Hand, foot, and mouth disease (HFMD) is a significant public health issue in China, and numerous studies have indicated a close association between HFMD incidence and meteorological factors. This study aims to investigate the relationship between meteorological factors and HFMD in Yangzhou City, Jiangsu Province, China.HFMD case reports and meteorological data from Yangzhou City between 2017 and 2022 were extracted from the National Notifiable Infectious Disease Surveillance System and the Meteorological Data Sharing Service System, respectively. A generalized additive model (GAM) was employed to assess the exposure-response relationship between meteorological factors and HFMD. Subsequently, a distributed lag nonlinear model (DLNM) was used to explore the exposure-lag-effect of meteorological factors on HFMD.HFMD in Yangzhou City exhibits obvious seasonality and periodicity. There is an inverted "U" shaped relationship between average temperature and the risk of HFMD, with the maximum lag effect observed at a temperature of 25°C with lag 0 day (RR = 2.07, 95% CI: 1.74-2.47). As the duration of sunshine and relative humidity increase, the risk of HFMD continuously rises, with the maximum lag effect observed at a sunshine duration of 12.4 h with a lag of 14 days (RR = 2.10, 95% CI: 1.17-3.77), and a relative humidity of 28% with a lag of 14 days (RR = 1.21, 95% CI: 1.01-1.64). There is a "U" shaped relationship between average atmospheric pressure and the risk of HFMD, with the maximum effect observed at an atmospheric pressure of 989 hPa with no lag (RR = 1.45, 95% CI: 1.25-1.69). As precipitation increases, the risk of HFMD decreases, with the maximum effect observed at a precipitation of 151 mm with a lag of 14 days (RR = 1.45, 95% CI: 1.19-2.53).Meteorological factors including average temperature, average atmospheric pressure, relative humidity, precipitation, and sunshine duration significantly influenced the risk of HFMD in Yangzhou City. Effective prevention measures for HFMD should be implemented, taking into account the local climate conditions.
Abstract Objective: Scarlet fever is an increasingly serious public health problem that has attracted widespread attention worldwide. In this study, two models were constructed based on time series to predict the number of scarlet fever incidence in Jiangsu province, China Methods: Two models, ARIMA model and TBATS model, were constructed to predict the number of scarlet fever incidence in Jiangsu province, China, in the first half of 2022 based on the number of scarlet fever incidence from 2013-2021, and root mean square error (RMSE) and mean absolute percentage error (MAPE) were used to select the models and evaluate the performance of the models. Results: The incidence of scarlet fever in Jiangsu province from 2013 to 2021 was significantly bi-seasonal and trendy, and the best ARIMA model established was ARIMA(1,0,1)(2,1,1) 12 , with RMSE=92.23 and MAPE=47.48% for the fitting part and RMSE=138.31 and MAPE=79.11 for the prediction part. The best The best TBATS model is TBATS(0.278,{0,0}, -, {<12,5>}) with RMSE=69.85 and MAPE=27.44% for the fitted part. The RMSE of the prediction part=57.11, MAPE=39.52%. The error of TBATS is smaller than that of ARIMA model for both fitting and forecasting. Conclusion: The TBATS model outperformed the most commonly used SARIMA model in predicting the number of scarlet fever incidence in Jiangsu Province, China, and can be used as a flexible and useful tool in the decision-making process of scarlet fever prevention and control in Jiangsu Province
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