A Comparative Analysis of Bayesian Network and ARIMA Approaches to Malaria Outbreak Prediction

Hasan, Ali Hussein; Haddawy, Peter; Lawpoolsri, Saranath

doi:10.1007/978-3-319-60663-7_10

Cited by 3 publications

(3 citation statements)

References 12 publications

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“…Researches have proved that the statistical model is helpful to predict the incidence rate of infectious diseases, which is very important for the health sector to identify the spread of epidemics as soon as possible. The autoregressive moving average hybrid model of time series analysis was originally designed for economics [ 15 , 16 ]. However, it played an important role in the prediction of infectious diseases (influenza, malaria, varicella, and others) and had been widely used at present.…”

Section: Discussionmentioning

confidence: 99%

Prediction of Incidence Trend of Influenza-Like Illness in Wuhan Based on ARIMA Model

Meng

Huang

Kong

2022

Computational and Mathematical Methods in Medicine

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Objective. The autoregressive integrated moving average (ARIMA) model has been widely used to predict the trend of infectious diseases. This paper is aimed at analyzing the application of the ARIMA model in the prediction of the incidence trend of influenza-like illness (ILI) in Wuhan and providing a scientific basis for the prediction and prevention of influenza. Methods. The weekly ILI data of two influenza surveillance sentinel hospitals in Wuhan City published on the website of the National Influenza Center of China were collected, and the ARIMA model was used to model the data from 2014 to 2020, to predict and verify the ILI data in 2021. Results. The optimal model for the incidence trend of ILI in Wuhan was ARIMA 1 , 1 , 1 , the residuals were in line with the white noise sequence ( 0.018 < Ljung ‐ Box Q < 30.695 , P > 0.05 ), and the relative error between the predicted value and the actual value was small, which all proved the model was practical. Conclusion. ARIMA 1 , 1 , 1 can effectively simulate the short-term incidence trend of ILI in Wuhan.

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Section: Discussionmentioning

confidence: 99%

Prediction of Incidence Trend of Influenza-Like Illness in Wuhan Based on ARIMA Model

Meng

Huang

Kong

2022

Computational and Mathematical Methods in Medicine

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“…ARIMA (p, d, q) model is an important time series analysis and prediction model, which is also called Autoregressive Integrated Moving Average Model [25,26]. Because the model can capture the trend and randomness of data, it is widely used in the prediction of infectious diseases, and has achieved good prediction results, such as, Wang et al [16] found that ARIMA model could predict the morbidity of influenza in Ningbo, China, 2006-2014, successfully; Shen et al [18] analyzed that ARIMA model was successful in predicting hemorrhagic fever with renal syndrome in China; Anokye et al [21] found that ARIMA model had good performance in predicting malaria incidence; etc [17,19,20].…”

Section: Arima Modelmentioning

confidence: 99%

“…In recent years, many mathematical model methods were used to predict the incidence of infectious diseases, such as linear model [11,12], dynamics model [13,14], grey model [15], time series ARIMA model, neural network model, and so on. Since the time series of infectious diseases often have the characteristics of trend and randomness, ARIMA model and neural network model can capture the regularity of such data well, so they were most widely used and obtained good prediction performance and high prediction accuracy [16][17][18][19][20][21][22][23][24].…”

Section: Introductionmentioning

confidence: 99%

A comparative study of two methods to predict the incidence of hepatitis B in Guangxi, China

et al. 2020

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In recent years, the incidence of hepatitis B (HB) in Guangxi is higher than that of the national level; it has been increasing, so it is urgent to do a good predictive research of HB incidence, which can help analyze the early warning of hepatitis B in Guangxi, China. In the study, the feasibility of predicting HB incidence in Guangxi by autoregressive integrated moving average (ARIMA) model method and Elman neural network (ElmanNN) method was discussed respectively, and the prediction accuracy of the two models was compared. Finally, we established the ARIMA (0, 1, 1) model and ElmanNN with 8 neurons. Both ARIMA (0, 1, 1) model and ElmanNN model had good performance, and their prediction accuracy were high. The fitting and prediction root-mean-square error (RMSE) and mean absolute error (MAE) of ElmanNN were smaller than those of ARIMA (0, 1, 1) model, which indicated that ElmanNN was superior to ARIMA (0, 1, 1) model in predicting the incidence of hepatitis B in Guangxi. Based on the ElmanNN, the HB incidence from September 2019 to December 2020 in Guangxi was predicted, the predicted results showed that the incidence of HB in 2020 was slightly higher than that in 2019 and the change trend was similar to that in 2019, for 2021 and beyond, the ElmanNN model could be used to continue the predictive analysis.

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Complexity-Based Spatial Hierarchical Clustering for Malaria Prediction

Haddawy

Yin

Wisanrakkit

et al. 2018

J Healthc Inform Res

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A Comparative Analysis of Bayesian Network and ARIMA Approaches to Malaria Outbreak Prediction

Cited by 3 publications

References 12 publications

Prediction of Incidence Trend of Influenza-Like Illness in Wuhan Based on ARIMA Model

Prediction of Incidence Trend of Influenza-Like Illness in Wuhan Based on ARIMA Model

A comparative study of two methods to predict the incidence of hepatitis B in Guangxi, China

Complexity-Based Spatial Hierarchical Clustering for Malaria Prediction

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