@article{10.1371/journal.pone.0019467,
  abstract = {        <p>Twitter is a free social networking and micro-blogging service that enables its                     millions of users to send and read each other's “tweets,” or                     short, 140-character messages. The service has more than 190 million registered                     users and processes about 55 million tweets per day. Useful information about                     news and geopolitical events lies embedded in the Twitter stream, which                     embodies, in the aggregate, Twitter users' perspectives and reactions to                     current events. By virtue of sheer volume, content embedded in the Twitter                     stream may be useful for tracking or even forecasting behavior if it can be                     extracted in an efficient manner. In this study, we examine the use of                     information embedded in the Twitter stream to (1) track rapidly-evolving public                     sentiment with respect to H1N1 or swine flu, and (2) track and measure actual                     disease activity. We also show that Twitter can be used as a measure of public                     interest or concern about health-related events. Our results show that estimates                     of influenza-like illness derived from Twitter chatter accurately track reported                     disease levels.</p>       },
  author = {Signorini, Alessio and Segre, Alberto Maria and Polgreen, Philip M.},
  doi = {10.1371/journal.pone.0019467},
  interhash = {56b199b8f3a3d085aef43e25b2aca06b},
  intrahash = {6c3bc3dabf1b1d0095774e87b14d3ad2},
  journal = {PLoS ONE},
  month = {05},
  number = 5,
  pages = {e19467},
  publisher = {Public Library of Science},
  title = {The Use of Twitter to Track Levels of Disease Activity and Public                     Concern in the U.S. during the Influenza A H1N1 Pandemic},
  url = {http://dx.doi.org/10.1371%2Fjournal.pone.0019467},
  volume = 6,
  year = 2011
}

@misc{Medus2011,
  abstract = {  The individual-based models constitute a set of widely implemented tools to
analyze the incidence of individuals heterogeneities in the spread of an
infectious disease. In this work we focus our attention on human contacts
heterogeneities through two of the main individual-based models: mobile agents
and complex networks models. We introduce a novel mobile agents model in which
individuals make displacements with sizes according to a truncated power-law
distribution based on empirical evidence about human mobility. Besides, we
present a procedure to obtain an equivalent weighted contact network from the
previous mobile agents model, where the weights of the links are interpreted as
contact probabilities. From the topological analysis of the equivalent contact
networks we show that small world characteristics are related with truncated
power-law distribution for agent displacements. Finally, we show the
equivalence between both approaches through some numerical experiments for the
spread of an infectious disease.
},
  author = {Medus, A. D. and Dorso, C. O.},
  interhash = {d2f8fefb3a26400deab44de456bd990c},
  intrahash = {8ad77fa71d773ea860280d528fd0b1c2},
  note = {cite arxiv:1104.4913
Comment: 19 pages, 7 figures, submitted to PRE},
  title = {Diseases spreading through individual based models with realistic
  mobility patterns},
  url = {http://arxiv.org/abs/1104.4913},
  year = 2011
}