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Seasonal influenza epidemics are a major public health concern, causing tens of millions of respiratory illnesses and 250,000 to 500,000 deaths worldwide each year (1). In addition to seasonal influenza, a new strain of influenza virus against which no previous immunity exists and that demonstrates human-to-human transmission could result in a pandemic with millions of fatalities (2). Early detection of disease activity, when followed by a rapid response, can reduce the impact of both seasonal and pandemic influenza (3,4). One way to improve early detection is to monitor health-seeking behaviour in the form of queries to online search engines, which are submitted by millions of users around the world each day. Here we present a method of analysing large numbers of Google search queries to track influenza-like illness in a population. Because the relative frequency of certain queries is highly correlated with the percentage of physician visits in which a patient presents with influenza-like symptoms, we can accurately estimate the current level of weekly influenza activity in each region of the United States, with a reporting lag of about one day. This approach may make it possible to use search queries to detect influenza epidemics in areas with a large population of web search users., Traditional surveillance systems, including those used by the US Centers for Disease Control and Prevention (CDC) and the European Influenza Surveillance Scheme (EISS), rely on both virological and clinical data, [...]

Objective: The objective of openFDA is to facilitate access and use of big important Food and Drug Administration public datasets by developers, researchers, and the public through harmonization of data across disparate FDA datasets provided via application programming interfaces (APIs).Materials and Methods: Using cutting-edge technologies deployed on FDA's new public cloud computing infrastructure, openFDA provides open data for easier, faster (over 300 requests per second per process), and better access to FDA datasets; open source code and documentation shared on GitHub for open community contributions of examples, apps and ideas; and infrastructure that can be adopted for other public health big data challenges.Results: Since its launch on June 2, 2014, openFDA has developed four APIs for drug and device adverse events, recall information for all FDA-regulated products, and drug labeling. There have been more than 20 million API calls (more than half from outside the United States), 6000 registered users, 20,000 connected Internet Protocol addresses, and dozens of new software (mobile or web) apps developed. A case study demonstrates a use of openFDA data to understand an apparent association of a drug with an adverse event.Conclusion: With easier and faster access to these datasets, consumers worldwide can learn more about FDA-regulated products. [ABSTRACT FROM AUTHOR]

Background: Google Flu Trends (GFT) uses anonymized, aggregated internet search activity to provide near-real time estimates of influenza activity. GFT estimates have shown a strong correlation with official influenza surveillance data. The 2009 influenza virus A (H1N1) pandemic [pH1N1] provided the first opportunity to evaluate GFT during a non-seasonal influenza outbreak. In September 2009, an updated United States GFT model was developed using data from the beginning of pH1N1. Methodology/Principal Findings: We evaluated the accuracy of each U.S. GFT model by comparing weekly estimates of ILI (influenza-like illness) activity with the U.S. Outpatient Influenza-like Illness Surveillance Network (ILINet). For each GFT model we calculated the correlation and RMSE (root mean square error) between model estimates and ILINet for four time periods: pre-H1N1, Summer H1N1, Winter H1N1, and H1N1 overall (Mar 2009-Dec 2009). We also compared the number of queries, query volume, and types of queries (e.g., influenza symptoms, influenza complications) in each model. Both models' estimates were highly correlated with ILINet pre-H1N1 and over the entire surveillance period, although the original model underestimated the magnitude of ILI activity during pH1N1. The updated model was more correlated with ILINet than the original model during Summer H1N1 (r = 0.95 and 0.29, respectively). The updated model included more search query terms than the original model, with more queries directly related to influenza infection, whereas the original model contained more queries related to influenza complications. Conclusions: Internet search behavior changed during pH1N1, particularly in the categories ''influenza complications'' and ''term for influenza.'' The complications associated with pH1N1, the fact that pH1N1 began in the summer rather than winter, and changes in health-seeking behavior each may have played a part. Both GFT models performed well prior to and during pH1N1, although the updated model performed better during pH1N1, especially during the summer months. [ABSTRACT FROM AUTHOR]

We describe a walking microrobot that is propelled by cilialike thermal bimorph actuator arrays. The robot consists of two actuator array chips, each having an 8 x 8 array of "motion pixels," which are composed of four orthogonally oriented cilia. Each group of unidirectional cilia is controlled independently for each chip, which provides planar motion with three degrees of freedom (x, y, θ). The robot is approximately 3 cm in length, 1 cm in width, and 0.9 mm in height and has a mass of 0.5 g. By varying the actuation frequency and motion gait strategy, the direction and velocity of the motion can be controlled. In this paper, we present the system architecture, control mechanism, and modeling of the robot, as well as experimental results, during linear and rotary motion. The robot can carry loads up to seven times its own mass, and it can operate at speeds up to 250 µm/s with step sizes from 1 to 4 µm. [ABSTRACT FROM AUTHOR]