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Employing seasonal autoregressive integrated moving average forecasting model to predict the number of dengue cases in Mumbai

Sujata Shirish Pol, Shekahr S Rajderkar, Seema S Bansode Gokhe, Kamlesh J Suthar.

Abstract
Background: Dengue control suffers challenges such as the absence of specific treatment and lack of vaccine. Forecasting of dengue would facilitate allocation of resources that are needed for such activities. The seasonal autoregressive integrated moving average (SARIMA) is the mathematical model, which provides the estimated monthly figures for the given period.

Objectives: The objectives of the study were to select the best prediction model for dengue fever (DF) by time series data over the past 13 years in the Mumbai city and to forecast monthly dengue incidence for 2019.

Materials and Methods: Retrospective study design was employed at epidemiology cell, Mumbai – Integrated Disease Surveillance Project (IDSP). The reported DF/dengue hemorrhagic fever cases during January 2006–December 2018 were mobilized from epidemiology cell of the city. Data were recorded on Excel sheet. The SARIMA model was applied to the data with R software.

Results: The cases showed a form with a seasonal difference. SARIMA (1, 1, 2)(0, 1, 1) model had the highest Akaike information criteria (AIC) of 1433.18 and mean absolute percentage error of 43.75 and performed to be the best model. Adequacy of the model was established through the Ljung–Box test, which showed no substantial correlation among residuals at different lag times. Seasonal peak is estimated in the month of September of 2019 with 323 (95% confidence interval: [196.56, 449.92]) cases followed by 222 (95% confidence interval: [95.58, 348.93]) in October.

Conclusions: The function of the SARIMA model may be useful for a forecast of cases and impending outbreaks of dengue.

Key words: Dengue; Forecasting; Prediction Model; Seasonal Autoregressive Integrated Moving Averages Model


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