1 Given the public health, economic, and conservation implications of zoonotic diseases, their effective surveillance is of paramount importance The traditional approach to estimating pathogen prevalence as the proportion of infected individuals in the population is biased because it fails to account for imperfect detection A statistically robust way to reduce bias in prevalence estimates is to obtain repeated samples (or sample many tissues in multi-tissue disease systems) and to apply statistical methods that account for imperfect detection and permit the interdependence of the infection process across multiple tissues 2 We developed a multi-state occupancy modelling framework which considers two scenarios about the infection process, one where no assumptions about the dependencies among the tissues are made (general), and another where dependence among tissues is not permitted (constrained) 3 We applied this framework to pseudorabies virus (PrV) DNA detection data obtained from whole blood;and oral, nasal, and genital mucosa of 510 feral swine (Sus scrofa) during the years 2014-2016 in Florida, USA 4 The constrained model was better supported by data Estimated PrV prevalence varied among tissues, were higher than the naïve estimates, ranging from to 0 06 (CI: 0 02-0 14) in genital to 0 54 (CI: 0 14-0 82) in nasal tissue Probability of PrV detection ranged from 0 11 (CI: 0 06-0 18) in nasal to 0 51 (CI: 0 21-0 81) in genital tissue 5 PrV prevalence was not affected by the age or sex of the animal or the year of sampling, but prevalence increased as drought severity increased 6 The conditional probability of detecting PrV given infection in at least one tissue type within an individual was highest for nasal tissue, suggesting that nasal is the best tissue to sample for PrV surveillance if only one tissue can be sampled, at least for systems with tissue-specific prevalence and detection probabilities similar to ours 7 Synthesis and applications We focused on inferences about pathogen prevalence in multi-tissue disease systems, dealing with both nondetection and potential dependencies among tissues in infection status We found strong evidence of variation in both prevalence and detection probabilities among tissues Our results emphasize the importance of sampling multiple tissues and of applying inference methods that account for imperfect detection in the surveillance of systemic diseases The multi-state modelling framework is broadly applicable to the surveillance of pathogens that infect multiple tissues and can be used even when the infection status of the pathogen in one tissue may depend on the infection status of the pathogen in other tissue(s)

中文翻译：

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用于稳健估计多组织疾病系统中疾病流行的多状态占用建模框架

1 考虑到人畜共患疾病对公共卫生、经济和保护的影响，对其进行有效监测至关重要 传统方法以受感染个体在人口中的比例来估计病原体流行率是有偏差的，因为它没有考虑到不完善的检测 A 统计减少流行率估计偏差的有效方法是获取重复样本（或对多组织疾病系统中的许多组织进行采样）并应用统计方法来解释不完善的检测并允许跨多个组织的感染过程相互依赖 2 我们开发了一个多状态占用建模框架，它考虑了关于感染过程的两种情况，一种是不假设组织之间的依赖关系（一般），以及另一个不允许（限制）组织依赖的情况 3 我们将此框架应用于从全血中获得的伪狂犬病病毒 (PrV) DNA 检测数据；多年来，510 头野猪 (Sus scrofa) 的口腔、鼻腔和生殖器粘膜2014-2016 年在美国佛罗里达州 4 约束模型得到数据的更好支持 估计的 PrV 患病率在组织之间有所不同，高于原始估计，范围从生殖器的 0 06 (CI: 0 02-0 14) 到 0 54 ( CI: 0 14-0 82) 在鼻组织中 PrV 检测的概率范围从鼻腔的 0 11 (CI: 0 06-0 18) 到生殖器组织的 0 51 (CI: 0 21-0 81) 5 PrV 流行率不是受动物年龄或性别或采样年份的影响，但流行率随着干旱严重程度的增加而增加 6 在一个人体内至少一种组织类型中检测到感染的 PrV 的条件概率对于鼻组织来说是最高的，这表明如果只能对一种组织进行采样，那么鼻是用于 PrV 监测的最佳组织，至少对于具有与我们相似的组织特异性流行率和检测概率的系统 7 合成和应用 我们专注于对多组织疾病系统中病原体流行率的推断，处理感染状态下组织之间的未检测和潜在依赖性 我们发现了组织之间流行率和检测概率变化的有力证据 我们的结果强调了采样多个组织和应用推理方法的重要性，这些方法可以解释全身监测中的不完善检测疾病 多状态建模框架广泛适用于感染多个组织的病原体的监测，即使在一个组织中病原体的感染状态可能取决于其他组织中病原体的感染状态时，也可以使用可能取决于病原体在其他组织中的感染状态可能取决于病原体在其他组织中的感染状态