Yulia R. Gel

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BACKGROUND We developed a practical influenza forecast model based on real-time, geographically focused, and easy to access data, designed to provide individual medical centers with advanced warning of the expected number of influenza cases, thus allowing for sufficient time to implement interventions. Secondly, we evaluated the effects of incorporating a(More)
Probabilistic weather forecasting consists of finding a joint probability distribution for future weather quantities or events. It is typically done by using a numerical weather prediction model, perturbing the inputs to the model in various ways, often depending on data assimilation, and running the model for each perturbed set of inputs. The result is(More)
In this paper we discuss the SIMID tool for simulation of the spread of infectious disease, enabling spatio-temporal visualization of the dynamics of influenza outbreaks. SIMID is based on modern random network methodology and implemented within the R and GIS frameworks. The key advantage of SIMID is that it allows not only for the construction of a(More)
Over the last decade there has been a marked interest to a Laplace distribution and its properties and generalizations, especially in a framework of financial applications. Such an interest has led to a revision and discussion of available goodness-of-fit procedures for a Laplace distribution. Indeed, since most of the studies which employ the Laplace(More)
BACKGROUND The objective of this study is to investigate predictive utility of online social media and web search queries, particularly, Google search data, to forecast new cases of influenza-like-illness (ILI) in general outpatient clinics (GOPC) in Hong Kong. To mitigate the impact of sensitivity to self-excitement (i.e., fickle media interest) and other(More)
In many applications, the underlying scientific question concerns whether the variances of k samples are equal. There are a substantial number of tests for this problem. Many of them rely on the assumption of normality and are not robust to its violation. In 1960 Professor Howard Levene proposed a new approach to this problem by applying the F -test to the(More)
Many statistical procedures rely on the assumption that the observed data are normally distributed. Consequently, there exists a vast literature on tests of normality and their statistical properties. Today the most commonly used omnibus test for general use is the Shapiro–Wilk method while the Jarque–Bera test is the most popular omnibus test in economics(More)
We propose a new method of nonparametric bootstrap to quantify estimation uncertainties in large and possibly sparse random networks. The method is tailored for inference on functions of network degree distribution, under the assumption that both network degree distribution and network order are unknown. The key idea is based on adaptation of the “blocking”(More)
Two methods for objective grid-based bias removal in mesoscale numerical weather prediction models are proposed, one global and one local. The global method is an elaboration of model output statistics (MOS), combining several modern methods for multiple regression: alternating conditional expectation (ACE), regression trees, and Bayesian model selection.(More)