Department of Biostatistics, The University of Iowa College of Public Health, Iowa City, Iowa, USA.
Department of Epidemiology, The University of Iowa College of Public Health, Iowa City, Iowa, USA.
Stat Med. 2023 Sep 20;42(21):3860-3876. doi: 10.1002/sim.9837. Epub 2023 Jun 23.
While many Bayesian state-space models for infectious disease processes focus on population infection dynamics (eg, compartmental models), in this work we examine the evolution of infection processes and the complexities of the immune responses within the host using these techniques. We present a joint Bayesian state-space model to better understand how the immune system contributes to the control of Leishmania infantum infections over the disease course. We use longitudinal molecular diagnostic and clinical data of a cohort of dogs to describe population progression rates and present evidence for important drivers of clinical disease. Among these results, we find evidence for the importance of co-infection in disease progression. We also show that as dogs progress through the infection, parasite load is influenced by their age, ectoparasiticide treatment status, and serology. Furthermore, we present evidence that pathogen load information from an earlier point in time influences its future value and that the size of this effect varies depending on the clinical stage of the dog. In addition to characterizing the processes driving disease progression, we predict individual and aggregate patterns of Canine Leishmaniasis progression. Both our findings and the application to individual-level predictions are of direct clinical relevance, presenting possible opportunities for application in veterinary practice and motivating lines of additional investigation to better understand and predict disease progression. Finally, as an important zoonotic human pathogen, these results may support future efforts to prevent and treat human Leishmaniosis.
虽然许多用于传染病过程的贝叶斯状态空间模型都集中在人群感染动力学上（例如，房室模型），但在这项工作中，我们使用这些技术来研究感染过程的演变和宿主中免疫反应的复杂性。我们提出了一个联合贝叶斯状态空间模型，以更好地了解免疫系统如何在疾病过程中控制利什曼原虫感染。我们使用一组狗的纵向分子诊断和临床数据来描述人群进展率，并提供临床疾病的重要驱动因素的证据。在这些结果中，我们发现合并感染在疾病进展中的重要性的证据。我们还表明，随着狗感染的进展，寄生虫负荷受其年龄、外寄生虫治疗状况和血清学的影响。此外，我们提供的证据表明，更早时间点的病原体负荷信息会影响其未来价值，并且这种影响的大小取决于狗的临床阶段。除了描述驱动疾病进展的过程外，我们还预测了犬利什曼病进展的个体和总体模式。我们的发现和对个体水平预测的应用都具有直接的临床相关性，为在兽医实践中的应用提供了可能的机会，并促使进一步开展研究以更好地理解和预测疾病进展。最后，作为一种重要的人兽共患人类病原体，这些结果可能支持未来预防和治疗人类利什曼病的努力。
