title: [New method for determining endemic levels].
abstract: OBJECTIVE
  Design an instrument for determining endemic levels or ranges using simple calculations; identify and estimate the parameters related to the dynamic transmission of communicable diseases.
  
  
  METHODS
  The parameters for establishing a theoretical curve of expected incidence based on the logistic growth model were identified. The parameters were estimated by nonlinear regression based on the cumulative incidence data from the previous five years. The weekly cumulative incidence of cases of influenza-like illness in Argentina in 2009 was used as an example. It was compared to the 2004-2008 case series in order to determine the cumulative and non-cumulative endemic levels.
  
  
  RESULTS
  According to the cumulative endemic levels identified, the country entered the outbreak area in week 2. The data from previous years showed that the maximum expected number of cases or carrying capacity (K) was 1 090 660. When the non-cumulative levels were considered, the outbreak was present in 34 out of 51 weeks. A range of 1.05 to 1.13 was estimated for the basic reproductive rate (R0) in the non-epidemic period from 2004-2008.
  
  
  CONCLUSIONS
  The new method facilitated the determination of endemic levels using a simple procedure with the identification of parameters that are important for transmission. Although it has limitations such as the fact that the equation used is more appropriate for evaluating diseases with a pronounced annual cycle and less accurate for cycles of less than 1 year, it can be considered a valuable alternative method for determining endemic ranges and a new contribution to the study of epidemic outbreaks at local health surveillance levels.
authors.affiliation: Departamento de Epidemiología, Ministerio de Salud de Tierra del Fuego, Antártida e Islas del Atlántico Sur, Argentina. porellano@gmail.com
authors.forename: Pablo Wenceslao
authors.initials: PW
authors.lastname: Orellano
authors.forename: Julieta Itatí
authors.initials: JI
authors.lastname: Reynoso
mesh_terms: D019353:Endemic Diseases / Q000706:statistics & numerical data*; D006801:Humans; D015233:Models, Statistical