Publications

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Traditional multiple imputation approaches may perform poorly for datasets with high rates of missingness unless many m imputations are used. This paper implements an alternative machine learning-based approach to imputing data that are missing at high rates. Here, we use boosting to create a strong learner from a weak learner fitted to a dataset missing many observations. This approach may be applied to a variety of types of learners (models). The approach is demonstrated by application to a spatiotemporal dataset for predicting dengue outbreaks in India from meteorological covariates. A Bayesian spatiotemporal CAR model is boosted to produce imputations, and the overall RMSE from a k-fold cross-validation is used to assess imputation accuracy.

The transformation of the distribution grid from a centralized to decentralized architecture, with bi-directional power and data flows, is made possible by a surge in network intelligence and grid automation. While changes are largely beneficial, the interface between grid operator and automated technologies is not well understood, nor are the benefits and risks of automation. Quantifying and understanding the latter is an important facet of grid resilience that needs to be fully investigated. The work described in this document represents the first empirical study aimed at identifying and mitigating the vulnerabilities posed by automation for a grid that for the foreseeable future will remain a human-in-the-loop critical infrastructure. Our scenario-based methodology enabled us to conduct a series of experimental studies to identify causal relationships between grid-operator performance and automated technologies and to collect measurements of human performance as a function of automation. Our findings, though preliminary, suggest there are predictive patterns in the interplay between human operators and automation, patterns that can inform the rollout of distribution automation and the hiring and training of operators, and contribute in multiple and significant ways to the field of grid resilience.

Open-source indicators have been proposed as a way of tracking and forecasting disease outbreaks. Some, such are meteorological data, are readily available as reanalysis products. Others, such as those derived from our online behavior (web searches, media article etc.) are gathered easily and are more timely than public health reporting. In this study we investigate how these datastreams may be combined to provide useful epidemiological information. The investigation is performed by building data assimilation systems to track influenza in California and dengue in India. The first does not suffer from incomplete data and was chosen to explore disease modeling needs. The second explores the case when observational data is sparse and disease modeling complexities are beside the point. The two test cases are for opposite ends of the disease tracking spectrum. We find that data assimilation systems that produce disease activity maps can be constructed. Further, being able to combine multiple open-source datastreams is a necessity as any one individually is not very informative. The data assimilation systems have very little in common except that they contain disease models, calibration algorithms and some ability to impute missing data. Thus while the data assimilation systems share the goal for accurate forecasting, they are practically designed to compensate for the shortcomings of the datastreams. Thus we expect them to be disease and location-specific.

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There is mounting evidence that alcohol use is significantly linked to lower HCV treatment response rates in interferon-based therapies, though some of the evidence is conflicting. Furthermore, although health care providers recommend reducing or abstaining from alcohol use prior to treatment, many patients do not succeed in doing so. The goal of this meta-analysis was to systematically review and summarize the Englishlanguage literature up through January 30, 2015 regarding the relationship between alcohol use and HCV treatment outcomes, among patients who were not required to abstain from alcohol use in order to receive treatment. Seven pertinent articles studying 1,751 HCV-infected patients were identified. Log-ORs of HCV treatment response for heavy alcohol use and light alcohol use were calculated and compared. We employed a hierarchical Bayesian meta-analytic model to accommodate the small sample size. The summary estimate for the log-OR of HCV treatment response was -0.775 with a 95% credible interval of (-1.397, -0.236). The results of the Bayesian meta-analysis are slightly more conservative compared to those obtained from a boot-strapped, random effects model. We found evidence of heterogeneity (Q = 14.489, p = 0.025), accounting for 60.28% of the variation among log-ORs. Meta-regression to capture the sources of this heterogeneity did not identify any of the covariates investigated as significant. This meta-analysis confirms that heavy alcohol use is associated with decreased HCV treatment response compared to lighter levels of alcohol use. Further research is required to characterize the mechanism by which alcohol use affects HCV treatment response.

Abstract not provided.