore, we highlight in certain regions the complexity of RRV transmission where additional epidemiological information is needed to accurately predict RRV activity. Our findings have been applied to produce a Ross River virus Outbreak Surveillance System (ROSS) to aid in public health decision making in Victoria.
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ItemOptimising predictive modelling of Ross River virus using meteorological variables(PUBLIC LIBRARY SCIENCE, 2021-03)BACKGROUND: Statistical models are regularly used in the forecasting and surveillance of infectious diseases to guide public health. Variable selection assists in determining factors associated with disease transmission, however, often overlooked in this process is the evaluation and suitability of the statistical model used in forecasting disease transmission and outbreaks. Here we aim to evaluate several modelling methods to optimise predictive modelling of Ross River virus (RRV) disease notifications and outbreaks in epidemiological important regions of Victoria and Western Australia. METHODOLOGY/PRINCIPAL FINDINGS: We developed several statistical methods using meteorological and RRV surveillance data from July 2000 until June 2018 in Victoria and from July 1991 until June 2018 in Western Australia. Models were developed for 11 Local Government Areas (LGAs) in Victoria and seven LGAs in Western Australia. We found generalised additive models and generalised boosted regression models, and generalised additive models and negative binomial models to be the best fit models when predicting RRV outbreaks and notifications, respectively. No association was found with a model's ability to predict RRV notifications in LGAs with greater RRV activity, or for outbreak predictions to have a higher accuracy in LGAs with greater RRV notifications. Moreover, we assessed the use of factor analysis to generate independent variables used in predictive modelling. In the majority of LGAs, this method did not result in better model predictive performance. CONCLUSIONS/SIGNIFICANCE: We demonstrate that models which are developed and used for predicting disease notifications may not be suitable for predicting disease outbreaks, or vice versa. Furthermore, poor predictive performance in modelling disease transmissions may be the result of inappropriate model selection methods. Our findings provide approaches and methods to facilitate the selection of the best fit statistical model for predicting mosquito-borne disease notifications and outbreaks used for disease surveillance.