Objectives Hand,foot and mouth disease(HFMD)is a widespread infectious disease that causes a significant disease burden on society.To achieve early intervention and to prevent outbreaks of disease,we propose a novel w...Objectives Hand,foot and mouth disease(HFMD)is a widespread infectious disease that causes a significant disease burden on society.To achieve early intervention and to prevent outbreaks of disease,we propose a novel warning model that can accurately predict the incidence of HFMD.Methods We propose a spatial-temporal graph convolutional network(STGCN)that combines spatial factors for surrounding cities with historical incidence over a certain time period to predict the future occurrence of HFMD in Guangdong and Shandong between 2011 and 2019.The 2011-2018 data served as the training and verification set,while data from 2019 served as the prediction set.Six important parameters were selected and verified in this model and the deviation was displayed by the root mean square error and the mean absolute error.Results As the first application using a STGCN for disease forecasting,we succeeded in accurately predicting the incidence of HFMD over a 12-week period at the prefecture level,especially for cities of significant concern.Conclusions This model provides a novel approach for infectious disease prediction and may help health administrative departments implement effective control measures up to 3 months in advance,which may significantly reduce the morbidity associated with HFMD in the future.展开更多
In view of the cumbersome and often untimely process of manual collection and observation of frozen soil data parameters,and the damage caused to dams by frost heaving of frozen soil,a remote monitoring and an early w...In view of the cumbersome and often untimely process of manual collection and observation of frozen soil data parameters,and the damage caused to dams by frost heaving of frozen soil,a remote monitoring and an early warning model for frozen soil in dam areas was presented.The Pt100 temperature sensors and JM seam gauges were used as measurement tools in the system.The sensor layout was designed,based on the actual situation in the monitoring area.A 4G network was used for wireless transmission to monitor frozen soil data in real time.BP neural network was used to predict the parameters of frozen soil.After analysis,four factors including the average temperature of frozen soil,the type of frozen soil,the artificial upper limit of frozen soil and the building construction time were selected to establish an early warning model using fuzzy reasoning.The experimental results showed that the early warning model could reflect the influence on dam buildings of frost heaving and sinking of frozen soil,and provided technical support for predicting the hazard level.展开更多
基金supported by grants from the Key Technologies Research and Development Program from the Ministry of Science and Technology[grant number:ZDZX-2018ZX102001002-003-003]the Beijing Natural Science Foundation[project number:L192014]
文摘Objectives Hand,foot and mouth disease(HFMD)is a widespread infectious disease that causes a significant disease burden on society.To achieve early intervention and to prevent outbreaks of disease,we propose a novel warning model that can accurately predict the incidence of HFMD.Methods We propose a spatial-temporal graph convolutional network(STGCN)that combines spatial factors for surrounding cities with historical incidence over a certain time period to predict the future occurrence of HFMD in Guangdong and Shandong between 2011 and 2019.The 2011-2018 data served as the training and verification set,while data from 2019 served as the prediction set.Six important parameters were selected and verified in this model and the deviation was displayed by the root mean square error and the mean absolute error.Results As the first application using a STGCN for disease forecasting,we succeeded in accurately predicting the incidence of HFMD over a 12-week period at the prefecture level,especially for cities of significant concern.Conclusions This model provides a novel approach for infectious disease prediction and may help health administrative departments implement effective control measures up to 3 months in advance,which may significantly reduce the morbidity associated with HFMD in the future.
基金Supported by the Application Technology Research and Development Plan Project of Heilongjiang Province(GY2014ZB0011)the 13th Five-year National Key R&D Program(2016YFD0300610)
文摘In view of the cumbersome and often untimely process of manual collection and observation of frozen soil data parameters,and the damage caused to dams by frost heaving of frozen soil,a remote monitoring and an early warning model for frozen soil in dam areas was presented.The Pt100 temperature sensors and JM seam gauges were used as measurement tools in the system.The sensor layout was designed,based on the actual situation in the monitoring area.A 4G network was used for wireless transmission to monitor frozen soil data in real time.BP neural network was used to predict the parameters of frozen soil.After analysis,four factors including the average temperature of frozen soil,the type of frozen soil,the artificial upper limit of frozen soil and the building construction time were selected to establish an early warning model using fuzzy reasoning.The experimental results showed that the early warning model could reflect the influence on dam buildings of frost heaving and sinking of frozen soil,and provided technical support for predicting the hazard level.
