A SARIMA forecasting model to predict the number of cases of dengue in Campinas, State of São Paulo, Brazil

Abstract

Introduction: 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.

Forecasting tourist in-flow in South East Asia: A case of Singapore

“…periodic pattern) and are non-stationary time series. The seasonal ARIMA (SARIMA) model is capable of absorbing this seasonality behavior in the time series and can be written as (Pankratz, 1983): The variable e t is commonly referred to as white noise in time series analysis (Martinez et al, 2011) and cannot easily be explained by the model. Considering our case, the time series of monthly tourist arrivals, this white noise (e t ) can vary, for example, due to an effect of weather variables (e.g.…”

Monthly Nigerian Interbank Call Rates Modeling by Seasonal Box-Jenkins Approach

“…This shows that the series is 12-monthly seasonal and there are seasonal autoregressive and moving average components of order one each. Hereby proposed are the Sarima models of orders (1, 1, 1)x(1, 1, 1) 12 , (1, 1, 2)x(1, 1, 1) 12 , (2, 1, 1)x(1, 1, 1) 12 and (2, 1, 2)x(1, 1, 1) 12 , with AIC values of 5.14, 5.37, 5.10 and 5.43, respectively. The best model, with the least AIC, is the (2, 1, 1)x(1, 1, 1) 12 model.…”

“…For instance, Etuk(2012a) observed that daily Nigeria Naira -US Dollar exchange rates tended to have peaks on Fridays and troughs on Mondays. Martinez et al(2011) observed that the number of reported cases of dengue in Campinas, State of Sao Paulo, Brazil tended to show a maximum in the rainy season and a minimum in the dry season. Such seasonal series may be modeled using a seasonal BoxJenkins approach.…”

Preditive Models And Health Sciences: A Brief Analysis

“…These models have extreme relevance to epidemiology and, subsequently, to Public Health [32], once they allow to evaluate the individual characteristics of living beings and its correlation with pathologies in a same space-time, similarly as in the case of the study about epidemiological course of Dengue in Campinas city, Brazilian southeast [33]. Therefore, 1.…”