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转变， 传统传染病管理的改善，同时性行为激增 传染性感染和结核病流行，以及慢性非传染性 疾病有了以下具体目标，这个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的性传播疾病。 (1)为了测量和了解受性行为影响的人口的重要组成系统， 传播感染通过实证调查的假设，涉及核心群体，并发症， 性关系和移徙对性传播感染的传播和蔓延的影响。 (2)修改，增强和建立数学/计算模型，以表示和调查 受性传播感染影响的人群：1）继续采用贝叶斯融合方法 这就说明了艾滋病规划署不分年龄估计所用模型投入和产出的不确定性 和预测包（EPP）模型; 2）适应和实施类似的方法， 传播感染使年龄特异性队列组成预测模型; 3）改善我现有的 性传播感染使能的微模拟器，通过：a）增加新的模块来处理社会、性 和移民网络，B）增加基于贝叶斯融合的新程序，以i）考虑不确定性，ii） 对输出设置合理的限制，iii）为输出生成预测分布，以及iv）提供标准， 可重复的方法来校准模拟器。 (3)模拟受性传播感染影响的人群，以了解和预测 干预的效果。传染病流行有三个最直接的决定因素， 传输概率f？，接触结构c和感染的持续时间 关系式R 0 = f3 <$c <$d，表示一个病例产生的二次病例数。模拟器将用于 探讨这些因素与性传播感染流行动态之间的关系。 通过这一进程获得的见解将用于模拟可能的真实的干预措施并确定其优先次序。 我有这种类型的调查经验和一些必要的技能，以解决这些具体的 目标。这个应用程序的职业发展部分是为了扩大我的最低限度的知识 和技能，在三个具体领域是必要的，以解决这些目标：1）社会网络理论和 建模方法，2）数理统计，特别是贝叶斯统计，以及3）现代最新 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.