Why We Need Crowdsourced Data in Infectious Disease Surveillance
- 399 Downloads
In infectious disease surveillance, public health data such as environmental, hospital, or census data have been extensively explored to create robust models of disease dynamics. However, this information is also subject to its own biases, including latency, high cost, contributor biases, and imprecise resolution. Simultaneously, new technologies including Internet and mobile phone based tools, now enable information to be garnered directly from individuals at the point of care. Here, we consider how these crowdsourced data offer the opportunity to fill gaps in and augment current epidemiological models. Challenges and methods for overcoming limitations of the data are also reviewed. As more new information sources become mature, incorporating these novel data into epidemiological frameworks will enable us to learn more about infectious disease dynamics.
KeywordsCrowdsourcing Surveillance Technology Bias
Research reported in this publication was supported by grants from the National Library of Medicine of the National Institutes of Health under Award Numbers G08 LM009776, and R01 LM010812 and Google.org.
Compliance with Ethics Guidelines
Conflict of Interest
Rumi Chunara, Mark S. Smolinski, and John S. Brownstein declare that they have no conflict of interest.
Human and Animal Rights and Informed Consent
This article does not contain any studies with human or animal subjects performed by any of the authors.
- 4.Reis BY, Mandl KD. Time series modeling for syndromic surveillance. BMC Med Inform Decis Mak. 2003;3.Google Scholar
- 10.Chunara R, Chhaya V, Bane S, Mekaru S, Chan E, Freifeld C, et al. Online reporting for malaria surveillance using micro-monetary incentives, in urban India 2010–2011. Mala J. 2012;11(43).Google Scholar
- 13.Meymaris K, Henderson S, Alaback P, Havens K, editors. Project BudBurst: Citizen Science for All Seasons. AGU Fall Meeting Abstracts; 2008.Google Scholar
- 15.Chunara R, Freifeld CC, Brownstein JS. New technologies for reporting real-time emergent infections. Parasitology. 2012;1(1):1–9.Google Scholar
- 16.The Centers for Disease Control and Prevention. FluView. Available from: gis.cdc.gov/grasp/fluview/fluportaldashboard.html. Accessed March 13, 2012.
- 17.Copeland KR, Allen AE, editors. Basic Models for Mapping Prescription Drug Data. Proceedings of the Survey Research Methods Section, American Statistical Association; 2005.Google Scholar
- 18.The World Health Organization. Global Outbreak Alert & Response Network. Available from: http://www.who.int/csr/outbreaknetwork/en/%5D. Accessed March 6, 2013.
- 21.The World Health Organization. Global Alert and Response: Epidemic intelligence - systematic event detection. Available from: http://www.who.int/csr/alertresponse/epidemicintelligence/en/index.html. Accessed March 6, 2013.
- 28.Chunara R, Bouton L, Ayers JW, Brownstein JS. Assessing the online social environment for surveillance of obesity prevalence. PloS One. 2013;8(4):e61373.Google Scholar
- 31.Chunara R, Aman S, Smolinski M, Brownstein JS. Flu near you: an online self-reported influenza surveillance system in the USA. Online J Public Health Inform. 2013;5(1).Google Scholar