Objective To establish a prediction model for the occurrence of non-occupational carbon monoxide poisoning events in Beijing, and to provide scientific basis and theoretical support for the prevention and warning of poisoning events. Methods Based on the monitoring data of non-occupational carbon monoxide poisoning events in Beijing from 2016 to 2024, the seasonal ARIMA model and seasonal index model were established to analyze the data and predict the occurrence of events. Results Between 2016 and 2024, a total of 436 cases of non-occupational carbon monoxide poisoning were reported in Beijing, showing a downward trend. The established SARIMA model and seasonal index model were SARIMA (1,0,0) (1,1,0) 12, Yt = (-0.0339t+5.8863) × St, and the average relative errors were 65.42% and 29.19%, respectively. In terms of months, the SARIMA model had better predictive performance during April and summer (June to August), while the seasonal index model was superior in other months. By combining the two models, the predicted number of events in 2025 was as follows: 3, 2, 2, 3, 1, 5, 2, 7, 1, 1, 1, and 2. Conclusion The seasonal index model has the best prediction effect on the non-occupational carbon monoxide poisoning events in Beijing throughout the year, and the number of summer events predicted by SARIMA model is closer to the actual values. The two models can be combined to predict the trend of non-occupational carbon monoxide poisoning, which provides a scientific basis for the prevention and control of carbon monoxide poisoning in the future.

Objective:To analyze the temporal distribution characteristics of hand, foot and mouth disease (HFMD) in Beijing and provide reference evidence in HFMD prevention and control.Methods:The monthly incidence data of HFMD in Beijing from 2008 to 2023 were collected from Notifiable Disease Management Information System of the Chinese Information System of Disease Control and Prevention, and the epidemiological characteristics of HFMD were analyzed by the methods of time series seasonal decomposition graph, concentration degree, and circular distribution.The WPS office software 2019 was used to clean the data, Python software 3.12 was used to analyze and make statistical charts.Results:The monthly incidence fluctuation of HFMD in Beijing from 2008 to 2015 was higher than that from 2016 to 2022. From 2016 to 2022, the fluctuation range of monthly incidence showed a gradually decreasing trend.From 2008 to 2015, the concentration ( M) was 0.58, indicating a relatively strong seasonality; the mean angle ( α) calculated by the circular distribution method was 174.95°, and the mean angle standard deviation ( s) was 60.43°. The annual incidence peak occurred on June 27, and the incidence peak period was from April 27 to August 27. From 2016 to 2019 and 2023, the M was 0.57, indicating a relatively strong seasonality. The α was 228.05°, and s was 61.44°. The annual incidence peak occurred on August 20, and the incidence peak period was from June 18 to October 21. From 2020 to 2022, the M was 0.42, indicating a seasonality, the α was 238.27° and s was 76.35°. The annual incidence peak occurred on July 15, and the incidence peak period was from June 14 to November 14. The α of 2008-2015, 2016-2019 and 2023, and 2020-2022 were tested by the Watson-Williams method and the difference was statistically significant ( F=33 443.09, P<0.001). In 2023, the M was 0.77, indicating a strong seasonality. The incidence peak occurred on September 16, and the incidence peak period was from August 5 to October 28. Conclusions:The seasonality of HFMD in Beijing was obvious from 2008 to 2023, and the incidence peak day and peak period overall had rearward shifts. It is necessary to strengthen the comprehensive analysis of the distribution characteristics at different dimensions and the comprehensive prevention and control in key areas, places, and populations during the peak incidence period.