David A. Broniatowski

Twitter Improves Influenza Forecasting

Accurate disease forecasts are imperative when preparing for influenza epidemic outbreaks; nevertheless, these forecasts are often limited by the time required to collect new, accurate data. In this paper, we show that data from the microblogging community Twitter significantly improves influenza forecasting. Most prior influenza forecast models are tested against historical influenza-like illness (ILI) data from the U.S. Centers for Disease Control and Prevention (CDC). These data are released with a one-week lag and are often initially inaccurate until the CDC revises them weeks later. Since previous studies utilize the final, revised data in evaluation, their evaluations do not properly determine the effectiveness of forecasting. Our experiments using ILI data available at the time of the forecast show that models incorporating data derived from Twitter can reduce forecasting error by 17-30% over a baseline that only uses historical data. For a given level of accuracy, using Twitter data produces forecasts that are two to four weeks ahead of baseline models. Additionally, we find that models using Twitter data are, on average, better predictors of influenza prevalence than are models using data from Google Flu Trends, the leading web data source.

Twitter: big data opportunities.

In their Policy Forum “The parable of Google Flu: Traps in big data analysis” (14 March, p. [1203][1]), D. Lazer et al. remark upon recent failures of Google Flu Trends (GFT) and cast these as limitations of big data analysis in general. However, many of these limitations have been overcome by

Germs Are Germs, and Why Not Take a Risk? Patients’ Expectations for Prescribing Antibiotics in an Inner-City Emergency Department

Background. Extensive use of unnecessary antibiotics has driven the emergence of resistant bacterial strains, posing a threat to public health. Physicians are more likely to prescribe antibiotics when they believe that patients expect them. Current attempts to change these expectations highlight the distinction between viruses and bacteria (“germs are germs”). Fuzzy-trace theory further predicts that patients expect antibiotics because they make decisions based on categorical gist, producing strategies that encourage risk taking when the status quo is bad (i.e., “why not take a risk?”). We investigate both hypotheses. Methods. We surveyed patients visiting the emergency department of a large urban hospital (72 [64%] were African American) using 17 Likert scale questions and 2 free-response questions regarding patient expectations for antibiotics. Results. After the clinical encounter, 113 patients completed the survey. Fifty-four (48%) patients agreed with items that assess the “germs are germs” hypothesis, whereas 86 (76%) agreed with items that assess the “why not take a risk?” hypothesis. “Why not take a risk?” captures significant unique variance in a factor analysis and is neither explained by “germs are germs” nor by patients’ lack of knowledge regarding side effects. Of the 81 patients who rejected the “germs are germs” hypothesis, 61 (75%) still indicated agreement with the “why not take a risk?” hypothesis. Several other misconceptions were also investigated. Conclusions. Our findings suggest that recent public health campaigns that have focused on educating patients about the differences between viruses and bacteria omit a key motivation for why patients expect antibiotics, supporting fuzzy-trace theory’s predictions about categorical gist. The implications for public health and emergency medicine are discussed.

Understanding Vaccine Refusal Why We Need Social Media Now

The recent Disneyland measles outbreak brought national attention to a growing problem: vaccine refusal—herd immunity is no longer a reality in many communities. Only 70% of children aged 19–35 months are up-to-date on immunizations, and in some communities, more than a quarter of school-age children have exemptions on file (www.doh.wa.gov/Portals/1/ Documents/Pubs/348-247-SY2014-15-ImmunizationMaps. pdf). Although they vary across the ideological spectrum, vaccine refusers tend to be well educated, white, and more affluent than people who typically experience health disparities. Prior studies have found that a diversity of motivations drive vaccine refusal, including fear that vaccines cause autism, concerns over toxins, beliefs about the benefits of measles to the immune system, distrust of government, distrust of pharmaceutical companies, and preference for a “natural” lifestyle. Arguments recommended by physicians’ groups and public health agencies to counter these beliefs do not always change minds; even parents who indicate high trust in their pediatricians may not follow doctors’ recommendations. Ultimately, people “persuade themselves to change attitudes and behavior,” and communicators must tailor messages to the beliefs, attitudes, and motivations of particular audience segments. Effective health communication about vaccines requires answering three questions:

Zika vaccine misconceptions: A social media analysis

Development of the Zika virus vaccine is in its early stages, but there is already cause for concern regarding the success of the eventual vaccination campaign. Evidence suggests the public is skeptical of the development and approval process for vaccines. Extreme media attention, which Zika has already received, can make people concerned about both the disease and the vaccine. We need only look back to the 2009–2010 H1N1 pandemic to find vaccine similar skepticism [1].

Decoupling of the minority PhD talent pool and assistant professor hiring in medical school basic science departments in the US

Faculty diversity is a longstanding challenge in the US. However, we lack a quantitative and systemic understanding of how the career transitions into assistant professor positions of PhD scientists from underrepresented minority (URM) and well-represented (WR) racial/ethnic backgrounds compare. Between 1980 and 2013, the number of PhD graduates from URM backgrounds increased by a factor of 9.3, compared with a 2.6-fold increase in the number of PhD graduates from WR groups. However, the number of scientists from URM backgrounds hired as assistant professors in medical school basic science departments was not related to the number of potential candidates (R2=0.12, p>0.07), whereas there was a strong correlation between these two numbers for scientists from WR backgrounds (R2=0.48, p<0.0001). We built and validated a conceptual system dynamics model based on these data that explained 79% of the variance in the hiring of assistant professors and posited no hiring discrimination. Simulations show that, given current transition rates of scientists from URM backgrounds to faculty positions, faculty diversity would not increase significantly through the year 2080 even in the context of an exponential growth in the population of PhD graduates from URM backgrounds, or significant increases in the number of faculty positions. Instead, the simulations showed that diversity increased as more postdoctoral candidates from URM backgrounds transitioned onto the market and were hired. DOI: http://dx.doi.org/10.7554/eLife.21393.001

System of Systems Architecture: The Case of Space Situational Awareness

The presence of space situational awareness is one approach to mitigating the long-term risks associated with space debris in low Earth orbit (LEO). As the U.S. and other nations continue to develop the space situational awareness mission area, questions arise as to how stakeholders should act to mitigate the effects of resident space objects and how our understanding of the physics of LEO inform the evolution of groundand space-based sensors. To characterize interactions among international stakeholders, space situational awareness is modeled as a system of systems with technical and social elements. Through the use of game-theoretic cooperation archetypes and System Dynamics modeling, possible futures are explored. Extensions in space situational awareness capabilities are modeled as mechanisms to improve satellite survivability. Finally, general implications for system architecture and systems of systems are elucidated.

Measuring Flexibility, Descriptive Complexity, and Rework Potential in Generic System Architectures

A systems architecture defines its flexibility-the ease with which changes to the systems structure may be made. Systems may possess an internal structure that is relatively complex to describe-especially after many changes. In addition, some changes may require several iterations before stakeholders converge on a final design choice. There is currently no unified mathematical theory that captures these factors. We developed metrics for flexibility, descriptive complexity, and rework potential defined on graph-based representations of a systems structure. We applied these metrics to four idealized generic system architectures: Tree-structured hierarchies are easy to describe and require minimal design iteration. However, they permit relatively few changes, unless the underlying architecture itself is changed. Furthermore, each such change adds significant complexity. Grid networks are more flexible than trees, but also more descriptively complex. Grids also require some degree of design iteration between stakeholders. Teams are extremely flexible but the requirement for consensus e.g., due to cognitive limits among humans restricts their size. Layered hierarchies possess moderate flexibility and require fewer iterations than corresponding grids and teams, although they can be difficult to describe succinctly. Our findings suggest that no architecture is ideal under all circumstances; rather, each has strengths and weaknesses that can be exploited in different environments.

A Formal Model of Fuzzy-Trace Theory: Variations on Framing Effects and the Allais Paradox.

Fuzzy-trace theory assumes that decision-makers process qualitative “gist” representations and quantitative “verbatim” representations in parallel. We develop a lattice model of fuzzy-trace theory that explains both processes. Specifically, the model provides a novel formalization of how (a) decision-makers encode multiple representations of options in parallel, (b) representations compete or combine so that choices often turn on the simplest representation of encoded gists, and (c) choices between representations are made based on positive versus negative valences associated with social and moral principles stored in long-term memory (e.g., saving lives is good). The model integrates effects of individual differences in numeracy, metacognitive monitoring and editing, and sensation seeking. We conducted a systematic review of variations on framing effects and the Allais Paradox, both core phenomena of risky decision-making, and tested whether our model could predict observed choices: The model successfully predicted 82 of 88 (93%) pairs of studies (comparing gain to loss conditions) demonstrating 16 variations on effects, theoretically critical manipulations that eliminate or exaggerate framing effects. When examining these conditions individually, the model successfully predicted 153 (90%) of 170 eligible studies. Parameters of the model varied in theoretically meaningful ways with differences in numeracy, metacognitive monitoring, and sensation seeking, accounting for risk preferences at the group level. New experiments show similar results at the individual level. The model is also shown to be scientifically parsimonious using standard measures. Relations to current theories, such as Cumulative Prospect Theory, and potential extensions are discussed.

Studying Group Behaviors: A tutorial on text and network analysis methods

Many important technical and policy decisions are made by small groups, especially by deliberative committees of technical experts. Such committees are charged with fairly combining information from multiple perspectives to reach a decision that one person could not make alone. Committees are social entities and are therefore affected by any number of mechanisms recorded in the social sciences. Our challenge is to determine which of these mechanisms are likely to be encountered in the deliberative process and to evaluate how they might impact upon decision outcomes. In particular, we examine the role of committee deliberations on the U.S. Food and Drug Administrations (FDAs) advisory panels.