Future directions in analytics for infectious disease intelligence: Toward an integrated warning system for emerging pathogens
- PMID: 27170620
- PMCID: PMC5278609
- DOI: 10.15252/embr.201642534
Future directions in analytics for infectious disease intelligence: Toward an integrated warning system for emerging pathogens
Abstract
Anticipating the emergence of infectious diseases would be more effective in reducing their toll, rather than reacting to an outbreak. Machine learning and simulation tools that use environmental, epidemiological and molecular data could help manage and analyse the flow or risks, and inform appropriate actions by public health authorities.
Figures
References
-
- World Economic Forum . Global Risk Report 2016. http://wef.ch/1QfL09i
-
- High‐level Panel on the Global Response to Health Crises (2016) Protecting humanity from future health crises. United Nations, New York, NY, USA. http://www.un.org/News/dh/infocus/HLP/2016-02-05_Final_Report_Global_Res...
-
- Noonan‐Wright EK, Opperman TS, Finney MA, Zimmerman GT, Seli RC, Elenz LM, Calkin DE, Fiedler JR (2011) Developing the US Wildland Fire Decision Support System. J Combust 2011: e168473
-
- Han BA, Kramer AM, Drake JM (2016) Global patterns of zoonotic disease in mammals. Trends Parasitol doi: 10.1016/j.pt.2016.04.007 - DOI - PMC - PubMed
Publication types
MeSH terms
Grants and funding
LinkOut - more resources
Full Text Sources
Other Literature Sources
