Model-based assessment of sampling protocols for infectious disease genomic surveillance
Sebastian Contreras, Karen Y. Oróstica, Anamaria Daza-Sanchez, Joel Wagner, Philipp Dönges, David Medina-Ortiz, Matias Jara, Ricardo Verdugo, Carlos Conca, Viol… 展开作者
Sebastian Contreras, Karen Y. Oróstica, Anamaria Daza-Sanchez, Joel Wagner, Philipp Dönges, David Medina-Ortiz, Matias Jara, Ricardo Verdugo, Carlos Conca, Viola Priesemann, Álvaro Olivera-Nappa
Genomic surveillance of infectious diseases allows monitoring circulating and emerging variants and quantifying their epidemic potential. However, due to the high costs associated with genomic sequencing, only a limited number of samples can be analysed. Thus, it is critical to understand how sampling impacts the information generated. Here, we combine a compartmental model for the spread of COVID-19 (distinguishing several SARS-CoV-2 variants) with different sampling strategies to assess their impact on genomic surveillance. In particular, we compare adaptive sampling, i.e., dynamically reallocating resources between screening at points of entry and inside communities, and constant sampling, i.e., assigning fixed resources to the two locations. We show that adaptive sampling uncovers new variants up to five weeks earlier than constant sampling, significantly reducing detection delays and estimation errors. This advantage is most prominent at low sequencing rates. Although increasing the sequencing rate has a similar effect, the marginal benefits of doing so may not always justify the associated costs. Consequently, it is convenient for countries with comparatively few resources to operate at lower sequencing rates, thereby profiting the most from adaptive sampling. Finally, our methodology can be readily adapted to study undersampling in other dynamical systems.
Using runs of homozygosity to detect genomic regions associated with susceptibility to infectious and metabolic diseases in dairy cows under intensive farming conditions
纯合性片段（ROH）是纯合基因组的连续片段，其可能反映来自共同祖先的传递，并且可用于追踪感兴趣的单倍型的遗传。本文从50 K SNPs中提取ROH，并用于检测468头荷斯坦-弗里斯兰奶牛群体中与疾病易感性相关的基因组区域。诊断的疾病分为传染病、代谢综合征、乳腺炎、生殖疾病和运动障碍。在BTA 12中发现了与感染性疾病、乳腺炎和运动障碍相关的ROH。在BTA 15上检测到与代谢综合征、传染病和生殖疾病相关的长纯合性区域，揭示了免疫、代谢和功能障碍之间的复杂关系。ROH与感染性疾病、生殖系统疾病、乳腺炎和代谢综合征有关，分布在3、5、7、13和18号染色体上。先前的研究报告了所有这些区域的产奶性状的QTL，从而证实了已知的产奶选择和奶牛健康之间的负相关关系。
Bayesian monitoring of emerging infectious diseases
我们定义数据分析来监测R的变化，R是由典型感染个体引起的继发病例的平均数量。输入数据集由划分为爆发的事件案例组成，每个事件案例都从单个索引案例开始。根据贝叶斯范式，我们将输入数据集分成两个连续的子集，以评估两个连续的R值。我们在具有两个不同R值的模型和具有单个R值的模型之间使用贝叶斯因子来证明R的变化具有统计学显著性。我们使用模拟数据验证了我们的方法，这些数据是使用已知的R。特别是，我们发现，声称两个不同的R值可能会显着取决于爆发的数量。然后，我们重新分析了Jansen et al. [Jansen et al. Science 301（5634），804]先前研究的关于1995-2002年期间英国麻疹有效繁殖数的数据。我们的分析表明，1995-2002年的数据集应分为两个独立的子集为1995-1998年和1999-2002年期间。相反，Jansen等人将这个分裂点作为他们分析的输入。我们估计的有效繁殖数R与Jansen等人发现的结果一致。总之，我们的方法用于检测R的时间变化，使用爆发大小的数据与模拟和真实世界的数据都令人满意。该方法可用于实时更新R，因为监测爆发数据变得可用。
Generative diffusion models for spatiotemporal influenza forecasting
Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill（流行病学系，全球公共卫生学院，北卡罗来纳大学查佩尔希尔分校）；Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA（流行病学系，约翰·霍普金斯伯恩斯坦公共卫生学院，巴尔的摩，MD 21205, USA）；Carolina Population Center, University of North Carolina at Chapel Hill（卡罗来纳人口中心，北卡罗来纳大学查佩尔希尔分校）
Biocomplexity Institute, University of Virginia（生物复杂性研究所，弗吉尼亚大学）；Johns Hopkins University（约翰霍普金斯大学）；Biocomplexity Institute and Department of Computer Science, University of Virginia（生物复杂性研究所与计算机科学系，弗吉尼亚大学）；University of Pittsburgh（匹兹堡大学）
