1,736 research outputs found

    Towards a data-driven characterization of behavioral changes induced by the seasonal flu

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    In this work, we aim to determine the main factors driving self-initiated behavioral changes during the seasonal flu. To this end, we designed and deployed a questionnaire via Influweb, a Web platform for participatory surveillance in Italy, during the 2017 − 18 and 2018 − 19 seasons. We collected 599 surveys completed by 434 users. The data provide socio-demographic information, level of concerns about the flu, past experience with illnesses, and the type of behavioral changes voluntarily implemented by each participant. We describe each response with a set of features and divide them in three target categories. These describe those that report i) no (26%), ii) only moderately (36%), iii) significant (38%) changes in behaviors. In these settings, we adopt machine learning algorithms to investigate the extent to which target variables can be predicted by looking only at the set of features. Notably, 66% of the samples in the category describing more significant changes in behaviors are correctly classified through Gradient Boosted Trees. Furthermore, we investigate the importance of each feature in the classification task and uncover complex relationships between individuals’ characteristics and their attitude towards behavioral change. We find that intensity, recency of past illnesses, perceived susceptibility to and perceived severity of an infection are the most significant features in the classification task and are associated to significant changes in behaviors. Overall, the research contributes to the small set of empirical studies devoted to the data-driven characterization of behavioral changes induced by infectious disease

    Collective Response to Media Coverage of the COVID-19 Pandemic on Reddit and Wikipedia: Mixed-Methods Analysis

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    Background: The exposure and consumption of information during epidemic outbreaks may alter people’s risk perception and trigger behavioral changes, which can ultimately affect the evolution of the disease. It is thus of utmost importance to map the dissemination of information by mainstream media outlets and the public response to this information. However, our understanding of this exposure-response dynamic during the COVID-19 pandemic is still limited. Objective: The goal of this study is to characterize the media coverage and collective internet response to the COVID-19 pandemic in four countries: Italy, the United Kingdom, the United States, and Canada. Methods: We collected a heterogeneous data set including 227,768 web-based news articles and 13,448 YouTube videos published by mainstream media outlets, 107,898 user posts and 3,829,309 comments on the social media platform Reddit, and 278,456,892 views of COVID-19–related Wikipedia pages. To analyze the relationship between media coverage, epidemic progression, and users’ collective web-based response, we considered a linear regression model that predicts the public response for each country given the amount of news exposure. We also applied topic modelling to the data set using nonnegative matrix factorization. Results: Our results show that public attention, quantified as user activity on Reddit and active searches on Wikipedia pages, is mainly driven by media coverage; meanwhile, this activity declines rapidly while news exposure and COVID-19 incidence remain high. Furthermore, using an unsupervised, dynamic topic modeling approach, we show that while the levels of attention dedicated to different topics by media outlets and internet users are in good accordance, interesting deviations emerge in their temporal patterns. Conclusions: Overall, our findings offer an additional key to interpret public perception and response to the current global health emergency and raise questions about the effects of attention saturation on people’s collective awareness and risk perception and thus on their tendencies toward behavioral change.Peer ReviewedPostprint (published version

    Coping with cyclic oxygen availability: evolutionary aspects

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    Both the gradual rise in atmospheric oxygen over the Proterozoic Eon as well as episodic fluctuations in oxygen over several million-year time spans during the Phanerozoic Era, have arguably exerted strong selective forces on cellular and organismic respiratory specialization and evolution. The rise in atmospheric oxygen, some 2 billion years after the origin of life, dramatically altered cell biology and set the stage for the appearance of multicelluar life forms in the Vendian (Ediacaran) Period of the Neoproterozoic Era. Over much of the Paleozoic, the level of oxygen in the atmosphere was near the present atmospheric level (21%). In the Late Paleozoic, however, there were extended times during which the level of atmospheric oxygen was either markedly lower or markedly higher than 21%. That these Paleozoic shifts in atmospheric oxygen affected the biota is suggested by the correlations between: (1) Reduced oxygen and the occurrences of extinctions, a lowered biodiversity and shifts in phyletic succession, and (2) During hyperoxia, the corresponding occurrence of phenomena such as arthropod gigantism, the origin of insect flight, and the evolution of vertebrate terrestriality. Basic similarities in features of adaptation to hyopoxia, manifest in living organisms at levels ranging from genetic and cellular to physiological and behavioral, suggest the common and early origin of a suite of adaptive mechanisms responsive to fluctuations in ambient oxygen. Comparative integrative approaches addressing the molecular bases of phenotypic adjustments to cyclic oxygen fluctuation provide broad insight into the incremental steps leading to the early evolution of homeostatic respiratory mechanisms and to the specialization of organismic respiratory functio

    Book Chapter in Computational Demography and Health

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    Recent developments in computing, data entry and generation, and analytic tools have changed the landscape of modern demography and health research. These changes have come to be known as computational demography, big data, and precision health in the field. This emerging interdisciplinary research comprises social scientists, physical scientists, engineers, data scientists, and disease experts. This work has changed how we use administrative data, conduct surveys, and allow for complex behavioral studies via big data (electronic trace data from mobile phones, apps, etc.). This chapter reviews this emerging field's new data sources, methods, and applications

    Correlation of influenza infection with glycan array

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    Poster Presentation: SPB1 / SPB2 - Virus Host Interaction/Pathogensis/Transmission: abstract no. B109PINTRODUCTION: The past 6 years has seen the introduction of glycan arrays containing large numbers of sialic acid (Sia) containing compounds and these arrays have been used to demonstrate the relative binding affinity of influenza viruses to different glycans. Though infor...postprin

    An analysis of target recipient groups for monovalent 2009 pandemic influenza vaccine and trivalent seasonal influenza vaccines in 2009-10 and 2010-11

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    Poster Presentation: SPA5 - How to Evaluate Vaccine Effectiveness and Efficacy?: abstract no. A513PINTRODUCTION: Vaccination is generally considered to be the best primary prevention measure against influenza virus infection. Many countries encourage specific target groups of people to undertake vaccination, often with financial subsidies or a list of priority. To understand differential patterns of national target groups for influenza vaccination before, during and after the 2009 influenza pandemic, we reviewed and identified changes in national target groups for trivalent seasonal influenza and the monovalent 2009 pandemic influenza vaccines dur...postprin

    Efficacy of live attenuated seasonal and pandemic influenza vaccine in school-age children: a randomized controlled trial

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    Poster Presentation: SPA5 - How to Evaluate Vaccine Effectiveness and Efficacy?: abstract no. A508PBACKGROUND: A novel pandemic influenza A(H1N1) virus emerged in North America in early 2009 and rapidly spread worldwide. Monovalent pH1N1 vaccines were licensed later in 2009 based on preliminary studies demonstrating their immunogenicity and safety. In this study we report the efficacy of live attenuated monovalent pH1N1 vacc...postprin
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