Jones Geoff, Heuer Cord, Johnson Wes, Begg Douglas, McFadden Andrew, Sutar Ashish, Abila Ronello, Browning Clare, Wilsden Ginette, Ludi Anna B, Khounsy Syseng, Subharat Supatsak
School of Mathematical and Computational Sciences, Massey University, Palmerston North, New Zealand.
EpiCentre, School of Veterinary Science, Massey University, Palmerston North, New Zealand.
Prev Vet Med. 2023 May;214:105889. doi: 10.1016/j.prevetmed.2023.105889. Epub 2023 Mar 7.
Controlling foot-and-mouth disease (FMD) by vaccination requires adequate population coverage and high vaccine efficacy under field conditions. To assure veterinary services that animals have acquired sufficient immunity, strategic post-vaccination surveys can be conducted to monitor the coverage and performance of the vaccine. Correct interpretation of these serological data and an ability to derive exact prevalence estimates of antibody responses requires an awareness of the performance of serological tests. Here, we used Bayesian latent class analysis to evaluate the diagnostic sensitivity and specificity of four tests. A non-structural protein (NSP) ELISA determines vaccine independent antibodies from environmental exposure to FMD virus (FMDV), and three assays measuring total antibodies derived from vaccine antigen or environmental exposure to two serotypes (A, O): the virus neutralisation test (VNT), a solid phase competitive ELISA (SPCE), and a liquid phase blocking ELISA (LPBE). Sera (n = 461) were collected by a strategic post-vaccination monitoring survey in two provinces of Southern Lao People's Democratic Republic (PDR) after a vaccination campaign in early 2017. Not all samples were tested by every assay and each serotype: VNT tested for serotype A and O, whereas SPCE and LPBE tested for serotype O, and only NSP-negative samples were tested by VNT, with 90 of them not tested (missing by study design). These data challenges required informed priors (based on expert opinion) for mitigating possible lack of model identifiability. The vaccination status of each animal, its environmental exposure to FMDV, and the indicator of successful vaccination were treated as latent (unobserved) variables. Posterior median for sensitivity and specificity of all tests were in the range of 92-99 %, except for the sensitivity of NSP (∼66%) and the specificity of LPBE (∼71 %). There was strong evidence that SPCE outperformed LPBE. In addition, the proportion of animals recorded as having been vaccinated that showed a serological immune response was estimated to be in the range of 67-86 %. The Bayesian latent class modelling framework can easily and appropriately impute missing data. It is important to use field study data as diagnostic tests are likely to perform differently on field survey samples compared to samples obtained under controlled conditions.
通过疫苗接种控制口蹄疫(FMD)需要在田间条件下实现足够的群体覆盖率和高疫苗效力。为了向兽医服务机构保证动物已获得足够的免疫力,可以开展战略性的疫苗接种后调查,以监测疫苗的覆盖率和效果。要正确解读这些血清学数据并能够得出抗体反应的确切流行率估计值,就需要了解血清学检测的性能。在此,我们使用贝叶斯潜在类别分析来评估四种检测的诊断敏感性和特异性。一种非结构蛋白(NSP)ELISA用于测定因环境接触口蹄疫病毒(FMDV)而产生的与疫苗无关的抗体,还有三种检测用于测量源自疫苗抗原或环境接触两种血清型(A、O)的总抗体:病毒中和试验(VNT)、固相竞争ELISA(SPCE)和液相阻断ELISA(LPBE)。在老挝人民民主共和国南部两个省份于2017年初开展疫苗接种运动后,通过战略性疫苗接种后监测调查收集了血清样本(n = 461)。并非所有样本都经过每种检测和每种血清型的检测:VNT检测血清型A和O,而SPCE和LPBE检测血清型O,并且只有NSP阴性样本通过VNT检测,其中有90个未检测(因研究设计缺失)。这些数据挑战需要有根据的先验信息(基于专家意见)来减轻可能出现的模型不可识别性问题。每只动物的疫苗接种状态、其环境接触FMDV的情况以及成功接种的指标均被视为潜在(未观察到)变量。除了NSP的敏感性(约66%)和LPBE的特异性(约71%)外,所有检测的敏感性和特异性的后验中位数在92 - 99%的范围内。有充分证据表明SPCE的性能优于LPBE。此外,记录为已接种疫苗且显示血清学免疫反应的动物比例估计在67 - 86%的范围内。贝叶斯潜在类别建模框架可以轻松且适当地填补缺失数据。使用现场研究数据很重要,因为与在受控条件下获得的样本相比,诊断检测在现场调查样本上的表现可能会有所不同。