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primary topic: Model Forecasting Development for Dengue Fever Incidence in Surabaya City Using Time Series Analysis

Model Forecasting Development for Dengue Fever Incidence in Surabaya City Using Time Series Analysis
type: AcademicArticle
type: Article
abstract: Dengue hemorrhagic fever (DHF) is one of the most widespread and deadly diseases in several parts of Indonesia. An accurate forecast-based model is required to reduce the incidence rate of this disease. Time-series methods such as autoregressive integrated moving average (ARIMA) models are used in epidemiology as statistical tools to study and forecast DHF and other infectious diseases. The present study attempted to forecast the monthly confirmed DHF cases via a time-series approach. The ARIMA, seasonal ARIMA (SARIMA), and long short-term memory (LSTM) models were compared to select the most accurate forecasting method for the deadly disease. The data were obtained from the Surabaya Health Office covering January 2014 to December 2016. The data were partitioned into the training and testing sets. The best forecasting model was selected based on the lowest values of accuracy metrics such as the root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). The findings demonstrated that the SARIMA (2,1,1) (1,0,0) model was able to forecast the DHF outbreaks in Surabaya City compared to the ARIMA (2,1,1) and LSTM models. We further forecasted the DHF cases for 12 month horizons starting from January 2017 to December 2017 using the SARIMA (2,1,1) (1,0,0), ARIMA (2,1,1), and LSTM models. The results revealed that the SARIMA (2,1,1) (1,0,0) model outperformed the ARIMA (2,1,1) and LSTM models based on the goodness-of-fit measure. The results showed significant seasonal outbreaks of DHF, particularly from March to September. The highest cases observed in May suggested a significant seasonal correlation between DHF and air temperature. This research is the first attempt to analyze the time-series model for DHF cases in Surabaya City and forecast future outbreaks. The findings could help policymakers and public health specialists develop efficient public health strategies to detect and control the disease, especially in the early phase…
date: 2022
volume: 10
issue: 11
is part of: Processes
status: peerReviewed
status: published
author list: authors
type: EPrint
type: ArticleEPrint
is part of: repository

MDPI
type: Organization

Processes
type: Collection
issn: 22279717

M.B. Qomaruddin
type: Person
given name: M.B.
family name: Qomaruddin

A.A. Suleiman
type: Person
given name: A.A.
family name: Suleiman

R. Indawati
type: Person
given name: R.
family name: Indawati

M. Othman
type: Person
given name: M.
family name: Othman

R. Sokkalingam
type: Person
given name: R.
family name: Sokkalingam

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primary topic: Model Forecasting Development for Dengue Fever Incidence in Surabaya City Using Time Series Analysis