Hostname: page-component-586b7cd67f-2plfb Total loading time: 0 Render date: 2024-11-22T23:42:26.988Z Has data issue: false hasContentIssue false
  • English
  • Français

New technologies for reporting real-time emergent infections

Published online by Cambridge University Press:  23 August 2012

RUMI CHUNARA ,
CLARK C. FREIFELD  and
JOHN S. BROWNSTEIN
RUMI CHUNARA*
Affiliation:
Department of Pediatrics, Harvard Medical School, Boston, Massachusetts, 02215, USA Children's Hospital Informatics Program, Division of Emergency Medicine, Boston Children's Hospital, Boston, Massachusetts, 02215, USA
CLARK C. FREIFELD
Affiliation:
Children's Hospital Informatics Program, Division of Emergency Medicine, Boston Children's Hospital, Boston, Massachusetts, 02215, USA Department of Biomedical Engineering, Boston University College of Engineering, Boston, Massachusetts, USA
JOHN S. BROWNSTEIN
Affiliation:
Department of Pediatrics, Harvard Medical School, Boston, Massachusetts, 02215, USA Children's Hospital Informatics Program, Division of Emergency Medicine, Boston Children's Hospital, Boston, Massachusetts, 02215, USA
*
*Corresponding author: 1 Autumn St. Fourth Floor Suite 433 Boston, MA 02215, Tel: 857-218-5108. Fax: 617-730-0267. E-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Summary

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.

Keywords

Technologyreal-timeinformalcrowd-sourced
Type
Research Article
Information
Parasitology , Volume 139 , Issue 14: Dynamics of parasite distributions: modern analytical approaches , December 2012 , pp. 1843 - 1851
Copyright
Copyright © Cambridge University Press 2012

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Atkinson, J. A., Vallely, A., Fitzgerald, L., Whittaker, M. and Tanner, M. (2011). The architecture and effect of participation: a systematic review of community participation for communicable disease control and elimination. Implications for malaria elimination. Malaria Journal 10, 225.CrossRefGoogle ScholarPubMed
Brownstein, J. S., Freifeld, C. C. and Madoff, L. C. (2009). Digital disease detection - harnessing the Web for public health surveillance. New England Journal of Medicine 360, 2153–2155, 2157. Epub 2009 May 2157.CrossRefGoogle ScholarPubMed
Brownstein, J. S., Freifeld, C. C., Reis, B. Y. and Mandl, K. D. (2008). Surveillance Sans Frontieres: Internet-based emerging infectious disease intelligence and the HealthMap project. PLoS Medicine 5, e151.CrossRefGoogle ScholarPubMed
Chan, E. H., Brewer, T. F., Madoff, L. C., Pollack, M. P., Sonricker, A. L., Keller, M., Freifeld, C. C., Blench, M., Mawudeku, A. and Brownstein, J. S. (2010). Global capacity for emerging infectious disease detection. Proceedings of the National Academy of Sciences, USA 107, 21701–21706. Epub 22010 Nov 21729.CrossRefGoogle ScholarPubMed
Chan, E. H., Sahai, V., Conrad, C. and Brownstein, J. S. (2011). Using web search query data to monitor dengue epidemics: a new model for neglected tropical disease surveillance. PLoS Neglected Tropical Diseases 5, e1206. Epub 2011 May 1231.CrossRefGoogle ScholarPubMed
Chin, C. D., Laksanasopin, T., Cheung, Y. K., Steinmiller, D., Linder, V., Parsa, H., Wang, J., Moore, H., Rouse, R., Umviligihozo, G., Karita, E., Mwambarangwe, L., Braunstein, S. L., van de Wijgert, J., Sahabo, R., Justman, J. E., El-Sadr, W. and Sia, S. K. (2011). Microfluidics-based diagnostics of infectious diseases in the developing world. Nature Medicine 17, 1015–1019. doi: 1010.1038/nm.2408.CrossRefGoogle ScholarPubMed
Choko, A. T., Desmond, N., Webb, E. L., Chavula, K., Napierala-Mavedzenge, S., Gaydos, C. A., Makombe, S. D., Chunda, T., Squire, S. B., French, N., Mwapasa, V. and Corbett, E. L. (2011). The uptake and accuracy of oral kits for HIV self-testing in high HIV prevalence setting: a cross-sectional feasibility study in Blantyre, Malawi. PLoS Medicine 8, e1001102. Epub 1002011 Oct 1001104.CrossRefGoogle ScholarPubMed
Chunara, R., Chhaya, V., Bane, S., Mekaru, S., Chan, E., Freifeld, C. and Brownstein, J. (2012). Online reporting for malaria surveillance using micro-monetary incentives, in urban India 2010–2011. Malaria Journal 11.CrossRefGoogle ScholarPubMed
comScore (2011). The comScore 2010 Mobile Year in Review.Google Scholar
Fingerut, J., Zimmer, C. A. and Zimmer, R. K. (2003). Patterns and processes of larval emergence in an estuarine parasite system. Biological Bulletin 205, 110–120.CrossRefGoogle Scholar
Freifeld, C. C., Chunara, R., Mekaru, S. R., Chan, E. H., Kass-Hout, T., Ayala Iacucci, A. and Brownstein, J. S. (2010). Participatory epidemiology: use of mobile phones for community-based health reporting. PLoS Medicine 7, e1000376.CrossRefGoogle ScholarPubMed
Freifeld, C. C., Mandl, K. D., Reis, B. Y. and Brownstein, J. S. (2008). HealthMap: global infectious disease monitoring through automated classification and visualization of Internet media reports. Journal of American Medical Informatics Association 15, 150–157. doi: Epub 2007 Dec 20.CrossRefGoogle ScholarPubMed
Ginsberg, J., Mohebbi, M. H., Patel, R. S., Brammer, L., Smolinski, M. S. and Brilliant, L. (2009). Detecting influenza epidemics using search engine query data. Nature 457, 1012–1014.CrossRefGoogle ScholarPubMed
Google Flu Trends, Available at: http://www.google.org/flutrends/us/#US, Accessed: Nov. 14, 2011.Google Scholar
Gottstein, B., Pozio, E. and Nockler, K. (2009). Epidemiology, diagnosis, treatment, and control of trichinellosis. Clinical Microbiology Review 22, 127–145.CrossRefGoogle ScholarPubMed
Harris, I., Sharrock, W. W., Bain, L. M., Gray, K.-A., Bobogare, A., Boaz, L., Lilley, K., Krause, D., Vallely, A., Johnson, M.-L., Gatton, M. L., Shanks, G. D. and Cheng, Q. (2010). A large proportion of asymptomatic Plasmodium infections with low and sub-microscopic parasite densities in the low transmission setting of Temotu Province, Solomon Islands: challenges for malaria diagnostics in an elimination setting. Malaria Journal doi: 0.1186/475-2875-9-254.CrossRefGoogle Scholar
Heffernan, R., Mostashari, F., Das, D., Karpati, A., Kulldorff, M. and Weiss, D. (2004). Syndromic surveillance in public health practice, New York City. Emerging Infectious Diseases 10, 858–864.CrossRefGoogle ScholarPubMed
Heymann, D. L. and Rodier, G. R. (2001). Hot spots in a wired world: WHO surveillance of emerging and re-emerging infectious diseases. Lancet Infectious Diseases 1, 345–353.CrossRefGoogle Scholar
Integrated Regional Information Networks (2011). Cambodia: Using text messaging as weapon in malaria war. In IRIN Humanitarian News and Analysis.Google Scholar
IDC - Press Release, Available at: http://www.idc.com/about/viewpressrelease.jsp?containerId=prUS22689111&sectionId=null&elementId=null&pageType=SYNOPSIS, Accessed: Dec. 24, 2011.Google Scholar
ITU Statshot, Available at: http://www.itu.int/net/pressoffice/stats/2011/03/index.aspx, Accessed: November 12, 2011.Google Scholar
Jones, K. E., Patel, N. G., Levy, M. A., Storeygard, A., Balk, D., Gittleman, J. L. and Daszak, P. (2008). Global trends in emerging infectious diseases. Nature 451, 990–993.CrossRefGoogle ScholarPubMed
Loonsk, J. (2004). BioSense: a national initiative for early detection and quantification of public health emergencies. MMWR Morbidity and Mortallity Weekly Report 53(suppl), 53–55.Google Scholar
Mavandadi, S., Dimitrov, S., Feng, S., Yu, F., Sikora, U. et al. (2012). Distributed medical image analysis and diagnosis through Crowd-sourced games: a malaria case study. PLoS ONE 7(5): e37245. doi: 10.1371/journal.phone.0037245CrossRefGoogle Scholar
National Vector Borne Disease Control Programme, Directorate General of Health Services, Ministry of Health & Family Welfare, Available at: http://nvbdcp.gov.in/, Accessed: Nov. 24, 2011.Google Scholar
O'Meara, W. P., McKenzie, F. E., Magill, A. J., Forney, J. R., Permpanich, B., Lucas, C., Gasser, R. A. Jr. and Wongsrichanalai, C. (2005). Sources of variability in determining malaria parasite density by microscopy. American Journal of Tropical Medicine and Hygiene 73, 593–598.CrossRefGoogle ScholarPubMed
Olson, D. R., Paladini, M., Lober, W. B. and Buckeridge, D. L. (2011). Applying a New Model for Sharing Population Health Data to National Syndromic Influenza Surveillance: DiSTRIBuTE Project Proof of Concept, 2006 to 2009. PLoS Currents 3, RRN1251.CrossRefGoogle ScholarPubMed
Poulin, R. (1998). Evolutionary Ecology of Parasites: From Individuals to Communities, Chapman & Hall.Google Scholar
Sana, Available at: http://www.sanamobile.org/, Accessed: Nov. 21, 2011.Google Scholar
Short, N. J. and Norval, R. A. (1981). The seasonal activity of Rhipicephalus appendiculatus Neumann 1901 (Acarina: Ixodidae) in the highveld of Zimbabwe Rhodesia. Journal of Parasitology 67, 77–84.CrossRefGoogle ScholarPubMed
Signorini, A., Segre, A. and Polgreen, P. (2010). Using Twitter to Estimate H1N1 Activity. In International Society of Disease Surveillance 9th Annual Conference Park City, Utah, USA.Google Scholar
UN Global Pulse, Available at: http://www.unglobalpulse.org, Accessed: April 30, 2012.Google Scholar
Varia, M., Wilson, S., Sarwal, S., McGeer, A., Gournis, E., Galanis, E. and Henry, B. (2003). Investigation of a nosocomial outbreak of severe acute respiratory syndrome (SARS) in Toronto, Canada. Canadian Medical Association Journal 169, 285–292.Google ScholarPubMed
Zheng, G., Lee, S. A., Antebi, Y., Elowitz, M. B. and Yang, C. (2011). The ePetri dish, an on-chip cell imaging platform based on subpixel perspective sweeping microscopy (SPSM). Proceedings of the National Academy of Sciences, USA 108, 16889–16894. Epub 12011 Oct 16883.CrossRefGoogle ScholarPubMed
Zhu, H., Yaglidere, O., Su, T. W., Tseng, D. and Ozcan, A. (2011). Cost-effective and compact wide-field fluorescent imaging on a cell-phone. Lab on a Chip 11, 315–322. Epub 2010 Nov 2019.CrossRefGoogle ScholarPubMed