Time Series Analysis of Dengue Incidence in Rio de Janeiro, Brazil

Abstract

We use the Box-Jenkins approach to fit an autoregressive integrated moving average (ARIMA) model to dengue incidence in Rio de Janeiro, Brazil, from 1997 to 2004. We find that the number of dengue cases in a month can be estimated by the number of dengue cases occurring one, two, and twelve months prior. We use our fitted model to predict dengue incidence for the year 2005 when two alternative approaches are used: 12-steps ahead versus 1-step ahead. Our calculations show that the 1-step ahead approach for predicting dengue incidence provides significantly more accurate predictions (P value=0.002, Wilcoxon signed-ranks test) than the 12-steps ahead approach. We also explore the predictive power of alternative ARIMA models incorporating climate variables as external regressors. Our findings indicate that ARIMA models are useful tools for monitoring dengue incidence in Rio de Janeiro. Furthermore, these models can be applied to surveillance data for predicting trends in dengue incidence.

Temporal analysis of the relationship between dengue and meteorological variables in the city of Rio de Janeiro, Brazil, 2001-2009

“…The variables used in these studies have been temperature, precipitation, relative humidity, wind velocity, and El Niño Southern Oscillation (ENSO) 2,3,4,5,6,7,8 . Studies have also evaluated dengue's relationship with socio-demographic and environmental variables in the municipality of Rio de Janeiro, Brazil, with a view to examining the effect of seasonal and annual factors on increases and decreases in dengue cases, as well as to make predictions 9,10,11,12 .…”

Application of an Autoregressive Integrated Moving Average Model for Predicting the Incidence of Hemorrhagic Fever with Renal Syndrome

“…The ARIMA model was first proposed in 1976 and ARIMA time series intervention analysis is widely used for prediction and early warning analysis of infectious diseases. [4][5][6] This purpose of this study was to fit ARIMA models and predict the HFRS epidemic trend by using Statistical Package for the Social Sciences (SPSS) version 13.0 (International Business Machines Corporation, Armonk, NY) correlation modules. Our study was based on HFRS epidemic data from the Hebei Province, China, where it could provide a basis for HFRS prevention and control.…”

Using an Autoregressive Integrated Moving Average Model to Predict the Incidence of Hemorrhagic Fever with Renal Syndrome in Zibo, China, 2004–2014

“…Autoregressive integrated moving average (ARIMA) models, which take into account changing trends, periodic changes, and random disturbances in time series, are very useful in modeling the temporal dependence structure of a time series (15). In epidemiology, ARIMA models have been successfully applied to predict the incidence of infectious diseases, such as HIV (16), influenza (17,18), malaria incidence (19), and others (20)(21)(22).…”