Epidemic modeling framework for complex, multi-species disease processes

尽管对多种传染病垂直传播的认识至少已有四分之三个世纪，但我们不知道垂直传播如何影响经典病媒传播感染的基本繁殖数(R0)。此外，多物种疾病很可能通过垂直和水平传播持续存在，对它们对R0的集体影响知之甚少。已经证明，垂直传播使美国猎犬体内的地方性犬VL保持流行。我们使用这个独特的队列来衡量垂直传播在VL中的感染能力。通过本研究获得的理解，我们将能够分别解释垂直传播和水平传播对R0的影响，并量化它们对R0的交互影响。建议的研究引入了新的统计和计算方法来改进R0的估计。具体地说，我们建议在贝叶斯分层框架内开发灵活的时空统计模型，以评估致病生物、它们的媒介和它们的宿主之间的相互作用。这项工作建议在VL环境下进行，旨在具体激励巴西改进干预工作，但具有更广泛的适用性，以更全面地了解复杂的传输过程如何影响R0的估计。以前的VL传播动力学模型没有考虑犬类垂直传播或人类宿主。与两种或两种以上病原体混合感染会改变宿主对每种病原体的免疫力。对于多重感染，这一假设是正确的，但疾病的传播性并不是基于对共病和传播性的观察而建模的。我们的目标是找到将VL R0降低到&lt；1.0的方法。我们假设，综合统计模型允许分层考虑犬和人类宿主之间的传播，以及垂直和媒介传播，是理解垂直和媒介传播疾病相互作用的变革性工具。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在病原体维持中的作用，例如Alter 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.