Forecasting Dengue Haemorrhagic Fever Cases in Southern Thailand using ARIMA Models (original) (raw)
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International journal of collaborative research on internal medicine and public health, 2019
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Model Forecasting Development for Dengue Fever Incidence in Surabaya City Using Time Series Analysis
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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 er...
Temporal patterns and forecast of dengue infection in Northeastern Thailand
The Southeast Asian journal of tropical medicine and public health, 2008
This study aimed to determine temporal patterns and develop a forecasting model for dengue incidence in northeastern Thailand. Reported cases were obtained from the Thailand national surveillance system. The temporal patterns were displayed by plotting monthly rates, the seasonal-trend decomposition procedure based on loess (STL) was performed using R 2.2.1 software, and the trend was assessed using Poisson regression. The forecasting model for dengue incidence was performed in R 2.2.1 and Intercooled Stata 9.2 using the seasonal Autoregressive Integrated Moving Average (ARIMA) model. The model was evaluated by comparing predicted versus actual rates of dengue for 1996 to 2005 and used to forecast monthly rates during January to December 2006. The results reveal that epidemics occurred every two years, with approximately three years per epidemic, and that the next epidemic will take place in 2006 to 2008. It was found that if a month increased, the rate ratio for dengue infection de...
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This study aimed at developing a predicting model on the incidence rate of dengue fever in four locations of Thailand--i.e. the northern region, Chiang Rai province, the north-eastern region and Sisaket province--using time series analysis. Seasonal Autoregressive Integrated Moving Average (SARIMA) model was performed using data on monthly incidence rate of dengue fever from 1981 to 2009, and validated using the monthly rate collected for the period January 2010 to October 2011. The results show that the SARIMA(1,0,1)(0,1,1)12 model is the most suitable model in all locations. The model for all locations indicated that the predicted dengue incidence rate and the actual dengue incidence rate matched reasonably well. The model was further validated by the Portmanteau test with no significant autocorrelation between residuals at different lag times. Our findings indicate that SARIMA model is a useful tool for monitoring dengue incidence in Thailand. Furthermore, this model can be appli...
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Health Science Reports, 2024
Backgrounds and Aims Dengue fever has been a continued public health problem in Bangladesh, with a recent surge in cases. The aim of this study was to train ARIMA and SARIMA models for time series analysis on the monthly prevalence of dengue in Bangladesh and to use these models to forecast the dengue prevalence for the next 12 months. Methods This secondary data-based study utilizes AutoRegressive Integrated Moving Average (ARIMA) and Seasonal AutoRegressive Integrated Moving Average (SARIMA) models to forecast dengue prevalence in Bangladesh. Data was sourced from the Institute of Epidemiology Disease Control and Research (IEDCR) and the Directorate General of Health Services (DGHS). STROBE Guideline for observational studies was followed for reporting this study. Results The ARIMA (1,1,1) and SARIMA (1,1,2) models were identified as the best-performing models. The forecasts indicate a steady dengue prevalence for 2024 according to ARIMA, while SARIMA predicts significant fluctuations. It was observed that ARIMA (1,1,1) and SARIMA (1,2,2) (1,1,2)12 were the most suitable models for prediction of dengue prevalence. Conclusion These models offer valuable insights for healthcare planning and resource allocation, although external factors and complex interactions must be considered. Dengue prevalence is expected to rise in future in Bangladesh.
Forecasting the incidence of dengue in Bangladesh-Application of time series model
Health Science Reports, 2022
Background: Dengue is an alarming public health concern in terms of its preventive and curative measures among people in Bangladesh; moreover, its sudden outbreak created a lot of suffering among people in 2018. Considering the greater burden of disease in larger epidemic years and the difficulty in understanding current and future needs, it is highly needed to address early warning systems to control epidemics from the earliest. Objective: The study objective was to select the most appropriate model for dengue incidence and using the selected model, the authors forecast the future dengue outbreak in Bangladesh. Methods and Materials: This study considered a secondary data set of monthly dengue occurrences over the period of January 2008 to January 2020. Initially, the authors found the suitable model from Autoregressive Integrated Moving Average (ARIMA), Error, Trend, Seasonal (ETS) and Trigonometric seasonality, Box-Cox transformation, ARMA errors, Trend and Seasonal (TBATS) models with the help of selected model selection criteria and finally employing the selected model make forecasting of dengue incidences in Bangladesh. Results: Among ARIMA, ETS, and TBATS models, the ARIMA model performs better than others. The Box-Jenkin's procedure is applicable here and it is found that the best-selected model to forecast the dengue outbreak in the context of Bangladesh is ARIMA (2,1,2). Conclusion: Before establishing a comprehensive plan for future combating strategies, it is vital to understand the future scenario of dengue occurrence. With this in mind, the authors aimed to select an appropriate model that might predict dengue fever outbreaks in Bangladesh. The findings revealed that dengue fever is expected to become more frequent in the future. The authors believe that the study findings will be helpful to take early initiatives to combat future dengue outbreaks.
Time Series Analysis on Admission Rates of Dengue In Medical College Hospital
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To develop a prediction model for dengue fever/dengue haemorrhagic (DF/DHF) using time series data over the past one year and to forecast monthly DF/ DHF incidence for the year 2018. Autoregressive integrated moving average (ARIMA) model was used for statistical modelling along with SPSS software. The data has been collected from January 2017 to December 2017 from a tertiary care hospital, mandya District, the reported DF cases showed a seasonal patterns. ARIMA (2, 0, 0) model has been found to be suitable model with least Normalized Bayesian Information Criteria ( BIC) of 8.493 and Mean Absolute Percent Error ( MAPE) of 125.935. The model explained 60.8% of the variance of the series (stationary R-squared). The forecasted value for the year 2018 showed a seasonal peak in the month of July with an estimated case. Application of ARIMA model may be useful for forecast of cases and impending outbreaks of DF/ DHF and other infectious diseases, which exhibit seasonal pattern.
Time Series Analysis of Dengue Incidence in Rio de Janeiro, Brazil
The American Journal of Tropical Medicine and Hygiene, 2008
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 ס 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.