@InProceedings{adam-EtAl:2017:DDDSM,
  author    = {Adam, Dillon C  and  Jonnagaddala, Jitendra  and  Han-Chen, Daniel  and  Batongbacal, Sean  and  Almeida, Luan  and  Zhu, Jing Z  and  Yang, Jenny J  and  Mundekkat, Jumail M  and  Badman, Steven  and  Chughtai, Abrar  and  MacIntyre, C Raina},
  title     = {ZikaHack 2016: A digital disease detection competition},
  booktitle = {Proceedings of the International Workshop on Digital Disease Detection using Social Media 2017 (DDDSM-2017)},
  month     = {November},
  year      = {2017},
  address   = {Taipei, Taiwan},
  publisher = {Association for Computational Linguistics},
  pages     = {39--46},
  abstract  = {Effective response to infectious diseases
	outbreaks relies on the rapid and
	early detection of those outbreaks. Invalidated,
	yet timely and openly available
	digital information can be used for
	the early detection of outbreaks. Public
	health surveillance authorities can exploit
	these early warnings to plan and
	co-ordinate rapid surveillance and
	emergency response programs. In
	2016, a digital disease detection competition
	named ZikaHack was
	launched. The objective of the competition
	was for multidisciplinary teams
	to design, develop and demonstrate innovative
	digital disease detection solutions
	to retrospectively detect the 2015-
	16 Brazilian Zika virus outbreak earlier
	than traditional surveillance methods.
	In this paper, an overview of the ZikaHack
	competition is provided. The
	challenges and lessons learned in organizing
	this competition are also discussed
	for use by other researchers interested
	in organizing similar competitions.},
  url       = {http://www.aclweb.org/anthology/W17-5806}
}