Journal Articles
Permanent URI for this collectionhttps://mro.massey.ac.nz/handle/10179/7915
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Item A novel Bayesian Latent Class Model (BLCM) evaluates multiple continuous and binary tests: A case study for Brucella abortus in dairy cattle.(Elsevier B.V., 2024-03-01) Wang Y; Vallée E; Compton C; Heuer C; Guo A; Wang Y; Zhang Z; Vignes MBovine brucellosis, primarily caused by Brucella abortus, severely affects both animal health and human well-being. Accurate diagnosis is crucial for designing informed control and prevention measures. Lacking a gold standard test makes it challenging to determine optimal cut-off values and evaluate the diagnostic performance of tests. In this study, we developed a novel Bayesian Latent Class Model that integrates both binary and continuous testing outcomes, incorporating additional fixed (parity) and random (farm) effects, to calibrate optimal cut-off values by maximizing Youden Index. We tested 651 serum samples collected from six dairy farms in two regions of Henan Province, China with four serological tests: Rose Bengal Test, Serum Agglutination Test, Fluorescence Polarization Assay, and Competitive Enzyme-Linked Immunosorbent Assay. Our analysis revealed that the optimal cut-off values for FPA and C-ELISA were 94.2 mP and 0.403 PI, respectively. Sensitivity estimates for the four tests ranged from 69.7% to 89.9%, while specificity estimates varied between 97.1% and 99.6%. The true prevalences in the two study regions in Henan province were 4.7% and 30.3%. Parity-specific odds ratios for positive serological status ranged from 1.2 to 2.2 for different parity groups compared to primiparous cows. This approach provides a robust framework for validating diagnostic tests for both continuous and discrete tests in the absence of a gold standard test. Our findings can enhance our ability to design targeted disease detection strategies and implement effective control measures for brucellosis in Chinese dairy farms.Item A cross-sectional investigation of Leptospira at the wildlife-livestock interface in New Zealand(PLOS, 2023-09-06) Moinet M; Oosterhof H; Nisa S; Haack N; Wilkinson DA; Aberdein D; Russell JC; Vallée E; Collins-Emerson J; Heuer C; Benschop J; Stevenson BThere has been a recent upsurge in human cases of leptospirosis in New Zealand, with wildlife a suspected emerging source, but up-to-date knowledge on this topic is lacking. We conducted a cross-sectional study in two farm environments to estimate Leptospira seroprevalence in wildlife and sympatric livestock, PCR/culture prevalence in wildlife, and compare seroprevalence and prevalence between species, sex, and age groups. Traps targeting house mice (Mus musculus), black rats (Rattus rattus), hedgehogs (Erinaceus europaeus) and brushtail possums (Trichosurus vulpecula) were set for 10 trap-nights in March-April 2017 on a dairy (A) and a beef and sheep (B) farm. Trapped wild animals and an age-stratified random sample of domestic animals, namely cattle, sheep and working dogs were blood sampled. Sera were tested by microagglutination test for five serogroups and titres compared using a Proportional Similarity Index (PSI). Wildlife kidneys were sampled for culture and qPCR targeting the lipL32 gene. True prevalence in mice was assessed using occupancy modelling by collating different laboratory results. Infection profiles varied by species, age group and farm. At the MAT cut-point of ≥ 48, up to 78% of wildlife species, and 16-99% of domestic animals were seropositive. Five of nine hedgehogs, 23/105 mice and 1/14 black rats reacted to L. borgpetersenii sv Ballum. The sera of 4/18 possums and 4/9 hedgehogs reacted to L. borgpetersenii sv Hardjobovis whilst 1/18 possums and 1/9 hedgehogs reacted to Tarassovi. In ruminants, seroprevalence for Hardjobovis and Pomona ranged 0-90% and 0-71% depending on the species and age group. Titres against Ballum, Tarassovi and Copenhageni were also observed in 4-20%, 0-25% and 0-21% of domestic species, respectively. The PSI indicated rodents and livestock had the most dissimilar serological responses. Three of nine hedgehogs, 31/105 mice and 2/14 rats were carrying leptospires (PCR and/or culture positive). True prevalence estimated by occupancy modelling in mice was 38% [95% Credible Interval 26, 51%] on Farm A and 22% [11, 40%] on Farm B. In the same environment, exposure to serovars found in wildlife species was commonly detected in livestock. Transmission pathways between and within species should be assessed to help in the development of efficient mitigation strategies against Leptospira.Item Creating symptom-based criteria for diagnostic testing: a case study based on a multivariate analysis of data collected during the first wave of the COVID-19 pandemic in New Zealand(BioMed Central Ltd, 2021-12) French N; Jones G; Heuer C; Hope V; Jefferies S; Muellner P; McNeill A; Haslett S; Priest PBACKGROUND: Diagnostic testing using PCR is a fundamental component of COVID-19 pandemic control. Criteria for determining who should be tested by PCR vary between countries, and ultimately depend on resource constraints and public health objectives. Decisions are often based on sets of symptoms in individuals presenting to health services, as well as demographic variables, such as age, and travel history. The objective of this study was to determine the sensitivity and specificity of sets of symptoms used for triaging individuals for confirmatory testing, with the aim of optimising public health decision making under different scenarios. METHODS: Data from the first wave of COVID-19 in New Zealand were analysed; comprising 1153 PCR-confirmed and 4750 symptomatic PCR negative individuals. Data were analysed using Multiple Correspondence Analysis (MCA), automated search algorithms, Bayesian Latent Class Analysis, Decision Tree Analysis and Random Forest (RF) machine learning. RESULTS: Clinical criteria used to guide who should be tested by PCR were based on a set of mostly respiratory symptoms: a new or worsening cough, sore throat, shortness of breath, coryza, anosmia, with or without fever. This set has relatively high sensitivity (> 90%) but low specificity (< 10%), using PCR as a quasi-gold standard. In contrast, a group of mostly non-respiratory symptoms, including weakness, muscle pain, joint pain, headache, anosmia and ageusia, explained more variance in the MCA and were associated with higher specificity, at the cost of reduced sensitivity. Using RF models, the incorporation of 15 common symptoms, age, sex and prioritised ethnicity provided algorithms that were both sensitive and specific (> 85% for both) for predicting PCR outcomes. CONCLUSIONS: If predominantly respiratory symptoms are used for test-triaging, a large proportion of the individuals being tested may not have COVID-19. This could overwhelm testing capacity and hinder attempts to trace and eliminate infection. Specificity can be increased using alternative rules based on sets of symptoms informed by multivariate analysis and automated search algorithms, albeit at the cost of sensitivity. Both sensitivity and specificity can be improved through machine learning algorithms, incorporating symptom and demographic data, and hence may provide an alternative approach to test-triaging that can be optimised according to prevailing conditions.