Department of Statistics

In this paper, we propose an approach to estimating traffic matrices that incorporates lightweight Origin- Destination (OD) flow measurements coupled with a computationally lightweight algorithm for producing the OD estimates. There are two key ingredients in our method, called PamTram, for PArtial Measurement of TRAffic Matrices. The first is to actively select a small number of informative OD flows to measure in each estimation time interval. To avoid the heavy computation of an optimal selection, we use a heuristic based on intuition from game theory. Randomized selection rules are developed based on the goals of reducing errors and adapting to traffic changes. We provide an algorithm for selecting a good flow to measure that is fast because it avoids the computations, such as integrating over past intervals, that are needed for optimal selection. The second key aspect of our method is an explanation and proof that an Iterative Proportional Fitting (IPF) algorithm can be used to approximate the traffic matrix estimate when the goal is a minimum mean squared error and the optimization starts from a maximum entropy initial estimate.

In addition, we provide a one-step average error bound for PamTram when the randomized selection rule is uniform and no link counts are used. This bounds the average error for the worst case selection rule. Finally, we validate our method using data from Sprint’s European Tier-1 IP backbone network. Results show that our method generates average errors below the 10% carrier target error rate. Interestingly, we show that it suffices to measure a single OD flow in each estimation interval,which renders our partial measurement method very lightweight in terms of measurement overhead.

© 2015 Nature Publishing Group, a division of Macmillan Publishers Limited. All Rights Reserved. Despite an extensive body of reported information about peripheral and central mechanisms involved in the pathophysiology of IBS symptoms, no comprehensive disease model has emerged that would guide the development of novel, effective therapies. In this Review, we will first describe novel insights into some key components of brain–gut interactions, starting with the emerging findings of distinct functional and structural brain signatures of IBS. We will then point out emerging correlations between these brain networks and genomic, gastrointestinal, immune and gut-microbiome-related parameters. We will incorporate this new information, as well as the reported extensive literature on various peripheral mechanisms, into a systems-based disease model of IBS, and discuss the implications of such a model for improved understanding of the disorder, and for the development of more-effective treatment approaches in the future.

Advances in wearables and digital technology now make it possible to deliver behavioral mobile health interventions to individuals in their everyday life. The micro-randomized trial (MRT) is increasingly used to provide data to inform the construction of these interventions. In an MRT, each individual is repeatedly randomized among multiple intervention options, often hundreds or even thousands of times, over the course of the trial. This work is motivated by multiple MRTs that have been conducted, or are currently in the field, in which the primary outcome is a longitudinal binary outcome. The primary aim of such MRTs is to examine whether a particular time-varying intervention has an effect on the longitudinal binary outcome, often marginally over all but a small subset of the individual's data. We propose the definition of causal excursion effect that can be used in such primary aim analysis for MRTs with binary outcomes. Under rather restrictive assumptions one can, based on existing literature, derive a semiparametric, locally efficient estimator of the causal effect. We, starting from this estimator, develop an estimator that can be used as the basis of a primary aim analysis under more plausible assumptions. Simulation studies are conducted to compare the estimators. We illustrate the developed methods using data from the MRT, BariFit. In BariFit, the goal is to support weight maintenance for individuals who received bariatric surgery.

In the course of malaria elimination in the Greater Mekong Subregion (GMS), malaria epidemiology has experienced drastic spatiotemporal changes with residual transmission concentrated along international borders and the rising predominance of Plasmodium vivax. The emergence of Plasmodium falciparum parasites resistant to artemisinin and partner drugs renders artemisinin-based combination therapies less effective while the potential spread of multidrug-resistant parasites elicits concern. Vector behavioral changes and insecticide resistance have reduced the effectiveness of core vector control measures. In recognition of these problems, the Southeast Asian International Center of Excellence for Malaria Research (ICEMR) has been conducting multidisciplinary research to determine how human migration, antimalarial drug resistance, vector behavior, and insecticide resistance sustain malaria transmission at international borders. These efforts allow us to comprehensively understand the ecology of border malaria transmission and develop population genomics tools to identify and track parasite introduction. In addition to employing in vivo, in vitro, and molecular approaches to monitor the emergence and spread of drug-resistant parasites, we also use genomic and genetic methods to reveal novel mechanisms of antimalarial drug resistance of parasites. We also use omics and population genetics approaches to study insecticide resistance in malaria vectors and identify changes in mosquito community structure, vectorial potential, and seasonal dynamics. Collectively, the scientific findings from the ICEMR research activities offer a systematic view of the factors sustaining residual malaria transmission and identify potential solutions to these problems to accelerate malaria elimination in the GMS.