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Integrating serological and genetic data to quantify cross-species transmission: brucellosis as a case study

机译：整合血清学和遗传数据以量化跨物种传播：以布鲁氏菌病为例

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Epidemiological data are often fragmented, partial, and/or ambiguous and unable to yield the desired level of understanding of infectious disease dynamics to adequately inform control measures. Here, we show how the information contained in widely available serology data can be enhanced by integration with less common type-specific data, to improve the understanding of the transmission dynamics of complex multi-species pathogens and host communities. Using brucellosis in northern Tanzania as a case study, we developed a latent process model based on serology data obtained from the field, to reconstruct Brucella transmission dynamics. We were able to identify sheep and goats as a more likely source of human and animal infection than cattle; however, the highly cross-reactive nature of Brucella spp. meant that it was not possible to determine which Brucella species (B. abortus or B. melitensis) is responsible for human infection. We extended our model to integrate simulated serology and typing data, and show that although serology alone can identify the host source of human infection under certain restrictive conditions, the integration of even small amounts (5%) of typing data can improve understanding of complex epidemiological dynamics. We show that data integration will often be essential when more than one pathogen is present and when the distinction between exposed and infectious individuals is not clear from serology data. With increasing epidemiological complexity, serology data become less informative. However, we show how this weakness can be mitigated by integrating such data with typing data, thereby enhancing the inference from these data and improving understanding of the underlying dynamics.
机译：流行病学数据通常是零散的，部分的和/或模棱两可的，并且无法对传染病动态产生所需的了解水平，无法充分告知控制措施。在这里，我们展示了如何通过与不太常见的特定类型数据进行整合来增强广泛可用的血清学数据中包含的信息，从而增进对复杂多物种病原体和宿主群落传播动态的理解。以坦桑尼亚北部的布鲁氏菌病为案例研究，我们基于从现场获得的血清学数据开发了一个潜在过程模型，以重建布鲁氏菌传播动力学。我们能够鉴定出绵羊和山羊比牛更可能是人类和动物感染的来源。然而，布鲁氏菌属具有高度交叉反应性。这意味着无法确定哪些布鲁氏菌物种（流产布鲁氏菌或melitensis）是造成人类感染的原因。我们扩展了模型以集成模拟血清学和分型数据，并表明尽管仅血清学可以在某些限制性条件下识别人类感染的宿主源，但即使是少量（5％）的分型数据的整合也可以增进对复杂流行病学的理解动力学。我们表明，当存在多种病原体并且血清学数据无法明确暴露个体和感染个体之间的区别时，数据整合通常将是必不可少的。随着流行病学复杂性的提高，血清学数据的信息量越来越少。但是，我们展示了如何通过将此类数据与类型数据集成在一起来减轻这种弱点，从而增强从这些数据中得出的推论并增进对基本动态的理解。

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