Objective: To compare the predictive performance of the seasonal autoregressive integrated moving average (SARIMA) model, the Prophet model, and the Bayesian structural time series (BSTS) model in forecasting the incidence of hand, foot, and mouth disease (HFMD) , so as to provide a basis for optimizing the early warning system of this disease. Methods: Weekly incidence data of HFMD in Longgang District, Shenzhen City from 2014 to 2024 were collected. The HFMD incidence data from 2014-2019 and 2023 were used as the training set to construct SARIMA, Prophet, and BSTS models, while the data from 2024 were used as the test set to compare and evaluate the predictive performance of the three models. The technique for order preference by similarity to ideal solution (TOPSIS) method was employed to calculate the C-value. This approach integrates multiple evaluation metrics, such as the mean absolute error (MAE), mean squared error (MSE), root mean squared error (RMSE), and symmetric mean absolute percentage error (SMAPE), to comprehensively assess model performance. Results: A total of 150 111 cases of HFMD were reported in Longgang District from 2014 to 2024, with an average annual incidence of 400.72/105. The weekly incidence fluctuated between 0 and 63.78/105, exhibiting a bimodal seasonal pattern characterized by a primary peak from May to July and a secondary peak from September to October. In the training set, all three models demonstrated a good fit to the bimodal epidemic trend of HFMD, with the BSTS model achieving the best fit. The BSTS model yielded performance metrics as follows: MAE=0.124, MSE=0.050, RMSE=0.223, SMAPE=0.021, and a C-value of 1.000. In the test set, all three models, including SARIMA, Prophet, and BSTS, performed well for short-term predictions (≤16 weeks), with the Prophet model showing relatively superior predictive performance. However, the prediction accuracy of all models declined as the forecast horizon extended. During the primary peak period (May-July), the Prophet model exhibited better predictive performance, whereas the BSTS model performed relatively better during the secondary peak period (September-October). Conclusions For the short-term forecasting of weekly HFMD incidence, the Prophet model outperformed both the SARIMA and BSTS models. During the primary peak period, the Prophet model demonstrated superior predictive performance, whereas the BSTS model exhibited better accuracy in forecasting the secondary peak period.

OBJECTIVE: To explore the effect of ultra-high frequency radiation on psychomotor neurological behavior in workers with exposure. METHODS: A total of 85 workers who exposed to 40.68 MHz radiofrequency were recruited as the exposure group by judgment sampling method. A group of 121 workers without occupational EMR exposure were recruited as the control group. Workers in both groups were from the same shoe factory. The electric field intensity(EFI) of ultra-high frequency radiation of workplace in the exposure group was measured. The computerized neurobehavioral evaluation system in Chinese version 3 was used to evaluate the psychomotor neurobehavioral function which included the neurobehavioral ability index(NAI) of simple visual reaction time(SVRT), digital screening and fit curve and the general NAI(GNAI) of the above 3 indexes. RESULTS: The median of the workplace EFI of ultra-high frequency radiation in the exposure group was 119.0 V/m, and all of them exceeded the national occupational exposure limit. NAI of digital screening in exposure group was lower than that in the control group(P<0.05). There is no statistically significant difference in the NAI of SVRT, fit curve and GNAI(P>0.05). Meanwhile, there is no statistically significant difference in abnormal rate of NAI of SVRT, digital screening, fit curve and GNAI(P>0.05). The results of multiple linear regression analysis showed that the ultra-high frequency radiation EFI exposure was negatively correlated with NAI of digital screening(P<0.05) after eliminating the influence of confounding factors such as age, working age, gender, education level, smoking, drinking and staying up late. CONCLUSION: The digital screening of psychomotor neurobehavioral function in the exposure workers was adversely affected by the ultra-high frequency radiation. The neural behavioral ability of eye-hand coordination and precise movement may be the specific performance.