Person: Gurmu, Yared
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Gurmu
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Yared
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Gurmu, Yared
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Publication Predicting Subnational Ebola Virus Disease Epidemic Dynamics from Sociodemographic Indicators(Public Library of Science (PLoS), 2016) Valeri, Linda; Patterson-Lomba, Oscar; Gurmu, Yared; Ablorh, Akweley; Bobb, Jennifer; Townes, Will; Harling, GuyBackground The recent Ebola virus disease (EVD) outbreak in West Africa has spread wider than any previous human EVD epidemic. While individual-level risk factors that contribute to the spread of EVD have been studied, the population-level attributes of subnational regions associated with outbreak severity have not yet been considered. Methods To investigate the area-level predictors of EVD dynamics, we integrated time series data on cumulative reported cases of EVD from the World Health Organization and covariate data from the Demographic and Health Surveys. We first estimated the early growth rates of epidemics in each second-level administrative district (ADM2) in Guinea, Sierra Leone and Liberia using exponential, logistic and polynomial growth models. We then evaluated how these growth rates, as well as epidemic size within ADM2s, were ecologically associated with several demographic and socio-economic characteristics of the ADM2, using bivariate correlations and multivariable regression models. Results The polynomial growth model appeared to best fit the ADM2 epidemic curves, displaying the lowest residual standard error. Each outcome was associated with various regional characteristics in bivariate models, however in stepwise multivariable models only mean education levels were consistently associated with a worse local epidemic. Discussion By combining two common methods—estimation of epidemic parameters using mathematical models, and estimation of associations using ecological regression models—we identified some factors predicting rapid and severe EVD epidemics in West African subnational regions. While care should be taken interpreting such results as anything more than correlational, we suggest that our approach of using data sources that were publicly available in advance of the epidemic or in real-time provides an analytic framework that may assist countries in understanding the dynamics of future outbreaks as they occur.Publication Modeling and Estimation of Patterns of Relationship Formation and Dissolution(2016-05-19) Gurmu, Yared; De Gruttola, Victor; Betensky, Rebecca; Trippa, Lorenzo; Qian, JingThis dissertation describes and develops methods for modeling sexual partnership formation and termination using retrospectively collected survey data. Such methods are required to produce information necessary to model propagation of sexually transmitted diseases and the impact of interventions on such processes which are being used both to design and to monitor HIV combination prevention studies. Sexual history data are commonly obtained through surveys that collect information on relationships that are ongoing during a fixed time window. This sampling mechanism leads to incomplete sexual history data and duration data that are left truncated and right censored. In Chapter 1, we describe a common sampling scheme for collecting sexual partnership data, discuss a key assumption required for unbiased estimation, and provide the conditions under which the nonparametric maximum likelihood estimator of the relationship duration distribution is unique and consistent. We also investigate the conditions required for the consistency of the regression coefficient from a Cox proportional hazards model that apply even when the distribution of duration is not completely identifiable due to restrictions on the support of the truncation distribution. Lastly, we will provide some illustrative examples on estimating distribution of most recent partnerships and present spline regression results based on sexual history data from Botswana. In Chapter 2, we present a Markov framework for modeling and estimation of partnership transition probabilities for sexual history data collected under a retrospective sampling scheme. We propose a stochastic expectation maximization algorithm (stEM) coupled with rejection-sampling scheme in order to estimate transition probabilities from a state of celibacy to monogamy and to concurrency (or vice versa). This approach accommodates the retrospective sampling scheme from which sexual partnership data is obtained and utilizes all available information from the sexual history data. In particular, this paper will address maximum likelihood estimation via stEM when our observed data includes information on the number of certain types of transitions without specifying the sojourn time in the states. For example, with regards to partnership data, the total life time number of partnerships (or number of partnerships within a fixed window of time) may be known even though the sojourn time of each of the partnerships in the different states may not be known. In the process of estimating transition rates, we incorporate such information by using rejection sampling. Simulation results showing the performance of the stEM will be presented. We also provide an application example based on partnership data collected from South Africa. In Chapter 3, we extend the Markov model presented in Chapter 2 so that the sexual history process can be fully characterized. This approach combines a Markov model and a logistic regression framework. The Markov model states we consider include celibacy, monogamy and concurrency; the logistic regression model classifies the pattern of concurrency, which can be either transitional (older partnership ends first) or embedded (new ends first). By using both types of models we can fully characterize the processes of interest. Estimation of model parameters is based on a stochastic expectation maximization algorithm (stEM) coupled with rejection-sampling scheme. Strategies based on statistics that arise naturally from the estimation procedure itself stEM are used to validate model assumptions. The method is illustrated using sexual history data collected from South Africa. Simulation results are used to demonstrate properties of the estimation methods.