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Comput Math Methods Med. 2013;2013:213206. doi: 10.1155/2013/213206. Epub 2013 Aug 28.
Detection of Severe Respiratory Disease Epidemic Outbreaks by CUSUM-Based Overcrowd-Severe-Respiratory-Disease-Index Model.
Polanco C, Casta??n-Gonz?lez JA, Mac?as AE, Samaniego JL, Buhse T, Villanueva-Mart?nez S.
Source
Facultad de Ciencias de la Salud, Universidad An?huac, Avenida Universidad An?huac No. 46, Col. Lomas An?huac, 52786 Huixquilucan, MEX, Mexico ; Subdirecci?n de Epidemiolog?a Hospitalaria y Control de Calidad de la Atenci?n M?dica, Instituto Nacional de Ciencias M?dicas y Nutrici?n Salvador Zubir?n, Vasco de Quiroga 15, Piso 4, Col. Secci?n XVI, DF, M?xico 14000, Mexico.
Abstract
A severe respiratory disease epidemic outbreak correlates with a high demand of specific supplies and specialized personnel to hold it back in a wide region or set of regions; these supplies would be beds, storage areas, hemodynamic monitors, and mechanical ventilators, as well as physicians, respiratory technicians, and specialized nurses. We describe an online cumulative sum based model named Overcrowd-Severe-Respiratory-Disease-Index based on the Modified Overcrowd Index that simultaneously monitors and informs the demand of those supplies and personnel in a healthcare network generating early warnings of severe respiratory disease epidemic outbreaks through the interpretation of such variables. A post hoc historical archive is generated, helping physicians in charge to improve the transit and future allocation of supplies in the entire hospital network during the outbreak. The model was thoroughly verified in a virtual scenario, generating multiple epidemic outbreaks in a 6-year span for a 13-hospital network. When it was superimposed over the H1N1 influenza outbreak census (2008-2010) taken by the National Institute of Medical Sciences and Nutrition Salvador Zubiran in Mexico City, it showed that it is an effective algorithm to notify early warnings of severe respiratory disease epidemic outbreaks with a minimal rate of false alerts.
PMID:
24069063
[PubMed - in process]
Free full text
http://www.ncbi.nlm.nih.gov/pubmed/24069063
Detection of Severe Respiratory Disease Epidemic Outbreaks by CUSUM-Based Overcrowd-Severe-Respiratory-Disease-Index Model.
Polanco C, Casta??n-Gonz?lez JA, Mac?as AE, Samaniego JL, Buhse T, Villanueva-Mart?nez S.
Source
Facultad de Ciencias de la Salud, Universidad An?huac, Avenida Universidad An?huac No. 46, Col. Lomas An?huac, 52786 Huixquilucan, MEX, Mexico ; Subdirecci?n de Epidemiolog?a Hospitalaria y Control de Calidad de la Atenci?n M?dica, Instituto Nacional de Ciencias M?dicas y Nutrici?n Salvador Zubir?n, Vasco de Quiroga 15, Piso 4, Col. Secci?n XVI, DF, M?xico 14000, Mexico.
Abstract
A severe respiratory disease epidemic outbreak correlates with a high demand of specific supplies and specialized personnel to hold it back in a wide region or set of regions; these supplies would be beds, storage areas, hemodynamic monitors, and mechanical ventilators, as well as physicians, respiratory technicians, and specialized nurses. We describe an online cumulative sum based model named Overcrowd-Severe-Respiratory-Disease-Index based on the Modified Overcrowd Index that simultaneously monitors and informs the demand of those supplies and personnel in a healthcare network generating early warnings of severe respiratory disease epidemic outbreaks through the interpretation of such variables. A post hoc historical archive is generated, helping physicians in charge to improve the transit and future allocation of supplies in the entire hospital network during the outbreak. The model was thoroughly verified in a virtual scenario, generating multiple epidemic outbreaks in a 6-year span for a 13-hospital network. When it was superimposed over the H1N1 influenza outbreak census (2008-2010) taken by the National Institute of Medical Sciences and Nutrition Salvador Zubiran in Mexico City, it showed that it is an effective algorithm to notify early warnings of severe respiratory disease epidemic outbreaks with a minimal rate of false alerts.
PMID:
24069063
[PubMed - in process]
Free full text
http://www.ncbi.nlm.nih.gov/pubmed/24069063