Predicting Impacts of Infectious Disease on Structure and Dynamics of Populations
Principal Investigator: Samuel J Clark
Abstract: 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[unreadable] c [unreadable]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.
Funding Period: 2008-06-15 - 2013-09-30
more information: NIH RePORT