Bayesian multivariate longitudinal model for immune responses to Leishmania: A tick-borne co-infection study.
Bayesian multivariate longitudinal model for immune responses to Leishmania: A tick-borne co-infection study.
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利什曼原虫免疫反应的贝叶斯多变量纵向模型：蜱传共感染研究。
DOI：
10.1002/sim.9837
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发表时间：
2023
影响因子：
2
通讯作者：
Petersen,ChristineA
中科院分区：
文献类型：
--
作者：
Pabon-Rodriguez,FelixM;Brown,GrantD;Scorza,BreannaM;Petersen,ChristineA
虽然许多用于传染病过程的贝叶斯状态空间模型专注于群体感染动力学（例如，房室模型），但在这项工作中，我们使用这些技术研究了感染过程的演变和宿主内免疫反应的复杂性。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婴儿利什曼原虫感染。我们使用一组狗的纵向分子诊断和临床数据来描述群体进展率，并为临床疾病的重要驱动因素提供证据。在这些结果中，我们发现了合并感染在疾病进展中的重要性的证据。我们还表明，随着狗通过感染的进展，寄生虫负荷的影响，他们的年龄，杀外寄生虫药治疗状态，和血清学。此外，我们提出的证据表明，病原体负荷信息从较早的时间点影响其未来的价值，这种影响的大小取决于狗的临床阶段。除了表征疾病进展的驱动过程外，我们还预测了犬利什曼病进展的个体和总体模式。我们的研究结果和对个体水平预测的应用都具有直接的临床相关性，为兽医实践中的应用提供了可能的机会，并激发了额外的研究，以更好地了解和预测疾病进展。最后，作为一种重要的人畜共患病原体，这些结果可能支持未来的努力，以预防和治疗人类利什曼病。
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 ofLeishmania infantuminfections 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 ofCanine Leishmaniasisprogression. 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.