Epidemiological monitoring for emerging infectious diseases
|Epidemiological monitoring for emerging infectious diseases|
|Published in||Proceedings of SPIE - The International Society for Optical Engineering|
|Keyword(s)||Unknown (Extra: Communications networks, Digital tools, Disease outbreaks, Filtering mechanism, Health organizations, Home land security, Infectious disease, Information overloads, Internet based, Rapid analysis, Real time, Self organizing, Social Networks, Surveillance information, Two way communications, Wikipedia, Digital devices, Health, Information dissemination, Internet, Ontology, Security systems, Sensors, Social sciences computing, Telecommunication networks, Disease control)|
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Epidemiological monitoring for emerging infectious diseases is a 2010 conference paper written in English by Greene M. and published in Proceedings of SPIE - The International Society for Optical Engineering.
The Homeland Security News Wire has been reporting on new ways to fight epidemics using digital tools such as iPhone, social networks, Wikipedia, and other Internet sites. Instant two-way communication now gives consumers the ability to complement official reports on emerging infectious diseases from health authorities. However, there is increasing concern that these communications networks could open the door to mass panic from unreliable or false reports. There is thus an urgent need to ensure that epidemiological monitoring for emerging infectious diseases gives health authorities the capability to identify, analyze, and report disease outbreaks in as timely and efficient a manner as possible. One of the dilemmas in the global dissemination of information on infectious diseases is the possibility that information overload will create inefficiencies as the volume of Internet-based surveillance information increases. What is needed is a filtering mechanism that will retrieve relevant information for further analysis by epidemiologists, laboratories, and other health organizations so they are not overwhelmed with irrelevant information and will be able to respond quickly. This paper introduces a self-organizing ontology that could be used as a filtering mechanism to increase relevance and allow rapid analysis of disease outbreaks as they evolve in real time.
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