title: A SARIMA forecasting model to predict the number of cases of dengue in Campinas, State of São Paulo, Brazil.
abstract: BACKGROUND
  Forecasting dengue cases in a population by using time-series models can provide useful information that can be used to facilitate the planning of public health interventions. The objective of this article was to develop a forecasting model for dengue incidence in Campinas, southeast Brazil, considering the Box-Jenkins modeling approach.
  
  
  METHODS
  The forecasting model for dengue incidence was performed with R software using the seasonal autoregressive integrated moving average (SARIMA) model. We fitted a model based on the reported monthly incidence of dengue from 1998 to 2008, and we validated the model using the data collected between January and December of 2009.
  
  
  RESULTS
  SARIMA (2,1,2) (1,1,1)12 was the model with the best fit for data. This model indicated that the number of dengue cases in a given month can be estimated by the number of dengue cases occurring one, two and twelve months prior. The predicted values for 2009 are relatively close to the observed values.
  
  
  CONCLUSIONS
  The results of this article indicate that SARIMA models are useful tools for monitoring dengue incidence. We also observe that the SARIMA model is capable of representing with relative precision the number of cases in a next year.
authors.affiliation: Departamento de Medicina Social, Faculdade de Medicina de Ribeirão Preto, Universidade de São Paulo, Ribeirão Preto, SP, Brasil.
authors.forename: Edson Zangiacomi
authors.initials: EZ
authors.lastname: Martinez
authors.forename: Elisângela Aparecida Soares da
authors.initials: EA
authors.lastname: Silva
authors.forename: Amaury Lelis Dal
authors.initials: AL
authors.lastname: Fabbro
mesh_terms: D001938:Brazil / Q000453:epidemiology; D003715:Dengue / Q000453:epidemiology*; D005544:Forecasting / Q000379:methods*; D006801:Humans; D015994:Incidence; D015233:Models, Statistical