Objective To understand the pathogen-carrying status of hand-foot-mouth disease (HFMD) among healthy people in Guangdong province.Methods Stool specimens were collected randomly on 7 age groups from 7 cities in Guangdong province.Real-time RT-PCR was used to detect enterovirus (EV),enterovirus 71 (EV71) and coxsackie virus A16 (CA16).Results Altogether,1285 stool specimens were collected.The positive rates of EV71,CA16 and other enterovirus were 0.39％ (5/1285),0.23％ (3/1285) and 7.00％ (90/1285),respectively.The highest EV71 positive rate (1.79％) was among the 4-6-year-old group,followed by the age group 0-3 with positive rate as 0.67％.EV71 was not found among the rest age groups.The highest CA16 positive rate (1.35％) was among the 4-6 year-olds group,but the CA16 was not found among the rest age groups.EV71 was only found among native population,with the positive-rate as 0.47％.CA16-positive rate was 0.19％ among the native population and 0.85％ among floating population,with no significant difference found (P＞0.05).The EV71 positive rate was 0.36％ among rural residents and 0.54％ among urban residents,but with no significant difference (P＞0.05).All CA16 was found among the urban residents.Conclusion Recessive infection of EV71 and CA16 were only found among 0-6 year-old group but not found among other groups,which suggested that the approaches on prevention and control should be targeted to all children especially on pre-school children.

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.
China/epidemiology* ; Cities/epidemiology* ; Data Visualization ; Disease Outbreaks/statistics & numerical data* ; Forecasting/methods* ; Hand, Foot and Mouth Disease/prevention & control* ; Humans ; Incidence ; Neural Networks, Computer ; Reproducibility of Results ; Spatio-Temporal Analysis ; Time Factors