Detection of Severe Respiratory Disease Epidemic Outbreaks by CUSUM-Based Overcrowd-Severe-Respiratory-Disease-Index Model

• Published in: Computational and mathematical methods in medicine 2013-01, Vol.2013 (2013), p.1-9
• Main Authors: Macías, Alejandro E., Polanco, Carlos, Villanueva-Martínez, Sebastián, Buhse, Thomas, Samaniego, José Lino, Castañón-González, Jorge Alberto
• Format: Article
• Language: English
• Subjects: Algorithms; Epidemics - statistics & numerical data; Health Resources - statistics & numerical data; Humans; Influenza A Virus, H1N1 Subtype; Influenza, Human - epidemiology; Influenza, Human - therapy; Mexico - epidemiology; Models, Statistical; Online Systems; Public Health Surveillance - methods; Respiratory Distress Syndrome, Adult - epidemiology; Respiratory Distress Syndrome, Adult - therapy; Respiratory Tract Diseases - epidemiology
• ISSN: 1748-670X; 1748-6718
• DOI: 10.1155/2013/213206

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.