Modeling Epidemic Infectious Diseases Using Sequence Analysis

使用序列分析模拟流行性传染病

• 批准号: 9300928
• 负责人: Sergei L Kosakovsky Pond
• 金额: $ 57.77万
• 依托单位: TEMPLE UNIV OF THE COMMONWEALTH
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-09-05 至 2019-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9300928
• 关键词: Address; Adoption; Algorithms; Anti-Retroviral Agents; Antiviral Agents; Applied Research; Base Sequence; Biology; Bovine Tuberculosis; Clostridium difficile; Communicable Diseases; Community Health; Computer Simulation; Computer software; Computing Methodologies; Data; Data Analyses; Data Set; Databases; Development; Elements; Epidemic; Epidemiology; Event; Evolution; Exhibits; Genetic; Geography; HIV; HIV-1; HIV/HCV; Hepatitis C; Individual; Infection; Intervention; Likelihood Functions; Link; Maintenance; Methodology; Methods; Modeling; Molecular; Molecular Epidemiology; Mycobacterium bovis; Mycobacterium tuberculosis; Pathway Analysis; Phylogenetic Analysis; Play; Population; Population Genetics; Prevention; Prevention strategy; Preventive Intervention; Probability; Property; Public Health; RNA Viruses; Research; Risk; Role; Sampling; Science; Sequence Analysis; Speed; Statistical Methods; Structure; Syphilis; Target Populations; Techniques; Testing; Time; Treatment Efficacy; Validation; Viral; Virus; Work; base; clinical care; cluster computing; cohort; data modeling; deep sequencing; density; efficacy evaluation; epidemiologic data; flu; infectious disease treatment; innovation; mathematical model; methicillin resistant Staphylococcus aureus; models and simulation; next generation sequencing; parallelization; pathogen; pre-exposure prophylaxis; programs; public health relevance; rapid technique; reproductive; research and development; seasonal influenza; software development; theories; tool; transmission process; treatment strategy; vaccine efficacy

DESCRIPTION (provided by applicant): Phylogenetic analysis has played a crucial role in increasing our understanding of many aspects of viral and bacterial pathogen biology. Recent advances in evolutionary analysis of sequence data, including time- stamped data and deep sequencing, have allowed quantitative description of epidemic structure for viruses like HIV and HCV. Phylogenetic approaches applied to local datasets of viral sequences with high density coverage of the target population, both from research cohorts and routine clinical care, have been used in studies of transmission correlates, vaccine efficacy evaluation, and various aspects of public health. The non- random structure of the underlying transmission network, its role in the epidemic and its implications for treatment and prevention can now be inferred from sequence data and modeled. In this project, we will develop more innovative models of pathogen transmission combining population genetics, sequence evolution, and network theory, provide efficient method implementation and fast approximate algorithms scalable to global-scale datasets, evaluate the effect of prevention and treatment approaches on epidemic dynamics in five localized epidemics of HIV and HCV, and model generalized epidemics for these and other pathogens. By developing computational and statistical methods that incorporate and analyze pathogen sequence and other epidemiologic data, we will be able to infer and characterize transmission networks to best identify targets for the most effective and parsimonious use of prevention interventions.

描述（由申请人提供）：系统发育分析在增加我们对病毒和细菌病原体生物学许多方面的理解方面起着至关重要的作用。序列数据进化分析的最新进展，包括时间戳数据和深度测序，允许对HIV和HCV等病毒的流行病结构进行定量描述。从研究队列和常规临床护理中，应用于具有高密度覆盖的病毒序列数据集的系统发育方法已用于传播相关性研究，疫苗功效评估以及公共卫生的各个方面。现在可以从序列数据中推断出基础传输网络的非随机结构，其在流行病中的作用及其对治疗和预防的影响并进行建模。在该项目中，我们将开发出更具创新性的病原体传播模型，结合了人口遗传学，序列进化和网络理论，提供有效的方法实施和快速近似算法，可扩展到全球规模数据集，评估预防和治疗方法对HIV和HCV五个局部流行病的流行病动力学的影响，以及HCV的五个局部流行动力学，并用于HCV和Model Model Ipperiped Epperiped and Epperiped and Epperiped and Epperiped and Epperiped and Epperiped and Epteriped and Epteriped and Epteriped and Epteriped and Epteriped and Epteriped and Epteriped and＆其他这些和其他这些。通过开发纳入和分析病原体序列和其他流行病学数据的计算和统计方法，我们将能够推断和表征传输网络，以最佳地识别最有效和最有用的预防干预措施的目标。