Addressing Heart Disease and Stroke Prevention Through Comprehensive Population-Level Approaches

Addressing heart disease and stroke prevention throug

Galaxy Cluster Masses Without Non-Baryonic Dark Matter

We apply the modified acceleration law obtained from Einstein gravity coupled to a massive skew symmetric field, F_{\mu\nu\lambda}, to the problem of explaining X-ray galaxy cluster masses without exotic dark matter. Utilizing X-ray observations to fit the gas mass profile and temperature profile of the hot intracluster medium (ICM) with King beta-models, we show that the dynamical masses of the galaxy clusters resulting from our modified acceleration law fit the cluster gas masses for our sample of 106 clusters without the need of introducing a non-baryonic dark matter component. We are further able to show for our sample of 106 clusters that the distribution of gas in the ICM as a function of radial distance is well fit by the dynamical mass distribution arising from our modified acceleration law without any additional dark matter component. In previous work, we applied this theory to galaxy rotation curves and demonstrated good fits to our sample of 101 LSB, HSB and dwarf galaxies including 58 galaxies that were fit photometrically with the single parameter (M/L)_{stars}. The results there were qualitatively similar to those obtained using Milgrom's phenomenological MOND model, although the determined galaxy masses were quantitatively different and MOND does not show a return to Keplerian behavior at extragalactic distances. The results here are compared to those obtained using Milgrom's phenomenological MOND model which does not fit the X-ray galaxy cluster masses unless an auxiliary dark matter component is included.Comment: Submitted to MNRAS, July 8, 2005. 16 pages, 2 figures, 1 table, 106 galaxy cluster

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