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

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

The Weak Lensing Signal and the Clustering of BOSS Galaxies I: Measurements

A joint analysis of the clustering of galaxies and their weak gravitational lensing signal is well-suited to simultaneously constrain the galaxy-halo connection as well as the cosmological parameters by breaking the degeneracy between galaxy bias and the amplitude of clustering signal. In a series of two papers, we perform such an analysis at the highest redshift ($z\sim0.53$) in the literature using CMASS galaxies in the Sloan Digital Sky Survey-III Baryon Oscillation Spectroscopic Survey Eleventh Data Release (SDSS-III/BOSS DR11) catalog spanning 8300~deg$^2$. In this paper, we present details of the clustering and weak lensing measurements of these galaxies. We define a subsample of 400,916 CMASS galaxies based on their redshifts and stellar mass estimates so that the galaxies constitute an approximately volume-limited and similar population over the redshift range $0.47\le z\le 0.59$. We obtain a signal-to-noise ratio $S/N\simeq 56$ for the galaxy clustering measurement. We also explore the redshift and stellar mass dependence of the clustering signal. For the weak lensing measurement, we use existing deeper imaging data from the CFHTLS with publicly available shape and photometric redshift catalogs from CFHTLenS, but only in a 105~deg$^2$ area which overlaps with BOSS. This restricts the lensing measurement to only 5,084 CMASS galaxies. After careful systematic tests, we find a highly significant detection of the CMASS weak lensing signal, with total $S/N\simeq 26$. These measurements form the basis of the halo occupation distribution and cosmology analysis presented in More et al. (Paper II).Comment: 15 pages, 13 figures. Accepted for publication in the Astrophysical Journa

Clinic accessibility and clinic-level predictors of the geographic variation in 2009 pandemic influenza vaccine coverage in Montreal, Canada

Background: Nineteen mass vaccination clinics were established in Montreal, Canada, as part of the 2009 influenza A/H1N1p vaccination campaign. Although approximately 50% of the population was vaccinated, there was a considerable variation in clinic performance and community vaccine coverage. Objective: To identify community- and clinic-level predictors of vaccine uptake, while accounting for the accessibility of clinics from the community of residence. Methods: All records of influenza A/H1N1p vaccinations administered in Montreal were obtained from a vaccine registry. Multivariable regression models, specifically Bayesian gravity models, were used to assess the relationship between vaccination rates and clinic accessibility, clinic-level factors, and community-level factors. Results: Relative risks compare the vaccination rates at the variable's upper quartile to the lower quartile. All else being equal, clinics in areas with high violent crime rates, high residential density, and high levels of material deprivation tended to perform poorly (adjusted relative risk [ARR]: 0·917, 95% CI [credible interval]: 0·915, 0·918; ARR: 0·663, 95% CI: 0·660, 0·666, ARR: 0·649, 95% CI: 0·645, 0·654, respectively). Even after controlling for accessibility and clinic-level predictors, communities with a greater proportion of new immigrants and families living below the poverty level tended to have lower rates (ARR: 0·936, 95% CI: 0·913, 0·959; ARR: 0·918, 95% CI: 0·893, 0·946, respectively), while communities with a higher proportion speaking English or French tended to have higher rates (ARR: 1·034, 95% CI: 1·012, 1·059). Conclusion: In planning future mass vaccination campaigns, the gravity model could be used to compare expected vaccine uptake for different clinic location strategies

Electronic Event–based Surveillance for Monitoring Dengue, Latin America

Dengue, a potentially fatal disease, is spreading around the world. An estimated 2.5 billion people in tropical and subtropical regions are at risk. Early detection of outbreaks is crucial to prevention and control of dengue virus and other viruses. Case reporting may often take weeks or months. Therefore, researchers explored whether electronic sources of real-time information (such as Internet news outlets, health expert mailing lists, social media sites, and queries to online search engines) might be faster, and they were. Although information from unofficial sources should be interpreted with caution, when used in conjunction with traditional case reporting, real-time electronic surveillance can help public health authorities allocate resources in time to avert full-blown epidemics