Infectious diseases investment decision evaluation algorithm: a quantitative algorithm for prioritization of naturally occurring infectious disease threats to the U.S. military.
Identification of the most significant infectious disease threats to deployed U.S. military forces is important for developing and maintaining an appropriate countermeasure research and development portfolio. We describe a quantitative algorithmic method (the Infectious Diseases Investment Decision...
| Publicado en: | Military Medicine Vol. 173; no. 2; pp. 174 - 182 |
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| Autores principales: | , , , , , , , , , , , , , , , , , |
| Formato: | Journal Article |
| Publicado: |
Oxford University Press / USA
Feb2008
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=105901383&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 105901383 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 00264075 4DV jtl: Military Medicine issn: 00264075 maglogo: N pubinfo: dt: Feb2008 vid: 173 iid: 2 pid: 622 pub: Oxford University Press / USA artinfo: ui: 105901383 105901383 NLM18333494 2009810080 10.7205/milmed.173.2.174 NLM18333494 105901383 ppf: 174 ppct: 8 formats: fmt: @attributes: type: P tig: atl: Infectious diseases investment decision evaluation algorithm: a quantitative algorithm for prioritization of naturally occurring infectious disease threats to the U.S. military. aug: au: Burnette WN Hoke CH Jr. Scovill J Clark K Abrams J Kitchen LW Hanson K Palys TJ Vaughn DW Burnette, W Neal Hoke, Charles H Jr Scovill, John Clark, Kathryn Abrams, Jerry Kitchen, Lynn W Hanson, Kevin Palys, Thomas J Vaughn, David W affil: Molecular Pharmaceutics Corp., Westlake Village, CA 91362-5280, USA sug: subj: Algorithms Decision Making Health and Welfare Planning Statistics and Numerical Data Infection Control Military Personnel United States ab: Identification of the most significant infectious disease threats to deployed U.S. military forces is important for developing and maintaining an appropriate countermeasure research and development portfolio. We describe a quantitative algorithmic method (the Infectious Diseases Investment Decision Evaluation Algorithm) that uses Armed Forces Medical Intelligence Center information to determine which naturally occurring pathogens pose the most substantial threat to U.S. deployed forces in the absence of specific mitigating countermeasures. The Infectious Diseases Investment Decision Evaluation Algorithm scores the relative importance of various diseases by taking into account both their severity and the likelihood of infection on a country-by-country basis. In such an analysis, the top three endemic disease threats to U.S. deployed forces are malaria, bacteria-caused diarrhea, and dengue fever. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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