oalib
Search Results: 1 - 10 of 100 matches for " "
All listed articles are free for downloading (OA Articles)
Page 1 /100
Display every page Item
Week 52 Influenza Forecast for the 2012-2013 U.S. Season  [PDF]
Jeffrey Shaman,Alicia Karspeck,Marc Lipsitch
Quantitative Biology , 2013,
Abstract: This document is another installment in a series of near real-time weekly influenza forecasts made during the 2012-2013 influenza season. Here we present some of the results of forecasts initiated following assimilation of observations for Week 52 (i.e. the forecast begins December 30, 2012) for municipalities in the United States. The forecasts were made on January 4, 2013. Results from forecasts initiated the five previous weeks (Weeks 47-51) are also presented.
Week 1 Influenza Forecast for the 2012-2013 U.S. Season  [PDF]
Jeffrey Shaman,Alicia Karspeck,Marc Lipsitch
Quantitative Biology , 2013,
Abstract: This is part of a series of weekly influenza forecasts made during the 2012-2013 influenza season. Here we present results of forecasts initiated following assimilation of observations for Week 1 (i.e. the forecast begins January 6, 2013) for municipalities in the United States. These forecasts were performed on January 11, 2013. Results from forecasts initiated the six previous weeks (Weeks 47-52) are also presented. The accuracy of these predictions will not be known for certain until the conclusion of the current influenza season; however, at the moment a number of the forecasted peaks appear to be inaccurate. This inaccuracy may be due to the virulence of influenza this season, which appears to be sending more influenza-infected persons to seek medical attention and inflates ILI levels (and possibly the proportion testing influenza positive) relative to years with milder flu strains. New forecasts that adjust, or scale, for this difference and match the two focus cities that appear to have already peaked are identified. These new forecasts will be used, in addition to the previously scaled forms, to make influenza predictions for the remainder of the season.
Week 49 Influenza Forecast for the 2012-2013 U.S. Season  [PDF]
Jeffrey Shaman,Alicia Karspeck,Marc Lipstich
Quantitative Biology , 2012,
Abstract: We present results of a forecast initiated Week 49 (beginning December 9, 2012) of the 2012-2013 influenza season for municipalities in the United States. The forecast was made on December 14, 2012. Results from forecasts initiated the two previous weeks (Weeks 47 and 48) are also presented. Also results from the forecast generated with the SIRS model without AH forcing (no AH) are shown
Week 51 Influenza Forecast for the 2012-2013 U.S. Season  [PDF]
Jeffrey Shaman,Alicia Karspeck,Marc Lipsitch
Quantitative Biology , 2012,
Abstract: This document is part of a series of near real-time weekly influenza forecasts made during the 2012-2013 influenza season. Here we present results of a forecast initiated following assimilation of observations for Week 51 (i.e. the forecast begins December 23, 2012) for municipalities in the United States. The forecast was made on December 28, 2012. Results from forecasts initiated the four previous weeks (Weeks 47-50) are also presented. Predictions generated with an alternate SIRS model, run without absolute humidity forcing (no AH), are also presented.
Forecasting the 2013–2014 Influenza Season Using Wikipedia  [PDF]
Kyle S. Hickmann?,Geoffrey Fairchild?,Reid Priedhorsky?,Nicholas Generous?,James M. Hyman?,Alina Deshpande?,Sara Y. Del Valle
PLOS Computational Biology , 2015, DOI: 10.1371/journal.pcbi.1004239
Abstract: Infectious diseases are one of the leading causes of morbidity and mortality around the world; thus, forecasting their impact is crucial for planning an effective response strategy. According to the Centers for Disease Control and Prevention (CDC), seasonal influenza affects 5% to 20% of the U.S. population and causes major economic impacts resulting from hospitalization and absenteeism. Understanding influenza dynamics and forecasting its impact is fundamental for developing prevention and mitigation strategies. We combine modern data assimilation methods with Wikipedia access logs and CDC influenza-like illness (ILI) reports to create a weekly forecast for seasonal influenza. The methods are applied to the 2013-2014 influenza season but are sufficiently general to forecast any disease outbreak, given incidence or case count data. We adjust the initialization and parametrization of a disease model and show that this allows us to determine systematic model bias. In addition, we provide a way to determine where the model diverges from observation and evaluate forecast accuracy. Wikipedia article access logs are shown to be highly correlated with historical ILI records and allow for accurate prediction of ILI data several weeks before it becomes available. The results show that prior to the peak of the flu season, our forecasting method produced 50% and 95% credible intervals for the 2013-2014 ILI observations that contained the actual observations for most weeks in the forecast. However, since our model does not account for re-infection or multiple strains of influenza, the tail of the epidemic is not predicted well after the peak of flu season has passed.
International Pynchon Week 2013: Call for Papers  [cached]
Samuel Thomas
Orbit : Writing Around Pynchon , 2012,
Abstract: Call for papers for International Pynchon Week 2013 to be held at Durham University, UK from the 5th-8th of August 2013
Recommendations pertaining to the use of viral vaccines: Influenza 2013
BD Schoub
South African Medical Journal , 2013,
Abstract: Here we provide recommendations for the use of viral vaccines in anticipation of the 2013 Southern Hemisphere influenza season. For a review of the 2012 influenza season, please refer to the website of the National Institute for Communicable Diseases of the National Health Laboratory Service (http://www.nicd.ac.za).
Modal Inter-Comparisons between North Atlantic Accumulated Cyclone Energy and the Atlantic Multi-Decadal Oscillation, and the Pathology of the 2013 Hurricane Season  [PDF]
Tingzhuang Yan, Shaowu Bao, Leonard J. Pietrafesa, Paul T. Gayes
Natural Science (NS) , 2014, DOI: 10.4236/ns.2014.68059
Abstract:

It is a community wide belief that the Atlantic Multi-decadal Oscillation (AMO) and the Accumulated Cyclone Energy (ACE) are strongly positively correlated and in lock-step for the characterization of a tropical cyclone (TC)—hurricane season; including how many named TCs will form and how many will become hurricanes and major hurricanes [1]-[4]. In this paper, we decompose the AMO and ACE time series into their internal modes of variability using the Hilbert-Huang Transform REF _Ref386094582 \r \h [5] and the Ensemble Empirical Modal Decomposition (EEMD) REF _Ref386094585 \r \h [6], and look into the relationships that exist between the individual corresponding modes of the AMO and the ACE. We then evaluate the degrees of frequency domain correlations between the internal modes of variability of the AMO and the ACE across the entire record length time series. The 2013 North Atlantic Hurricane Season, which had been predicted to be “above normal”, with an ACE estimated to be between 122 and 138 by the National Oceanic & Atmospheric Administration (NOAA), turned out to be one of the quietest on record. The actual 2013 observed ACE was only 33 (unit: 104 kn2) or 29% of the 65 year (1948-2012) average of 103 (with a median of 89.5) and is the 5th lowest value since 1950. Following the visual correlations between the ACE and the AMO in the past, and assuming past is prologue to the future, the “above normal” forecast of the ACE led to a tropical cyclone community wide forecast of a highly active 2013 hurricane season. So why the busted 2013 forecast? This study will address the possible reasons.

Antiviral Prescriptions to U.S. Ambulatory Care Visits with a Diagnosis of Influenza before and after High Level of Adamantane Resistance 2005–06 Season  [PDF]
Yu-Hsiang Hsieh,Kuan-Fu Chen,Charlotte A. Gaydos,Richard E. Rothman,Gabor D. Kelen
PLOS ONE , 2012, DOI: 10.1371/journal.pone.0008945
Abstract: Rapid emergence of influenza A viruses resistance to anti-influenza drugs has been observed in the past five years. Our objective was to compare antiviral prescription patterns of ambulatory care providers to patients with a diagnosis of influenza before and after the 2005–2006 influenza season, which was temporally concordant with the emergence of adamantane resistance. We also determined providers' adherence to Centers for Disease Control and Prevention (CDC) 2006 interim treatment guidelines for influenza after the dissemination of guidelines.
Planning the Future of U.S. Particle Physics (Snowmass 2013): Chapter 1: Summary  [PDF]
J. L. Rosner,M. Bardeen,W. Barletta,L. A. T. Bauerdick,R. H. Bernstein,R. Brock,D. Cronin-Hennessy,M. Demarteau,M. Dine,J. L. Feng,M. Gilchriese,S. Gottlieb,N. Graf,N. Hadley,J. L. Hewett,R. Lipton,P. McBride,H. Nicholson,M. E. Peskin,P. Ramond,S. Ritz,I. Shipsey,N. Varelas,H. Weerts,K. Yurkewicz
Physics , 2014,
Abstract: These reports present the results of the 2013 Community Summer Study of the APS Division of Particles and Fields ("Snowmass 2013") on the future program of particle physics in the U.S. Chapter 1 contains the Executive Summary and the summaries of the reports of the nine working groups.
Page 1 /100
Display every page Item


Home
Copyright © 2008-2017 Open Access Library. All rights reserved.