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New Technologies for Reporting Real-time Emergent Infections

Overview
Journal Parasitology
Specialty Parasitology
Date 2012 Aug 17
PMID 22894823
Citations 20
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Abstract

Novel technologies have prompted a new paradigm in disease surveillance. Advances in computation, communications and materials enable new technologies such as mobile phones and microfluidic chips. In this paper we illustrate examples of new technologies that can augment disease detection. We describe technologies harnessing the internet, mobile phones, point of care diagnostic tools and methods that facilitate detection from passively collected unstructured data. We demonstrate how these can all assist in quicker detection, investigation and response to emerging infectious events. Novel technologies enable collection and dissemination of epidemic intelligence data to both public health practitioners and the general public, enabling finer temporal and spatial resolution of disease monitoring than through traditional public health processes.

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References
1.
Brownstein J, Freifeld C, Madoff L . Digital disease detection--harnessing the Web for public health surveillance. N Engl J Med. 2009; 360(21):2153-5, 2157. PMC: 2917042. DOI: 10.1056/NEJMp0900702. View

2.
Chan E, Sahai V, Conrad C, Brownstein J . Using web search query data to monitor dengue epidemics: a new model for neglected tropical disease surveillance. PLoS Negl Trop Dis. 2011; 5(5):e1206. PMC: 3104029. DOI: 10.1371/journal.pntd.0001206. View

3.
Prudhomme OMeara W, McKenzie F, Magill A, Forney J, Permpanich B, Lucas C . Sources of variability in determining malaria parasite density by microscopy. Am J Trop Med Hyg. 2005; 73(3):593-8. PMC: 2500224. View

4.
Zheng G, Ah Lee S, Antebi Y, Elowitz M, Yang C . The ePetri dish, an on-chip cell imaging platform based on subpixel perspective sweeping microscopy (SPSM). Proc Natl Acad Sci U S A. 2011; 108(41):16889-94. PMC: 3193234. DOI: 10.1073/pnas.1110681108. View

5.
Brownstein J, Freifeld C, Reis B, Mandl K . Surveillance Sans Frontières: Internet-based emerging infectious disease intelligence and the HealthMap project. PLoS Med. 2008; 5(7):e151. PMC: 2443186. DOI: 10.1371/journal.pmed.0050151. View