About: A Systematic Review of Risk Analysis Tools for Differentiating Unnatural From Natural Epidemics

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An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
title	A Systematic Review of Risk Analysis Tools for Differentiating Unnatural From Natural Epidemics
Creator	Chen, Xin Macintyre, Raina Chughtai, Ahmad Abrar, ;
topic	covid:63525d422f2c2925aec01fea328c8f56b382e1e4#this
source	Medline; PMC
abstract	Introduction: In the era of genetic engineering of pathogens, distinguishing unnatural epidemics from natural ones is a challenge. Successful identification of unnatural infectious disease events can assist in rapid response, which relies on a sensitive risk assessment tool used for the early detection of deliberate attacks (i.e., bioterrorism). Methods: A systematic review was conducted according to the outline of Preferred Reporting Items for Systematic Reviews. Published papers related to the detection of unnatural diseases were searched in MEDLINE (January 1927–April 2016), EMBASE (January 1937–March 2016), and Web of Science (January 1978–March 2016). Full texts were reviewed for the selection of studies on scoring systems specially designed to discern between unnatural and natural outbreaks. Results: A total of 1,753 papers were reviewed, of which we identified the following five scoring systems specifically designed for detecting unnatural outbreaks: (1) the Grunow–Finke epidemiological assessment tool, (2) potential epidemiological clues to a deliberate epidemic, (3) bioterrorism risk assessment scoring, (4) and (5) two modified scoring systems based on (3). Various criteria ranging from the information on perpetrators, type of agents, spatial distribution, and intelligence of deliberate release were involved. Of these systems, the Grunow–Finke assessment tool remains the most widely used, but has low sensitivity for correctly identifying unnatural epidemics when tested against actual historical outbreaks. Others were applied into a few scenarios but provided different perspectives for bioterrorism detection and bio-preparedness. Conclusion: There are few risk assessment tools for differentiating unnatural from natural epidemics. These tools are increasingly necessary and valuable, but improved scoring systems with higher sensitivity, specificity, timeliness, and wider application to biological attacks must be developed.
has issue date	2017-01-01(xsd:dateTime)
bibo:doi	10.7205/milmed-d-17-00090
bibo:pmid	29087849
has license	no-cc
sha1sum (hex)	63525d422f2c2925aec01fea328c8f56b382e1e4
schema:url	https://doi.org/10.7205/milmed-d-17-00090
resource representing a document's title	A Systematic Review of Risk Analysis Tools for Differentiating Unnatural From Natural Epidemics
has PubMed Central identifier	PMC7107703
has PubMed identifier	29087849
schema:publication	Mil Med
resource representing a document's body	covid:63525d422f2c2925aec01fea328c8f56b382e1e4#body_text
is http://vocab.deri.ie/void#inDataset of	https://covidontheweb.inria.fr:4443/about/id/http/ns.inria.fr/covid19/63525d422f2c2925aec01fea328c8f56b382e1e4
is schema:about of	named entity 'OF RISK' named entity 'RISK ANALYSIS' named entity 'identifying' named entity 'identification' named entity 'Methods' »more»

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