Statistics and Algorithms in Evolution

Spouge博士开发了一些计算与进化模型相关的概率的通用策略。这些结果有助于推断单个宿主中病毒感染开始时的事件，具有潜在的应用，例如，艾滋病毒疫苗和预防性治疗的试验。特别是，这些结果有助于估计病毒在感染开始时的基本繁殖数R0（来自单个感染细胞的感染细胞的平均数量）。 因此，类似于人类遗传学的方法，潜伏感染后很好地采样的病毒序列中的突变模式可以保留关于初始R0的信息。因此，对来自病毒疗法的患者和动物试验的序列中的模式的分析可能能够指示细微的治疗进展，即使在治疗组和对照组的感染没有统计学显著差异的情况下。

Dr Spouge has developed some general strategies for calculating probabilities relevant to evolutionary models. The results help infer events at the start of viral infection in a single host, with potential applications to, e.g., trials of HIV vaccines and preventative therapies. In particular, the results help estimate the basic reproduction number R0 (the average number of infected cells that derive from a single infected cell) of a virus at the beginning of an infection. In analogy to methods in human genetics, therefore, the mutational patterns in viral sequences sampled well after latent infection can remain informative about the initial R0. The analysis of the patterns in sequences derived from patient and animal trials of viral therapies may therefore be able to indicate subtle therapeutic progress, even in the absence of statistically significant differences in the infection of treatment and control groups.