A comparison of SARIMA and LSTM in forecasting dengue hemorrhagic fever incidence in Jambi, Indonesia

Khaira, Ulfa; Utomo, Pradita Eko Prasetyo; Aryani, Reni; Weni, Indra

doi:10.1088/1742-6596/1566/1/012054

Cited by 4 publications

(5 citation statements)

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“…Similarly, Nadda et al [20] found that the LSTM model with a fully connected neural network had better accuracy than the cross-entropy loss function for detecting dengue fever using symptom text. Khaira et al [21] also compared the performance of an LSTM model with a SARIMA model for forecasting dengue incidence in Jambi, Indonesia, and found that both models performed relatively well.…”

Section: Literature Reviewmentioning

confidence: 99%

A Deep Learning Approach for Dengue Fever Prediction in Malaysia Using LSTM with Spatial Attention

Majeed

Shafri

Zulkafli

et al. 2023

IJERPH

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This research aims to predict dengue fever cases in Malaysia using machine learning techniques. A dataset consisting of weekly dengue cases at the state level in Malaysia from 2010 to 2016 was obtained from the Malaysia Open Data website and includes variables such as climate, geography, and demographics. Six different long short-term memory (LSTM) models were developed and compared for dengue prediction in Malaysia: LSTM, stacked LSTM (S-LSTM), LSTM with temporal attention (TA-LSTM), S-LSTM with temporal attention (STA-LSTM), LSTM with spatial attention (SA-LSTM), and S-LSTM with spatial attention (SSA-LSTM). The models were trained and evaluated on a dataset of monthly dengue cases in Malaysia from 2010 to 2016, with the task of predicting the number of dengue cases based on various climate, topographic, demographic, and land-use variables. The SSA-LSTM model, which used both stacked LSTM layers and spatial attention, performed the best, with an average root mean squared error (RMSE) of 3.17 across all lookback periods. When compared to three benchmark models (SVM, DT, ANN), the SSA-LSTM model had a significantly lower average RMSE. The SSA-LSTM model also performed well in different states in Malaysia, with RMSE values ranging from 2.91 to 4.55. When comparing temporal and spatial attention models, the spatial models generally performed better at predicting dengue cases. The SSA-LSTM model was also found to perform well at different prediction horizons, with the lowest RMSE at 4- and 5-month lookback periods. Overall, the results suggest that the SSA-LSTM model is effective at predicting dengue cases in Malaysia.

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Section: Literature Reviewmentioning

confidence: 99%

A Deep Learning Approach for Dengue Fever Prediction in Malaysia Using LSTM with Spatial Attention

Majeed

Shafri

Zulkafli

et al. 2023

IJERPH

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“…For instance, recurrent neural networks (RNN) with long-short-term memory (LSTM) type, have been applied to forecast the incidence in 20 Chinese cities from climatic variables and historical cases [24]. Furthermore, hybrid methods using both traditional statistical and LSTM have been studied, evaluating their ability to reproduce epidemic events seen for dengue, as well as predict future incidence [25]. Also, implementations of stacking models have been made, where stacking involves averaging predictions of multiple models using a weighted average, these models outperform the Bayesian moving average [26].…”

Section: Modeling the Dengue Incidence As A Time Seriesmentioning

confidence: 99%

Assessing the Effect of Climate Variables on the Incidence of Dengue Cases in the Metropolitan Region of Panama City

Valencia

Díaz

Pascale

et al. 2021

IJERPH

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The present analysis uses the data of confirmed incidence of dengue cases in the metropolitan region of Panama from 1999 to 2017 and climatic variables (air temperature, precipitation, and relative humidity) during the same period to determine if there exists a correlation between these variables. In addition, we compare the predictive performance of two regression models (SARIMA, SARIMAX) and a recurrent neural network model (RNN-LSTM) on the dengue incidence series. For this data from 1999–2014 was used for training and the three subsequent years of incidence 2015–2017 were used for prediction. The results show a correlation coefficient between the climatic variables and the incidence of dengue were low but statistical significant. The RMSE and MAPE obtained for the SARIMAX and RNN-LSTM models were 25.76, 108.44 and 26.16, 59.68, which suggest that any of these models can be used to predict new outbreaks. Although, it can be said that there is a limited role of climatic variables in the outputs the models. The value of this work is that it helps understand the behaviour of cases in a tropical setting as is the Metropolitan Region of Panama City, and provides the basis needed for a much needed early alert system for the region.

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“…The aim is to determine which methods provide superior predictive performance and computational efficiency, which could inform the design of dengue surveillance systems. Several studies have compared the performance of various dengue prediction methods [8,[16][17][18][19][20][21]. However, these studies have several gaps which we aim to address in our evaluation.…”

Section: Introductionmentioning

confidence: 99%

Assessing dengue forecasting methods: A comparative study of statistical models and machine learning techniques in Rio de Janeiro, Brazil

Chen,

Moraga

2024

Preprint

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BackgroundDengue is a mosquito-borne viral disease that poses a significant public health threat in tropical and subtropical regions worldwide. Accurate forecasting of dengue outbreaks is crucial for effective public health planning and intervention. This study aims to assess the predictive performance and computational efficiency of a number of statistical models and machine learning techniques for dengue forecasting, both with and without the inclusion of climate factors, to inform the design of dengue surveillance systems.MethodsThe study considers dengue cases in Rio de Janeiro, Brazil, as well as climate factors known to affect disease transmission. Employing a dynamic window approach, various statistical methods and machine learning techniques were used to generate weekly forecasts at several time horizons. Error measures, uncertainty intervals, and computational efficiency obtained with each method were compared. Statistical models considered were Autoregressive (AR), Moving Average (MA), Autoregressive Integrated Moving Average (ARIMA), and Exponential Smoothing State Space Model (ETS). Additionally, models incorporating temperature and humidity as covariates, such as Vector Autoregression (VAR) and Seasonal ARIMAX (SARIMAX), were employed. Machine learning techniques evaluated were Random Forest, XGBoost, Support Vector Machine (SVM), Long Short-Term Memory (LSTM) networks, and Prophet. Ensemble approaches that integrated the top performing methods were also considered. The evaluated methods also incorporated lagged climatic variables to account for delayed effects.ResultsAmong the statistical models, ARIMA demonstrated the best performance using only historical case data, while SARIMAX significantly improved predictive accuracy by incorporating climate covariates. In general, the LSTM model, particularly when combined with climate covariates, proved to be the most accurate machine learning model, despite being slower to train and predict. For long-term forecasts, Prophet with climate covariates was the most effective. Ensemble models, such as the combination of LSTM and ARIMA, showed substantial improvements over individual models.ConclusionThis study demonstrates the strengths and limitations of various methods for dengue forecasting across multiple timeframes. It highlights the best-performing statistical and machine learning methods, including their computational efficiency, underscoring the significance of machine learning techniques and the integration of climate covariates to improve forecasts. These findings offer valuable insights for public health officials, facilitating the development of dengue surveillance systems for more accurate forecasting and timely allocation of resources to mitigate dengue outbreaks.Author summaryDengue is a mosquito-borne viral disease that poses a significant public health threat in tropical and subtropical regions worldwide. Accurate forecasting of dengue can significantly aid in public health planning and response. In this study, we compared the performance of various statistical models and machine learning techniques to predict dengue cases across several timeframes. In the evaluation, we used historical dengue case data in Rio de Janeiro, Brazil, as well as climate factors such as temperature and humidity known to affect transmission. Methods considered included traditional statistical models like ARIMA and SARIMAX, and advanced machine learning approaches like Random Forest, XGBoost, SVM, LSTM, and Prophet. We found that integrating climate data significantly improved the accuracy of forecasts. Specifically, the LSTM model combined with climate covariates provided the most accurate predictions overall, while Prophet was particularly effective for long-term forecasts. Additionally, ensemble approaches that combined multiple models outperformed individual models. This work demonstrates the potential of machine learning techniques to provide timely and accurate predictions, and emphasizes the importance of climate data in dengue forecasting. The study aims to support public health officials in developing dengue surveillance systems to enable informed decision-making for mitigating the impact of dengue outbreaks.

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A comparison of SARIMA and LSTM in forecasting dengue hemorrhagic fever incidence in Jambi, Indonesia

Cited by 4 publications

References 10 publications

A Deep Learning Approach for Dengue Fever Prediction in Malaysia Using LSTM with Spatial Attention

A Deep Learning Approach for Dengue Fever Prediction in Malaysia Using LSTM with Spatial Attention

Assessing the Effect of Climate Variables on the Incidence of Dengue Cases in the Metropolitan Region of Panama City

Assessing dengue forecasting methods: A comparative study of statistical models and machine learning techniques in Rio de Janeiro, Brazil

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