Epidemic modeling framework for complex, multi-species disease processes

尽管在至少四分之三的世纪里，人们已经了解了多种传染病的垂直传播，但我们不知道垂直传播如何影响传统媒介传播感染的基本生殖数（R0）。此外，多物种疾病很可能通过垂直和水平传播持续存在，并且对它们对R0的集体影响了解不够。它已被证明，垂直传播保持地方性犬VL在美国猎犬。我们使用这个独特的队列来衡量垂直传播的VL感染能力。通过本研究获得的理解，我们将能够解释垂直传输和水平传输如何分别影响R0，我们将量化它们对R0的交互作用。拟议的研究引入了新的统计和计算方法，以改善R0的估计。具体而言，我们建议开发灵活的时空统计模型贝叶斯层次框架内，以评估致病生物，其载体，和他们的主机之间的相互作用。这项工作被提议在VL设置中开发，并且被设计为专门激励改进巴西的干预工作，但是对于更普遍地理解复杂的传输过程如何影响R0的估计具有更广泛的适用性。以前的模型VL传播动力学没有考虑到犬垂直传播或人类水库。两种或多种病原体的共感染改变了宿主对每种病原体的免疫力。这已被假定为对多重感染是正确的，但疾病的传播性还没有建立模型的基础上观察到的合并症和传播性。我们的目标是找到将VL R0降低到<1.0的方法。我们假设，一个综合的统计模型，允许分层考虑在犬和人类宿主的传播，垂直加媒介传播，是一个变革性的工具，以了解垂直和媒介传播的疾病的相互作用。最后，我们将确定疾病传播如何受到不同环境和共病的影响，并比较关键传播动力学对病原体维持的作用，例如改变R0。

Despite knowledge of vertical transmission for multiple infectious diseases for at least three-quarters of a century, we do not know how vertical transmission impacts the basic reproductive number (R0) of classically vector-borne infections. In addition, multi-species diseases are likely to persist through both vertical and horizontal transmission, and not enough is known about their collective impact on R0. It has been demonstrated that vertical transmission maintains endemic canine VL within US hunting hounds. We use this unique cohort to measure the infective capacity of vertical transmission in VL. With understanding gained from this study, we will be able to interpret how vertical transmission and horizontal transmission impact R0 separately, and we will quantify their interactive effect on R0. The proposed research introduces new statistical and computational methods for improved estimation of R0. Specifically, we propose to develop flexible spatio-temporal statistical models within the Bayesian hierarchical framework to evaluate the interactions between disease-causing organisms, their vectors, and their hosts. This work is proposed to be developed in the VL setting, and is designed to specifically motivate improved intervention efforts in Brazil, but to have wider applicability for a more general understanding of how complex transmission processes impact the estimation of R0. Previous models of VL transmission dynamics have not accounted for canine vertical transmission or a human reservoir. Coinfection with two or more pathogens modifies host immunity to each. This has been presumed true for multiple infections, but disease transmissibility has not been modeled based on the observation of comorbidity and transmissibility. Our goal is to find ways to decrease VL R0 to <1.0. We hypothesize that an integrative statistical model allowing hierarchical consideration of transmission in canine and human hosts, and vertical plus vector transmission, is a transformative tool to understand the interplay of vertical and vector borne disease. Finally, we will determine how disease transmission is influenced by different environments and comorbidities and compare roles of key transmission dynamics on pathogen maintenance, e.g. alter R0.