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Comparison of ARIMA and GM(1,1) models for prediction of hepatitis B in China.

ABSTRACT: BACKGROUND:Hepatitis B virus (HBV) infection is a major public health threat in China for China has a hepatitis B prevalence of more than one million people in 2017 year. Disease incidence prediction may help hepatitis B prevention and control. This study intends to build and compare 2 forecasting models for hepatitis B incidence in China. METHODS:Autoregressive integrated moving average (ARIMA) model and grey model GM(1,1) were adopted to fit the monthly incidence of hepatitis B in China from March 2010 to October 2017. The fitting and forecasting performances of the 2 models were evaluated. The better one was adopted to predict the incidence from November 2017 to March 2018. Database was built by Excel 2016 and statistical analysis was completed using R 3.4.3 software. RESULTS:Descriptive analysis showed that the incidence of hepatitis B in China has seasonal variation and has shown a downward trend from 2010 to 2017. We selected the ARIMA (3,1,1) (0,1,2)12 model among all the ARIMA models for it has the lowest AIC value. Model expression of GM (1,1) was X(1) (k + 1) = 3386876.7478e0.0249k - 3289206.7428. The root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) of ARIMA(3,1,1)(0,1,2)12 model were lower than GM(1,1) model on fitting part and forecasting part. According to the forecast results, the incidence may have a slight fluctuation during the following months. CONCLUSIONS:The ARIMA model showed better hepatitis B fitting and forecasting performance than GM(1,1) model. It is a potential decision supportive tool for controlling hepatitis B in China before a predictive hepatitis B outbreak.

SUBMITTER: Wang YW

PROVIDER: S-EPMC6122800 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Comparison of ARIMA and GM(1,1) models for prediction of hepatitis B in China.

Wang Ya-Wen YW Shen Zhong-Zhou ZZ Jiang Yu Y

PloS one 20180904 9

<h4>Background</h4>Hepatitis B virus (HBV) infection is a major public health threat in China for China has a hepatitis B prevalence of more than one million people in 2017 year. Disease incidence prediction may help hepatitis B prevention and control. This study intends to build and compare 2 forecasting models for hepatitis B incidence in China.<h4>Methods</h4>Autoregressive integrated moving average (ARIMA) model and grey model GM(1,1) were adopted to fit the monthly incidence of hepatitis B ...[more]

PMID: 30180159

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Project description:BackgroundCOVID-19 and Sexually Transmitted Diseases (STDs) are two very important diseases. However, relevant researches about how COVID-19 pandemic has impacted on the epidemiological trend of STDs are limited in China. This study aimed to analyze the impact of COVID-19 on STDs in China and proposed relevant recommendations to be used in bettering health.MethodsThe incidence of HIV infection, syphilis and gonorrhea in China from 2008 to 2020 were collected. Grey Model (1,1) were established to predict the incidence of STDs with the incidence data of these three STDs from 2013 to 2018 considering the impact of policies in China, respectively. We then calculated the predictive incidence of each STD in 2019, 2020 and 2021 by the established Model. And we estimated the extent of the impact of COVID-19 on the epidemiological changes of STDs by analyzing the difference between the absolute percentage error (APE) of the predictive incidence and actual rate in 2019 and 2020.ResultsThe incidence of HIV infection and syphilis showed a trend of increase from 2008 to 2019 in China, but that for gonorrhea was fluctuant. Of note, the incidence of these three STDs decreased significantly in 2020 compared with that in 2019. The APE of HIV infection, syphilis and gonorrhea in 2020 (20.54%, 15.45% and 60.88%) were about 7 times, 4 times and 2 times of that in 2019 (2.94%, 4.07% and 30.41%). The incidence of HIV infection, syphilis and gonorrhea would be 5.77/100,000, 39.64/100,000 and 13.19/100,000 in 2021 based on our model.ConclusionsThe epidemiological trend of STDs in China was significant influenced by COVID-19 pandemic. It is important to balance the control of COVID-19 and timely management of STDs during the COVID-19 epidemic to prevent or reduce the poor outcome among COVID-19 patients with STDs. New management strategies on STDs, such as leveraging social media, online medical care, rapid self-testing, timely diagnosis and treatment guarantee and balance of medical resources for STDs management should be adapted in the context of the long-term effects of COVID-19.

Project description:BackgroundAccurate and reliable predictions of infectious disease can be valuable to public health organizations that plan interventions to decrease or prevent disease transmission. A great variety of models have been developed for this task. However, for different data series, the performance of these models varies. Hepatitis E, as an acute liver disease, has been a major public health problem. Which model is more appropriate for predicting the incidence of hepatitis E? In this paper, three different methods are used and the performance of the three methods is compared.MethodsAutoregressive integrated moving average(ARIMA), support vector machine(SVM) and long short-term memory(LSTM) recurrent neural network were adopted and compared. ARIMA was implemented by python with the help of statsmodels. SVM was accomplished by matlab with libSVM library. LSTM was designed by ourselves with Keras, a deep learning library. To tackle the problem of overfitting caused by limited training samples, we adopted dropout and regularization strategies in our LSTM model. Experimental data were obtained from the monthly incidence and cases number of hepatitis E from January 2005 to December 2017 in Shandong province, China. We selected data from July 2015 to December 2017 to validate the models, and the rest was taken as training set. Three metrics were applied to compare the performance of models, including root mean square error(RMSE), mean absolute percentage error(MAPE) and mean absolute error(MAE).ResultsBy analyzing data, we took ARIMA(1, 1, 1), ARIMA(3, 1, 2) as monthly incidence prediction model and cases number prediction model, respectively. Cross-validation and grid search were used to optimize parameters of SVM. Penalty coefficient C and kernel function parameter g were set 8, 0.125 for incidence prediction, and 22, 0.01 for cases number prediction. LSTM has 4 nodes. Dropout and L2 regularization parameters were set 0.15, 0.001, respectively. By the metrics of RMSE, we obtained 0.022, 0.0204, 0.01 for incidence prediction, using ARIMA, SVM and LSTM. And we obtained 22.25, 20.0368, 11.75 for cases number prediction, using three models. For MAPE metrics, the results were 23.5%, 21.7%, 15.08%, and 23.6%, 21.44%, 13.6%, for incidence prediction and cases number prediction, respectively. For MAE metrics, the results were 0.018, 0.0167, 0.011 and 18.003, 16.5815, 9.984, for incidence prediction and cases number prediction, respectively.ConclusionsComparing ARIMA, SVM and LSTM, we found that nonlinear models(SVM, LSTM) outperform linear models(ARIMA). LSTM obtained the best performance in all three metrics of RSME, MAPE, MAE. Hence, LSTM is the most suitable for predicting hepatitis E monthly incidence and cases number.

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Comparison of ARIMA and GM(1,1) models for prediction of hepatitis B in China.

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Comparison of ARIMA and GM(1,1) models for prediction of hepatitis B in China.

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