Predicting Impacts of Infectious Disease on Structure and Dynamics of Populations

非洲正在经历一场戏剧性的流行病学转变，由持续不断的 改善对传统传染病的管理，同时发生性行为爆炸 传播感染和结核病流行，慢性非传染性疾病流行率迅速上升 疾病。有以下具体目标，这份职业发展申请符合我的长期目标 有助于了解并最终控制非洲流行的性传播感染。 (1)测量和了解受性行为影响的人口的重要组成部分 通过对与核心群体相关的假设的实证调查传播感染，并发 性关系和移民导致性传播感染的传播和传播。 (2)修改、加强和建立数学/计算模型以表示和研究 受性传播感染影响的人群：1)继续采用贝叶斯融合方法 这解释了与联合国艾滋病规划署的非特定年龄估计相配合的模型输入和输出的不确定性 和投影包(EPP)模型；2)适应和实施类似的方法来处理性行为 启用传播感染的年龄段队列成分预测模型；3)改善我现有的 通过以下方式启用性传播感染的微型模拟器：a)添加新的模块来处理社交、性 和移民网络，b)将基于贝叶斯融合的新程序添加到i)考虑不确定性，ii) 对产出设置合理的限制，iii)产生产出的预测分布，以及iv)提供标准， 模拟器的校准方法可重复性好。 (3)模拟性传播感染人群，了解和预测总体情况 干预措施的效果。传染病流行有三个直接决定因素，即 传播概率/？、接触结构和感染持续时间 一个案例产生的次要案例数的关系式r0=f3？c？d。将使用模拟器 探讨这些因素与性传播感染流行动态的关系。 通过这一过程获得的洞察力将被用来模拟和确定可能的实际干预措施的优先顺序。 我有这类调查的经验和解决这些具体问题所需的一些技能 目标。此应用程序的职业发展部分旨在扩展我最基本的知识 和三个特定领域的技能，这是实现这些目标所必需的：1)社会网络理论和 建模方法，2)特别是数理统计和贝叶斯统计，以及3)现代最新 具备软件算法设计和计算机编程技能。

Africa is experiencing a dramatic epidemiological transition driven in opposite directions by continuing improvements in the management of traditional infectious diseases, concurrent exploding sexually transmitted infection and TB epidemics, and swift increases in the prevalence of chronic non-communicable diseases. With the following specific aims, this career development application fits into my long-term goal to contribute to understanding and eventually controlling pandemic sexually transmitted infections inAfrica. (1) To measure and understand the important component systems of a population affected by sexually transmitted infections through empirical investigation of hypotheses that relate core groups, concurrency in sexual relationships and migration to the transmission and spread of sexually transmitted infections. (2) To modify, enhance and build mathematical/computational models to represent and investigate populations affected by sexually transmitted infections by: 1) continuing to adapt Bayesian melding methods that account for uncertainty in model inputs and outputs to work with UNAIDS's non-age-specific estimation and projections package (EPP) model; 2) to adapt and implement similar methods to work with a sexually transmitted infection-enabled age-specific cohort component projection model; 3) to improve my existing sexually transmitted infection-enabled microsimulator by: a) adding new modules to handle social, sexual and migrant networks, b) adding new procedures based on Bayesian melding to i) account for uncertainty, ii) put reasonable limits on outputs, iii)produce predictive distributions for outputs, and iv) provide a standard, reproducible method to calibrate the simulator. (3) To simulate populations affected by sexually transmitted infections to understand and predict the overall effects of interventions. There are three proximate determinants of an infectious disease epidemic, the transmission probability/?, the contact structurec, and the duration of infectiousnessdsuggested by the relationship R0 = f3¿ c ¿d for the number of secondary cases produced by a case. The simulator will be used to explore the relationships between these and the dynamics of sexually transmitted infection epidemics. Insight gained through this process will be used to simulate and prioritize possible real interventions. I have experience with this type of investigation and some of the skills necessary to address these specific aims. The career development component of this application is designed to expand my minimal knowledge and skills in three specific areas that are necessary to address these aims: 1) social network theory and modeling methods, 2) mathematical statistics and Bayesian statistics in particular, and 3) modem up-to-date software algorithm design and computer programming skills.