Short-Term Infectious Diarrhea Prediction Using Weather and Search Data in Xiamen, China

Wang, Zhijin; Huang, Yaohui; He, Bin; Luo, Ting; Wang, Yongming; Fu, Yonggang; Huang, Chenxi

doi:10.1155/2020/8814222

Cited by 10 publications

(13 citation statements)

References 36 publications

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“…(2) The learning methods focus on temporal, which inputs of different categories are differential treatment, such as [24][25][26]. For these methods, the temporal dynamics of input data is captured to use RNN structures, and a nonlinear mapping from inputs to the target is learned from training data.…”

Section: Decomposition Methodsmentioning

confidence: 99%

Prediction of HFMD Cases by Leveraging Time Series Decomposition and Local Fusion

Wang

Lin

et al. 2021

Wireless Communications and Mobile Computing

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Hand, foot, and mouth disease (HFMD) is an infection that is common in children under 5 years old. This disease is not a serious disease commonly, but it is one of the most widespread infectious diseases which can still be fatal. HFMD still poses a threat to the lives and health of children and adolescents. An effective prediction model would be very helpful to HFMD control and prevention. Several methods have been proposed to predict HFMD outpatient cases. These methods tend to utilize the connection between cases and exogenous data, but exogenous data is not always available. In this paper, a novel method combined time series composition and local fusion has been proposed. The Empirical Mode Decomposition (EMD) method is used to decompose HFMD outpatient time series. Linear local predictors are applied to processing input data. The predicted value is generated via fusing the output of local predictors. The evaluation of the proposed model is carried on a real dataset comparing with the state-of-the-art methods. The results show that our model is more accurately compared with other baseline models. Thus, the model we proposed can be an effective method in the HFMD outpatient prediction mission.

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Section: Decomposition Methodsmentioning

confidence: 99%

Prediction of HFMD Cases by Leveraging Time Series Decomposition and Local Fusion

Wang

Lin

et al. 2021

Wireless Communications and Mobile Computing

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“…This study proposes a Dual-stage Attention-based Recurrent Neural Network (DA-RNN) to solve these two problems. Within DA-RNN model, it involves two attention layers: the first is an input attention layer that determines which feature should be given more attention than the others; the second is a temporal attention layer that determines the weight of importance for each historical temporal step [ 41 , 42 ]. Subsequently, we concatenated the output from the second attention layer to the historical information, so as to forecast the confirmed cases of COVID-19 in the next time step, as shown in Fig.…”

Section: Forecastingmentioning

confidence: 99%

Cross-sectional analysis and data-driven forecasting of confirmed COVID-19 cases

Jiang

Shi

et al. 2021

Appl Intell

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The coronavirus disease 2019 (COVID-19) is rapidly becoming one of the leading causes for mortality worldwide. Various models have been built in previous works to study the spread characteristics and trends of the COVID-19 pandemic. Nevertheless, due to the limited information and data source, the understanding of the spread and impact of the COVID-19 pandemic is still restricted. Therefore, within this paper not only daily historical time-series data of COVID-19 have been taken into account during the modeling, but also regional attributes, e.g., geographic and local factors, which may have played an important role on the confirmed COVID-19 cases in certain regions. In this regard, this study then conducts a comprehensive cross-sectional analysis and data-driven forecasting on this pandemic. The critical features, which has the significant influence on the infection rate of COVID-19, is determined by employing XGB (eXtreme Gradient Boosting) algorithm and SHAP (SHapley Additive exPlanation) and the comparison is carried out by utilizing the RF (Random Forest) and LGB (Light Gradient Boosting) models. To forecast the number of confirmed COVID-19 cases more accurately, a Dual-Stage Attention-Based Recurrent Neural Network (DA-RNN) is applied in this paper. This model has better performance than SVR (Support Vector Regression) and the encoder-decoder network on the experimental dataset. And the model performance is evaluated in the light of three statistic metrics, i.e. MAE, RMSE and R 2 . Furthermore, this study is expected to serve as meaningful references for the control and prevention of the COVID-19 pandemic.

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“…The HFMD prediction can be viewed as the problem of time series forecasting 12 . Numerous studies have examined the effects of exogenous data in improving the prediction performance 13 . The exogenous data can be climate data, water condition data, and air pollution data.…”

Section: Introductionmentioning

confidence: 99%

Dual‐grained representation for hand, foot, and mouth disease prediction within public health cyber‐physical systems

Wang

Huang

2020

Softw Pract Exp

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The prediction model is a major component within public health cyber‐physical systems, which supports decisions on prevention and control of diseases. Hand, foot, and mouth disease (HFMD) is one of the most common global infectious diseases with the highest incidence rate. Previous HFMD prediction models are mainly based on the time series that counted in equal‐grained time intervals. However, there are details in the time series counted in fine‐grained time intervals. To benefit from both equal‐grained and fine‐grained data, we proposed a dual‐grained representation (DGR) model. The DGR first represents inputted data to temporal patterns. Then, the represented patterns are consolidated to generate predictions. Experimental comparisons of the short‐term prediction performance are figured out by using real outpatient collections in Xiamen, China.

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Short-Term Infectious Diarrhea Prediction Using Weather and Search Data in Xiamen, China

Cited by 10 publications

References 36 publications

Prediction of HFMD Cases by Leveraging Time Series Decomposition and Local Fusion

Prediction of HFMD Cases by Leveraging Time Series Decomposition and Local Fusion

Cross-sectional analysis and data-driven forecasting of confirmed COVID-19 cases

Dual‐grained representation for hand, foot, and mouth disease prediction within public health cyber‐physical systems

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