ZikaHack 2016: A digital disease detection competition

Dillon C Adam, Jitendra Jonnagaddala, Daniel Han-Chen, Sean Batongbacal, Luan Almeida, Jing Z Zhu, Jenny J Yang, Jumail M Mundekkat, Steven Badman, Abrar Chughtai, C Raina MacIntyre


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.
Anthology ID:
W17-5806
Volume:
Proceedings of the International Workshop on Digital Disease Detection using Social Media 2017 (DDDSM-2017)
Month:
November
Year:
2017
Address:
Taipei, Taiwan
Venue:
WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
39–46
Language:
URL:
https://www.aclweb.org/anthology/W17-5806
DOI:
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PDF:
http://aclanthology.lst.uni-saarland.de/W17-5806.pdf