Comparison of ARIMA model and XGBoost model for prediction of human brucellosis in mainland China: a time-series study

Alim, Mirxat; Ye, Guohua; Guan, Peng; Huang, Desheng; Zhou, Baosen; Wu, Wei

doi:10.1136/bmjopen-2020-039676

Cited by 56 publications

(49 citation statements)

References 43 publications

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“…It is good at dealing with nonlinear data but has poor interpretability. From studies in other fields, the XGBoost model performed well in predicting nonlinear time series [ 28 – 31 ]. By integrating multiple CART models, XGBoost model can achieve a better generalizability than a single model, which means that the XGBoost has a larger postpruning penalty than a GBDT model and makes the learned model less prone to overfitting.…”

Section: Discussionmentioning

confidence: 99%

Time series analysis of hemorrhagic fever with renal syndrome in mainland China by using an XGBoost forecasting model

Qiao

et al. 2021

BMC Infect Dis

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Background Hemorrhagic fever with renal syndrome (HFRS) is still attracting public attention because of its outbreak in various cities in China. Predicting future outbreaks or epidemics disease based on past incidence data can help health departments take targeted measures to prevent diseases in advance. In this study, we propose a multistep prediction strategy based on extreme gradient boosting (XGBoost) for HFRS as an extension of the one-step prediction model. Moreover, the fitting and prediction accuracy of the XGBoost model will be compared with the autoregressive integrated moving average (ARIMA) model by different evaluation indicators. Methods We collected HFRS incidence data from 2004 to 2018 of mainland China. The data from 2004 to 2017 were divided into training sets to establish the seasonal ARIMA model and XGBoost model, while the 2018 data were used to test the prediction performance. In the multistep XGBoost forecasting model, one-hot encoding was used to handle seasonal features. Furthermore, a series of evaluation indices were performed to evaluate the accuracy of the multistep forecast XGBoost model. Results There were 200,237 HFRS cases in China from 2004 to 2018. A long-term downward trend and bimodal seasonality were identified in the original time series. According to the minimum corrected akaike information criterion (CAIC) value, the optimal ARIMA (3, 1, 0) × (1, 1, 0)12 model is selected. The index ME, RMSE, MAE, MPE, MAPE, and MASE indices of the XGBoost model were higher than those of the ARIMA model in the fitting part, whereas the RMSE of the XGBoost model was lower. The prediction performance evaluation indicators (MAE, MPE, MAPE, RMSE and MASE) of the one-step prediction and multistep prediction XGBoost model were all notably lower than those of the ARIMA model. Conclusions The multistep XGBoost prediction model showed a much better prediction accuracy and model stability than the multistep ARIMA prediction model. The XGBoost model performed better in predicting complicated and nonlinear data like HFRS. Additionally, Multistep prediction models are more practical than one-step prediction models in forecasting infectious diseases.

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Section: Discussionmentioning

confidence: 99%

Time series analysis of hemorrhagic fever with renal syndrome in mainland China by using an XGBoost forecasting model

Qiao

et al. 2021

BMC Infect Dis

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“…As our time-series data showed stationary characteristics without seasonality, which means that the mean, variance, and covariance of data were invariant to time, in the augmented Dickey-Fuller test (p < 0.001) [66], we applied the non-seasonal ARIMA (1, 0, 1) model using the following parameters, with the lowest Akaike Information Criterion value (388. As our time-series data showed stationary characteristics without seasonality, which means that the mean, variance, and covariance of data were invariant to time, in the augmented Dickey-Fuller test (p < 0.001) [66], we applied the non-seasonal ARIMA (1, 0, 1) model using the following parameters, with the lowest Akaike Information Criterion value (388.7): (1) 1 of autoregression (p) from the autocorrelation function of residuals, (2) 0 of degree of differencing (integrated, d), and (3) 1 of size of the moving average window (q) from the partial autocorrelation function of residuals (Supplementary Figure S3) [59,67,68]. As the observed PCP-confirmed cases did not exist for several months, and average numbers of observed PCP-confirmed cases per month in each year were very small in the pre-and post-COVID-19 periods, we did not perform the time-series analysis for HSCT recipients, chronic lung disease, and HIV-1-infected individuals in the ARIMA and BSTS model.…”

Section: Clinical Information Of Total Pcp-confirmed Inpatientsmentioning

confidence: 99%

No Change of Pneumocystis jirovecii Pneumonia after the COVID-19 Pandemic: Multicenter Time-Series Analyses

Kim

Jeon

et al. 2021

JoF

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Consolidated infection control measures imposed by the government and hospitals during COVID-19 pandemic resulted in a sharp decline of respiratory viruses. Based on the issue of whether Pneumocystis jirovecii could be transmitted by airborne and acquired from the environment, we assessed changes in P. jirovecii pneumonia (PCP) cases in a hospital setting before and after COVID-19. We retrospectively collected data of PCP-confirmed inpatients aged ≥18 years (N = 2922) in four university-affiliated hospitals between January 2015 and June 2021. The index and intervention dates were defined as the first time of P. jirovecii diagnosis and January 2020, respectively. We predicted PCP cases for post-COVID-19 and obtained the difference (residuals) between forecasted and observed cases using the autoregressive integrated moving average (ARIMA) and the Bayesian structural time-series (BSTS) models. Overall, the average of observed PCP cases per month in each year were 36.1 and 47.3 for pre- and post-COVID-19, respectively. The estimate for residuals in the ARIMA model was not significantly different in the total PCP-confirmed inpatients (7.4%, p = 0.765). The forecasted PCP cases by the BSTS model were not significantly different from the observed cases in the post-COVID-19 (−0.6%, 95% credible interval; −9.6~9.1%, p = 0.450). The unprecedented strict non-pharmacological interventions did not affect PCP cases.

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“…In this paper, we used two machine learning methods, Random Forest and XGBoost, to perform our analysis and prediction on HFMD incidence. There have been a large number of studies focusing on machine learning methods to analyze different infectious diseases, and to perform prediction about the incidence of diseases, such as dengue [ 30 , 33 – 35 ], polio [ 36 ], human brucellosis [ 37 ], malaria [ 38 ], and COVID-19 [ 39 , 40 ]. It is also applied for the prediction of HFMD from meteorological factors in a single province in China [ 41 ].…”

Section: Discussionmentioning

confidence: 99%

“…It is also applied for the prediction of HFMD from meteorological factors in a single province in China [ 41 ]. Several studies using machine learning methods compared the performance of different prediction methods [ 30 , 34 , 37 ]. A study on human brucellosis in mainland China stated that XGBoost model is more suitable for prediction cases of human brucellosis in mainland China than ARIMA model [ 37 ].…”

Section: Discussionmentioning

confidence: 99%

“…Several studies using machine learning methods compared the performance of different prediction methods [ 30 , 34 , 37 ]. A study on human brucellosis in mainland China stated that XGBoost model is more suitable for prediction cases of human brucellosis in mainland China than ARIMA model [ 37 ]. Our study did not involve ARIMA model, but we compared the prediction performance of XGBoost model and Random Forest model.…”

Section: Discussionmentioning

confidence: 99%

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Analysis and prediction of hand, foot and mouth disease incidence in China using Random Forest and XGBoost

Meng

Zhao

2021

PLoS ONE

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Hand, foot and mouth disease (HFMD) is an increasingly serious public health problem, and it has caused an outbreak in China every year since 2008. Predicting the incidence of HFMD and analyzing its influential factors are of great significance to its prevention. Now, machine learning has shown advantages in infectious disease models, but there are few studies on HFMD incidence based on machine learning that cover all the provinces in mainland China. In this study, we proposed two different machine learning algorithms, Random Forest and eXtreme Gradient Boosting (XGBoost), to perform our analysis and prediction. We first used Random Forest to examine the association between HFMD incidence and potential influential factors for 31 provinces in mainland China. Next, we established Random Forest and XGBoost prediction models using meteorological and social factors as the predictors. Finally, we applied our prediction models in four different regions of mainland China and evaluated the performance of them. Our results show that: 1) Meteorological factors and social factors jointly affect the incidence of HFMD in mainland China. Average temperature and population density are the two most significant influential factors; 2) Population flux has different delayed effect in affecting HFMD incidence in different regions. From a national perspective, the model using population flux data delayed for one month has better prediction performance; 3) The prediction capability of XGBoost model was better than that of Random Forest model from the overall perspective. XGBoost model is more suitable for predicting the incidence of HFMD in mainland China.

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Comparison of ARIMA model and XGBoost model for prediction of human brucellosis in mainland China: a time-series study

Cited by 56 publications

References 43 publications

Time series analysis of hemorrhagic fever with renal syndrome in mainland China by using an XGBoost forecasting model

Time series analysis of hemorrhagic fever with renal syndrome in mainland China by using an XGBoost forecasting model

No Change of Pneumocystis jirovecii Pneumonia after the COVID-19 Pandemic: Multicenter Time-Series Analyses

Analysis and prediction of hand, foot and mouth disease incidence in China using Random Forest and XGBoost

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