2,385 research outputs found
Quantifying the Biases of Spectroscopically Selected Gravitational Lenses
Spectroscopic selection has been the most productive technique for the
selection of galaxy-scale strong gravitational lens systems with known
redshifts. Statistically significant samples of strong lenses provide a
powerful method for measuring the mass-density parameters of the lensing
population, but results can only be generalized to the parent population if the
lensing selection biases are sufficiently understood. We perform controlled
Monte Carlo simulations of spectroscopic lens surveys in order to quantify the
bias of lenses relative to parent galaxies in velocity dispersion, mass axis
ratio, and mass density profile. For parameters typical of the SLACS and BELLS
surveys, we find: (1) no significant mass axis ratio detection bias of lenses
relative to parent galaxies; (2) a very small detection bias toward shallow
mass density profiles, which is likely negligible compared to other sources of
uncertainty in this parameter; (3) a detection bias towards smaller Einstein
radius for systems drawn from parent populations with group- and cluster-scale
lensing masses; and (4) a lens-modeling bias towards larger velocity
dispersions for systems drawn from parent samples with sub-arcsecond mean
Einstein radii. This last finding indicates that the incorporation of
velocity-dispersion upper limits of \textit{non-lenses} is an important
ingredient for unbiased analyses of spectroscopically selected lens samples. In
general we find that the completeness of spectroscopic lens surveys in the
plane of Einstein radius and mass-density profile power-law index is quite
uniform, up to a sharp drop in the region of large Einstein radius and steep
mass density profile, and hence that such surveys are ideally suited to the
study of massive field galaxies.Comment: Accepted for publication in Astrophys. J., June 7, 2012. In press. 9
pages, 5 figures, 1 tabl
Probing Brownstein-Moffat Gravity via Numerical Simulations
In the standard scenario of the Newtonian gravity, a late-type galaxy (i.e.,
a spiral galaxy) is well described by a disk and a bulge embedded in a halo
mainly composed by dark matter. In Brownstein-Moffat gravity, there is a claim
that late-type galaxy systems would not need to have halos, avoiding as a
result the dark matter problem, i.e., a modified gravity (non-Newtonian) would
account for the galactic structure with no need of dark matter. In the present
paper, we probe this claim via numerical simulations. Instead of using a
"static galaxy," where the centrifugal equilibrium is usually adopted, we probe
the Brownstein-Moffat gravity dynamically via numerical -body simulations.Comment: 33 pages and 14 figures - To appear in The Astrophysical Journa
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Wikipedia Usage Estimates Prevalence of Influenza-Like Illness in the United States in Near Real-Time
Circulating levels of both seasonal and pandemic influenza require constant surveillance to ensure the health and safety of the population. While up-to-date information is critical, traditional surveillance systems can have data availability lags of up to two weeks. We introduce a novel method of estimating, in near-real time, the level of influenza-like illness (ILI) in the United States (US) by monitoring the rate of particular Wikipedia article views on a daily basis. We calculated the number of times certain influenza- or health-related Wikipedia articles were accessed each day between December 2007 and August 2013 and compared these data to official ILI activity levels provided by the Centers for Disease Control and Prevention (CDC). We developed a Poisson model that accurately estimates the level of ILI activity in the American population, up to two weeks ahead of the CDC, with an absolute average difference between the two estimates of just 0.27% over 294 weeks of data. Wikipedia-derived ILI models performed well through both abnormally high media coverage events (such as during the 2009 H1N1 pandemic) as well as unusually severe influenza seasons (such as the 2012–2013 influenza season). Wikipedia usage accurately estimated the week of peak ILI activity 17% more often than Google Flu Trends data and was often more accurate in its measure of ILI intensity. With further study, this method could potentially be implemented for continuous monitoring of ILI activity in the US and to provide support for traditional influenza surveillance tools
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Quantifying the effect of media limitations on outbreak data in a global online web-crawling epidemic intelligence system, 2008–2011
Background: This is the first study quantitatively evaluating the effect that media-related limitations have on data from an automated epidemic intelligence system. Methods: We modeled time series of HealthMap's two main data feeds, Google News and Moreover, to test for evidence of two potential limitations: first, human resources constraints, and second, high-profile outbreaks “crowding out” coverage of other infectious diseases. Results: Google News events declined by 58.3%, 65.9%, and 14.7% on Saturday, Sunday and Monday, respectively, relative to other weekdays. Events were reduced by 27.4% during Christmas/New Years weeks and 33.6% lower during American Thanksgiving week than during an average week for Google News. Moreover data yielded similar results with the addition of Memorial Day (US) being associated with a 36.2% reduction in events. Other holiday effects were not statistically significant. We found evidence for a crowd out phenomenon for influenza/H1N1, where a 50% increase in influenza events corresponded with a 4% decline in other disease events for Google News only. Other prominent diseases in this database – avian influenza (H5N1), cholera, or foodborne illness – were not associated with a crowd out phenomenon. Conclusions: These results provide quantitative evidence for the limited impact of editorial biases on HealthMap's web-crawling epidemic intelligence
Big brother is watching - using digital disease surveillance tools for near real-time forecasting
Abstract for the International Journal of Infectious Diseases 79 (S1) (2019).https://www.ijidonline.com/article/S1201-9712(18)34659-9/abstractPublished versio
Combining Search, Social Media, and Traditional Data Sources to Improve Influenza Surveillance
We present a machine learning-based methodology capable of providing
real-time ("nowcast") and forecast estimates of influenza activity in the US by
leveraging data from multiple data sources including: Google searches, Twitter
microblogs, nearly real-time hospital visit records, and data from a
participatory surveillance system. Our main contribution consists of combining
multiple influenza-like illnesses (ILI) activity estimates, generated
independently with each data source, into a single prediction of ILI utilizing
machine learning ensemble approaches. Our methodology exploits the information
in each data source and produces accurate weekly ILI predictions for up to four
weeks ahead of the release of CDC's ILI reports. We evaluate the predictive
ability of our ensemble approach during the 2013-2014 (retrospective) and
2014-2015 (live) flu seasons for each of the four weekly time horizons. Our
ensemble approach demonstrates several advantages: (1) our ensemble method's
predictions outperform every prediction using each data source independently,
(2) our methodology can produce predictions one week ahead of GFT's real-time
estimates with comparable accuracy, and (3) our two and three week forecast
estimates have comparable accuracy to real-time predictions using an
autoregressive model. Moreover, our results show that considerable insight is
gained from incorporating disparate data streams, in the form of social media
and crowd sourced data, into influenza predictions in all time horizon
Using search queries for malaria surveillance, Thailand
Background: Internet search query trends have been shown to correlate with incidence trends for select infectious diseases and countries. Herein, the first use of Google search queries for malaria surveillance is investigated. The research focuses on Thailand where real-time malaria surveillance is crucial as malaria is re-emerging and developing resistance to pharmaceuticals in the region. Methods: Official Thai malaria case data was acquired from the World Health Organization (WHO) from 2005 to 2009. Using Google correlate, an openly available online tool, and by surveying Thai physicians, search queries potentially related to malaria prevalence were identified. Four linear regression models were built from different sub-sets of malaria-related queries to be used in future predictions. The models’ accuracies were evaluated by their ability to predict the malaria outbreak in 2009, their correlation with the entire available malaria case data, and by Akaike information criterion (AIC). Results: Each model captured the bulk of the variability in officially reported malaria incidence. Correlation in the validation set ranged from 0.75 to 0.92 and AIC values ranged from 808 to 586 for the models. While models using malaria-related and general health terms were successful, one model using only microscopy-related terms obtained equally high correlations to malaria case data trends. The model built strictly of queries provided by Thai physicians was the only one that consistently captured the well-documented second seasonal malaria peak in Thailand. Conclusions: Models built from Google search queries were able to adequately estimate malaria activity trends in Thailand, from 2005–2010, according to official malaria case counts reported by WHO. While presenting their own limitations, these search queries may be valid real-time indicators of malaria incidence in the population, as correlations were on par with those of related studies for other infectious diseases. Additionally, this methodology provides a cost-effective description of malaria prevalence that can act as a complement to traditional public health surveillance. This and future studies will continue to identify ways to leverage web-based data to improve public health
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