Statistical Methods for Studying Infectious disease

描述（由申请人提供）：我们的长期目标是开发一个 流行病和病原体重新出现的预测科学（理解）， 研究引起人类传染病的病原微生物的进化， 疾病，包括流行病。具体目标包括：（1）发展人口 遗传学理论处理纵向样本。我们将研究统计 各种汇总统计的属性，并开发模拟方法 群体样本，并计算分离模式的概率 网站. (2)发展和评估统计方法，以估计重要的 人口参数。我们将开发统计方法来估计 群体参数，如突变率、有效群体大小、生长 速率、世代时间和突变的年龄。(3)制定统计 检验假设的方法。我们将开发统计方法来检验 对于理解进化机制至关重要的假设， 包括中性突变、恒定有效群体等假设 两个种群之间没有遗传分化。(4)分析 A组链球菌流行的分子数据。我们会分析， 使用现有的和新开发的统计方法，多态性 关于一种称为链球菌补体抑制剂（原文如此）的基因的数据， 高度多态性和sic变体与GAS流行有关。(5)到 发展一套方便使用的电脑程式，以分析纵向样本 采用新开发的统计方法。

DESCRIPTION (provided by applicant): Our long-term goal is to develop a predictive science (understandings) of epidemics and pathogen re-emergence and to study the evolution of pathogenic microbes which cause human infectious disease including epidemics. Specific aims include (1) To develop population genetics theory for treatinglongitudinal samples.We will study the statistical properties of various summary statistics and develop methods to simulate population samples and to compute the probability of a pattern of segregating sites. (2) To develop and evaluate statistical methods for estimating important population parameters. We will develop statistical methods to estimate population parameters such as mutation rate, effective population size, growth rate, generation time and the age of a mutation. (3) To develop statistical methods fortesting hypotheses. We will develop statistical methods to test hypotheses that are essential for understanding the mechanism of evolution, including the hypotheses of neutral mutations, constant effective population size, and no genetic differentiation between two populations. (4) To analyze molecular data from the epidemics of Group A streptococcus. We will analyze, using both existing and newly developed statistical methods, the polymorphism data on a gene called Streptococcal inhibitor of complement (sic), which is highly polymorphic and sic variants are associated with GAS epidemics. (5) To develop a user-friendly computer package for analyzing longitudinal samples incorporating newly developed statistical methods.