ترغب بنشر مسار تعليمي؟ اضغط هنا

Modeling Corporate Epidemiology

102   0   0.0 ( 0 )
 نشر من قبل Manuel Cebrian
 تاريخ النشر 2010
  مجال البحث الهندسة المعلوماتية
والبحث باللغة English




اسأل ChatGPT حول البحث

Corporate responses to illness is currently an ad-hoc, subjective process that has little basis in data on how disease actually spreads at the workplace. Additionally, many studies have shown that productivity is not an individual factor but a social one: in any study on epidemic responses this social factor has to be taken into account. The barrier to addressing this problem has been the lack of data on the interaction and mobility patterns of people in the workplace. We have created a wearable Sociometric Badge that senses interactions between individuals using an infra-red (IR) transceiver and proximity using a radio transmitter. Using the data from the Sociometric Badges, we are able to simulate diseases spreading through face-to-face interactions with realistic epidemiological parameters. In this paper we construct a curve trading off productivity with epidemic potential. We are able to take into account impacts on productivity that arise from social factors, such as interaction diversity and density, which studies that take an individual approach ignore. We also propose new organizational responses to diseases that take into account behavioral patterns that are associated with a more virulent disease spread. This is advantageous because it will allow companies to decide appropriate responses based on the organizational context of a disease outbreak.



قيم البحث

اقرأ أيضاً

Diseases spread over temporal networks of interaction events between individuals. Structures of these temporal networks hold the keys to understanding epidemic propagation. One early concept of the literature to aid in discussing these structures is concurrency -- quantifying individuals tendency to form time-overlapping partnerships. Although conflicting evaluations and an overabundance of operational definitions have marred the history of concurrency, it remains important, especially in the area of sexually transmitted infections. Today, much of theoretical epidemiology uses more direct models of contact patterns, and there is an emerging body of literature trying to connect methods to the concurrency literature. In this review, we will cover the development of the concept of concurrency and these new approaches.
Many governments have managed to control their COVID-19 outbreak with a simple message: keep the effective $R$ number $R<1$ to prevent widespread contagion and flatten the curve. This raises the question whether a similar policy could control dangero us online infodemics of information, misinformation and disinformation. Here we show, using multi-platform data from the COVID-19 infodemic, that its online spreading instead encompasses a different dynamical regime where communities and users within and across independent platforms, sporadically form temporary active links on similar timescales to the viral spreading. This allows material that might have died out, to evolve and even mutate. This has enabled niche networks that were already successfully spreading hate and anti-vaccination material, to rapidly become global super-spreaders of narratives featuring fake COVID-19 treatments, anti-Asian sentiment and conspiracy theories. We derive new tools that incorporate these coupled social-viral dynamics, including an online $R$, to help prevent infodemic spreading at all scales: from spreading across platforms (e.g. Facebook, 4Chan) to spreading within a given subpopulation, or community, or topic. By accounting for similar social and viral timescales, the same mathematical theory also offers a quantitative description of other unconventional infection profiles such as rumors spreading in financial markets and colds spreading in schools.
We examine the temporal evolution of digital communication activity relating to the American anti-capitalist movement Occupy Wall Street. Using a high-volume sample from the microblogging site Twitter, we investigate changes in Occupy participant eng agement, interests, and social connectivity over a fifteen month period starting three months prior to the movements first protest action. The results of this analysis indicate that, on Twitter, the Occupy movement tended to elicit participation from a set of highly interconnected users with pre-existing interests in domestic politics and foreign social movements. These users, while highly vocal in the months immediately following the birth of the movement, appear to have lost interest in Occupy related communication over the remainder of the study period.
164 - Scott A. Hale , Helen Margetts , 2013
Now that so much of collective action takes place online, web-generated data can further understanding of the mechanics of Internet-based mobilisation. This trace data offers social science researchers the potential for new forms of analysis, using r eal-time transactional data based on entire populations, rather than sample-based surveys of what people think they did or might do. This paper uses a `big data approach to track the growth of over 8,000 petitions to the UK Government on the No. 10 Downing Street website for two years, analysing the rate of growth per day and testing the hypothesis that the distribution of daily change will be leptokurtic (rather than normal) as previous research on agenda setting would suggest. This hypothesis is confirmed, suggesting that Internet-based mobilisation is characterized by tipping points (or punctuated equilibria) and explaining some of the volatility in online collective action. We find also that most successful petitions grow quickly and that the number of signatures a petition receives on its first day is a significant factor in explaining the overall number of signatures a petition receives during its lifetime. These findings have implications for the strategies of those initiating petitions and the design of web sites with the aim of maximising citizen engagement with policy issues.
We present work in jointly inferring the unique individuals as well as their social rank within a collection of letters from an Old Assyrian trade colony in Kultepe, Turkey, settled by merchants from the ancient city of Assur for approximately 200 ye ars between 1950-1750 BCE, the height of the Middle Bronze Age. Using a probabilistic latent-variable model, we leverage pairwise social differences between names in cuneiform tablets to infer a single underlying social order that best explains the data we observe. Evaluating our output with published judgments by domain experts suggests that our method may be used for building informed hypotheses that are driven by data, and that may offer promising avenues for directed research by Assyriologists.
التعليقات
جاري جلب التعليقات جاري جلب التعليقات
سجل دخول لتتمكن من متابعة معايير البحث التي قمت باختيارها
mircosoft-partner

هل ترغب بارسال اشعارات عن اخر التحديثات في شمرا-اكاديميا