Publications by authors named "Chongchong Yu"

5 Publications

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Time Series Analysis and Forecasting of the Hand-Foot-Mouth Disease Morbidity in China Using An Advanced Exponential Smoothing State Space TBATS Model.

Infect Drug Resist 2021 21;14:2809-2821. Epub 2021 Jul 21.

Department of Epidemiology and Health Statistics, School of Public Health, Xinxiang Medical University, Xinxiang, Henan Province, People's Republic of China.

Objective: The high morbidity, complex seasonality, and recurring risk of hand-foot-and-mouth disease (HFMD) exert a major burden in China. Forecasting its epidemic trends is greatly instrumental in informing vaccine and targeted interventions. This study sets out to investigate the usefulness of an advanced exponential smoothing state space framework by combining Box-Cox transformations, Fourier representations with time-varying coefficients and autoregressive moving average (ARMA) error correction (TBATS) method to assess the temporal trends of HFMD in China.

Methods: Data from January 2009 to December 2019 were drawn, and then they were split into two segments comprising the in-sample training data and out-of-sample testing data to develop and validate the TBATS model, and its fitting and forecasting abilities were compared with the most frequently used seasonal autoregressive integrated moving average (SARIMA) method.

Results: Following the modelling procedures of the SARIMA and TBATS methods, the SARIMA (1,0,1)(0,1,1) and TBATS (0.024, {1,1}, 0.855, {<12,4>}) specifications were recognized as being the optimal models, respectively, for the 12-step ahead forecasting, along with the SARIMA (1,0,1)(0,1,1) and TBATS (0.062, {1,3}, 0.86, {<12,4>}) models as being the optimal models, respectively, for the 24-step ahead forecasting. Among them, the optimal TBATS models produced lower error rates in both 12-step and 24-step ahead forecasting aspects compared to the preferred SARIMA models. Descriptive analysis of the data showed a significantly high level and a marked dual seasonal pattern in the HFMD morbidity.

Conclusion: The TBATS model has the capacity to outperform the most frequently used SARIMA model in forecasting the HFMD incidence in China, and it can be recommended as a flexible and useful tool in the decision-making process of HFMD prevention and control in China.
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http://dx.doi.org/10.2147/IDR.S304652DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8312251PMC
July 2021

Forecasting the Tuberculosis Incidence Using a Novel Ensemble Empirical Mode Decomposition-Based Data-Driven Hybrid Model in Tibet, China.

Infect Drug Resist 2021 25;14:1941-1955. Epub 2021 May 25.

Department of Epidemiology and Health Statistics, School of Public Health, Xinxiang Medical University, Xinxiang, Henan Province, People's Republic of China.

Objective: The purpose of this study is to develop a novel data-driven hybrid model by fusing ensemble empirical mode decomposition (EEMD), seasonal autoregressive integrated moving average (SARIMA), with nonlinear autoregressive artificial neural network (NARNN), called EEMD-ARIMA-NARNN model, to assess and forecast the epidemic patterns of TB in Tibet.

Methods: The TB incidence from January 2006 to December 2017 was obtained, and then the time series was partitioned into training subsamples (from January 2006 to December 2016) and testing subsamples (from January to December 2017). Among them, the training set was used to develop the EEMD-SARIMA-NARNN combined model, whereas the testing set was used to validate the forecasting performance of the model. Whilst the forecasting accuracy level of this novel method was compared with the basic SARIMA model, basic NARNN model, error-trend-seasonal (ETS) model, and traditional SARIMA-NARNN mixture model.

Results: By comparing the accuracy level of the forecasting measurements including root-mean-square error, mean absolute deviation, mean error rate, mean absolute percentage error, and root-mean-square percentage error, it was shown that the EEMD-SARIMA-NARNN combined method produced lower error rates than the others. The descriptive statistics suggested that TB was a seasonal disease, peaking in late winter and early spring and a trough in autumn and early winter, and the TB epidemic indicated a drastic increase by a factor of 1.7 from 2006 to 2017 in Tibet, with average annual percentage change of 5.8 (95% confidence intervals: 3.5-8.1).

Conclusion: This novel data-driven hybrid method can better consider both linear and nonlinear components in the TB incidence than the others used in this study, which is of great help to estimate and forecast the future epidemic trends of TB in Tibet. Besides, under present trends, strict precautionary measures are required to reduce the spread of TB in Tibet.
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http://dx.doi.org/10.2147/IDR.S299704DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8164697PMC
May 2021

Stable isotopes verify geographical origin of yak meat from Qinghai-Tibet plateau.

Meat Sci 2020 Jul 14;165:108113. Epub 2020 Mar 14.

Institute of Quality and Standard of Agricultural Product, Zhejiang Academy of Agricultural Sciences, Hangzhou 310021, China; National Isotope Centre, GNS Science, 30 Gracefield Road, Lower Hutt 5040, New Zealand. Electronic address:

High-value yak meat from Qinghai-Tibet Plateau was investigated using stable isotopes (δC, δH, δO, δN and δS) to identify attributes which could verify and protect its geographical origin. Supervised PLS-DA was applied to the isotope data to discriminate four geographical locations. δC, δH, and δO values showed significant differences according to origin while δN and δS values did not show any change across the different regions. Isotope values of different body tissues from the same animal showed no statistical difference for the five stable isotopes. In addition, the δH and δO values of defatted yak meat was highly correlated to farm altitude and associated drinking water. This yak meat traceability method is particularly useful to protect the Product of Geographical Indication (PGI) status of Gannan yak meat and verify the farming origin of yak meat sold in markets for food safety purposes, especially when excessive hormones, pesticides or heavy metals are found.
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http://dx.doi.org/10.1016/j.meatsci.2020.108113DOI Listing
July 2020

Sensor Drift Compensation Based on the Improved LSTM and SVM Multi-Class Ensemble Learning Models.

Sensors (Basel) 2019 Sep 5;19(18). Epub 2019 Sep 5.

Beijing Key Laboratory of Big Data Technology for Food Safety, Beijing Technology and Business University, Beijing 100048, China.

Drift is an important issue that impairs the reliability of sensors, especially in gas sensors. The conventional method usually adopts the reference gas to compensate for the drift. However, its classification accuracy is not high. We propose a supervised learning algorithm that is based on multi-classifier integration for drift compensation in this paper, which incorporates drift compensation into the classification process, motivated by the fact that the goal of drift compensation is to improve the classification performance. In our method, with the obtained characteristics of sensors and the advantage of Support Vector Machine (SVM) in few-shot classification, the improved Long Shot Term Memory (LSTM) is integrated to build the multi-class classifier model. We tested the proposed approach on the publicly available time series dataset that was collected over three years by the metal-oxide gas sensors. The results clearly indicate the superiority of multiple classifier approach, which achieves higher classification accuracy as compared with different approaches during testing period with an ensemble of classifiers in the presence of sensor drift over time.
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http://dx.doi.org/10.3390/s19183844DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6767085PMC
September 2019

A New Method of Mixed Gas Identification Based on a Convolutional Neural Network for Time Series Classification.

Sensors (Basel) 2019 Apr 26;19(9). Epub 2019 Apr 26.

School of Computer and Information Engineering, Beijing Technology and Business University, Beijing 100048, China.

This paper proposes a new method of mixed gas identification based on a convolutional neural network for time series classification. In view of the superiority of convolutional neural networks in the field of computer vision, we applied the concept to the classification of five mixed gas time series data collected by an array of eight MOX gas sensors. Existing convolutional neural networks are mostly used for processing visual data, and are rarely used in gas data classification and have great limitations. Therefore, the idea of mapping time series data into an analogous-image matrix data is proposed. Then, five kinds of convolutional neural networks-VGG-16, VGG-19, ResNet18, ResNet34 and ResNet50-were used to classify and compare five kinds of mixed gases. By adjusting the parameters of the convolutional neural networks, the final gas recognition rate is 96.67%. The experimental results show that the method can classify the gas data quickly and effectively, and effectively combine the gas time series data with classical convolutional neural networks, which provides a new idea for the identification of mixed gases.
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http://dx.doi.org/10.3390/s19091960DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6539079PMC
April 2019
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