references.bib

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@misc{cdc,
    author={CDC},
    title={Influenza (flu)},
    howpublished={\url{www.cdc.gov/flu/index.htm}},
    note={Accessed: 2015-09-17}
}

@misc{paho,
    author={PAHO},
    title={Influenza and other Respiratory Viruses},
    howpublished={\url{http://ais.paho.org/phip/viz/ed_flu.asp}},
    note={Accessed: 2015-09-01}
}

@misc{who,
    author={WHO},
    title={Surveillance and Monitoring},
    howpublished={\url{http://www.who.int/influenza/surveillance_monitoring/en/}},
    note={Accessed: 2015-09-17}
}

@article{fischhoff2014communicating,
  title={Communicating Scientific Uncertainty},
  author={Fischhoff, Baruch and Davis, Alex L},
  journal={Proceedings of the National Academy of Sciences},
  volume={111},
  number={Supplement 4},
  pages={13664--13671},
  year={2014},
  publisher={National Acad Sciences}
}


@inproceedings{chakraborty2014forecasting,
  title={Forecasting a moving target: Ensemble models for ILI case count
      predictions},
  author={Chakraborty, Prithwish and Khadivi, Pejman and Lewis, Bryan and
      Mahendiran, Aravindan and Chen, Jiangzhuo and Butler, Patrick and
      Nsoesie, Elaine O and Mekaru, Sumiko R and Brownstein, John S and
      Marathe, Madhav V and Ramakrishnan, Naren},
  booktitle={Proceedings of SDM '14},
  year={2014},
  pages={262--270},
  organization={SIAM}
}

@article{shaman2013real,
  title={Real-time influenza forecasts during the 2012--2013 season},
  author={Shaman, Jeffrey and Karspeck, Alicia and Yang, Wan and Tamerius, James and Lipsitch, Marc},
  journal={Nature communications},
  volume={4},
  year={2013},
  publisher={Nature Publishing Group}
}

@inproceedings{matsubara2014funnel,
  title={FUNNEL: automatic mining of spatially coevolving epidemics},
  author={Matsubara, Yasuko and Sakurai, Yasushi and van Panhuis, Willem G and Faloutsos, Christos},
  booktitle={Proceedings of KDD'14},
  pages={105--114},
  year={2014},
  organization={ACM}
}

@techreport{tabataba2015smq,
title = {Standard Measures and Quality Metrics for Evaluating the Performance of Forecasting Methods: Special Study on Influenza in US },
author = {Farzaneh S. Tabataba and Prithwish Chakraborty and Naren Ramakrishnan
    and Madhav V. Marathe and Jiangzhuo Chen and Bryan L. Lewis},
year = {2015},
institution = {NDSSL},
reportnumber = {15-107},
abstract = {Over the past few decades, numerous forecasting methods have been
  proposed in the field of epidemic forecasting. Such methods can be
  classified into different categories such as deterministic vs
  probabilistic, comparative methods versus generative methods and so on.
  In some of the more popular comparative methods, researchers compare
  observed epidemiological data from early stages of an outbreak with
  proposed models to forecast about the future trend and prevalence of
  the pandemic. One of the most important problems in this area is the
  lack of standard well defined evaluation measures to select the best
  algorithm among different ones as well as for selecting the best
  possible configuration for a par- ticular algorithm. In this paper, we
  present different epidemic measures to characterize the output of
  forecasting methods and furthermore provide suitable metrics that could
  be used to evaluate the accu- racy of the methods with respect to these
  epidemic measures. we are focusing on seasonal predictions rather than
  near time predictions and present our analysis on two different
  forecasting methods which generate prediction for the whole season
  based on observed data. Our accuracy results suggest that, while
  evaluating forecasts with respect to various epidemic measures, no
  single measure or metric can describe all the features of a forecasting
  algorithm. Furthermore, all/most of them should be considered to have a
  comprehensive evaluation. Moreover, depending on the purpose of the
  forecasting algorithm, some epidemic measures should be weighted more
  for concluding the comparison. The proposed epidemic measures can also
  be used to describe different features of an epidemic season and the 
  abnormal values of these measures (low or high), can indicate a severe 
  season that is an alarm for health center providers.},
}

@article{hall2007,
    author={Hall, I. M. and Gani, R. and Hughes, H. E. and Leach, S.},
    title={Real-time epidemic forecasting for pandemic influenza},
    journal={Epidemiology and Infection},
    volume={135},
    issue={03},
    month={4},
    year={2007},
    pages={372--385},
    numpages={14},
}

@article{lampos2012nowcasting,
  title={Nowcasting events from the social web with statistical learning},
  author={Lampos, Vasileios and Cristianini, Nello},
  journal={ACM Transactions on Intelligent Systems and Technology (TIST)},
  volume={3},
  number={4},
  pages={72},
  year={2012},
  publisher={ACM}
}

@article{Preis140095,
	author={Preis, Tobias and Moat, Helen Susannah},
	title={Adaptive nowcasting of influenza outbreaks using~Google searches},
	volume={1},
	number={2},
	year={2014},
	_doi={10.1098/rsos.140095},
	publisher={The Royal Society},
	journal={Royal Society Open Science}
}

@article{ronni2015empbayes,
    author={Brooks, Logan and Hyun, Sangwon and Tibshirani, Ryan and Rosenfeld, Roni},
    title={Flexible Modeling of Epidemics with an Empirical Bayes Framework},
    journal={PLoS Computational Biology},
    volume={11},
    number={8},
    year={2015},
}

@article{mciver2014wikipedia,
  title={Wikipedia usage estimates prevalence of influenza-like illness in the United States in near real-time},
  author={McIver, David J and Brownstein, John S},
  journal={PLoS Comput Biol},
  volume={10},
  number={4},
  pages={e1003581},
  year={2014}
}


@article{hickman2015wikipedia,
    author = {Hickmann, Kyle S. AND Fairchild, Geoffrey AND Priedhorsky, Reid AND Generous, Nicholas AND Hyman, James M. AND Deshpande, Alina AND Del Valle, Sara Y.},
    journal = {PLoS Comput Biol},
    _publisher = {Public Library of Science},
    title = {Forecasting the 2013–2014 Influenza Season Using Wikipedia},
    year = {2015},
    _month = {05},
    volume = {11},
    pages = {e1004239},
    number = {5},
}        

@article{lazer2014parable,
  title={The parable of Google Flu: traps in big data analysis},
  author={Lazer, David and Kennedy, Ryan and King, Gary and Vespignani, Alessandro},
  journal={Science},
  volume={343},
  number={14 March},
  year={2014}
}