Objective To compare the performance of seasonal difference autoregressive moving average exogenous variable model (SARIMAX), backpropagation neural network (BPNN), long short-term memory network (LSTM), and various combination models in fitting and predicting the incidence of hand, foot, and mouth disease (HFMD), and to provide a reference for optimizing HFMD prediction and prevention and control decisions. Methods Taking the monthly HFMD incidence data in Changsha from 2010 to 2024 as the training set, and the monthly HFMD incidence data from January to August 2025 as the validation set, the single models, SARIMAX-BPNN, SARIMAX-LSTM and weight combination models were constructed respectively. The R², mean absolute percentage error (MAPE), mean absolute error (MAE), and root mean square error (RMSE) were used to evaluate the model fitting and prediction performance. Results Based solely on MAPE, the LSTM model showed the best fitting and prediction performance. A comprehensive analysis of multiple indicators including R², MAPE, MAE, and RMSE indicated that the combination models had superior prediction performance. Compared with the LSTM, RMSE predicted by equal weight combination, different weight combination and SARIMAX-BPNN decreased by 40.97%, 37.50%, and 25.00%, respectively, while MAPE increased by 124.84%, 96.90%, and 89.69%, respectively. The fitting effect was as follows: LSTM > unequal weight combination > equal weight combination > SARIMAX-LSTM > SARIMAX-BPNN > BPNN > SARIMAX. The prediction performance was as follows: SARIMAX-BPNN > unequal weight combination > equal weight combination > LSTM > SARIMAX-LSTM > BPNN > SARIMAX. Conclusion SARIMAX-BPNN combination or weighted combination of multiple different models are two more robust combination strategies for predicting the incidence trend of hand, foot and mouth disease in Changsha.

Objective To analyze the current epidemic status of soil-transmitted helminthiasis (STHs) in China, providing reference data for the prevention and control of soil-transmitted helminthiasis. Methods Surveillance data on soil-transmitted helminthiasis in China from 2017 to 2022 were collected and statistically analyzed to investigate the infection status, infection trends, and spatial autocorrelation. Results　From 2017 to 2022, the average annual infection rate (AAIR) of soil-transmitted helminthiasis was 1.12% (25 123/2 251 632), with hookworm, roundworm, and whipworm having AAIRs of 0.70%, 0.26%, and 0.20% (15 827 cases, 5 836 cases and 4 586 cases), respectively. The infection rate of soil-transmitted helminths decreased annually by an average of 17.16% (AAPC =-17.16, P=0.007), with annual decreases in the infection rates of hookworm, roundworm, and whipworm being 14.70%, 22.13%, and 20.56%, respectively (AAPC=-14.70, P=0.025; AAPC=-22.13, P=0.015; AAPC =-20.56, P=0.029). The spatial distribution of hookworm, roundworm, and whipworm exhibited spatial positive correlation (Moran's I=0.09, P=0.121; Moran's I=0.46, P=0.001; Moran's I=0.23, P=0.013), with roundworm showing the highest spatial aggregation. The AAIRs of males and females were 1.03% (11 308/1 100 016) and 1.20% (13 851/1 149 286), respectively, with statistical significance (χ2=154.19, P<0.001). The highest AAIR was 1.85% (10 949/590 621) in individuals over 60 years old, with statistical significance across different age groups (χ2=4426.44, P<0.001). Hookworm, roundworm, and whipworm were mainly mild infections. The average annual positive rates of soil ascaris eggs and hookworms were 4.06% (649/15 984) and 2.45%(391/15 984), respectively, with statistical significance (χ2=66.16,P<0.001). 　Conclusions The infection rate of soil-transmitted helminthiasis in China is relatively low and shows a downward trend. Yet, there are local high-prevalence areas, with the elderly and females being the risk-prone populations. Comprehensive measures including expanding the monitoring scope, innovating monitoring technology, and promoting the rural toilet revolution are necessary to block the spread of soil-transmitted helminthiasis further.

Abstract: Objective To analyze the incidence trends and spatiotemporal distribution characteristics of dengue fever in China based on the data from the China Disease Control Information System and the "National Statutory Infectious Disease Epidemic Overview" published by the National Health Commission of the People's Republic of China, and to provide a scientific basis for the prevention and control of dengue fever epidemics. Methods The incidence rates and monthly case numbers of dengue fever in China from 2015 to 2023 were collected. Provinces were classified according to the severity of the epidemic and the distribution status of the Aedes vector. The incidence trends, seasonal distribution, and spatial aggregation of dengue fever in China were statistically analyzed. Results The top five provinces with the highest average annual incidence rates from 2015 to 2023 were Yunnan (6.16/100 000), Guangdong (1.70/100 000), Hainan (1.13/100 000), Fujian (1.13/100 000), and Chongqing (0.74/100 000). The average annual incidence rate of dengue fever in China increased by 70.79% from 2015 to 2019 (AAPC=70.79, P=0.045). In Class Ⅰ regions, the incidence of Guangxi and Hainan showed a linear regression trend, with average annual increases of 844.73% (AAPC=844.73, P=0.021) and 516.51% (AAPC=516.51, P=0.013), respectively. In Class Ⅱ regions, except for Shanghai, the incidence of other provinces showed a linear regression trend, with Jiangxi having the highest average annual increase of 610.16% (AAPC=610.16, P=0.021), followed by Chongqing at 345.12% (AAPC=345.12, P=0.038). In Class Ⅲ regions, the incidence of Hebei, Shanxi, and Liaoning had a linear regression trend, with average annual increases of 80.67% (AAPC=80.67, P=0.028), 202.31% (AAPC=202.31, P=0.001), and 70.19% (AAPC=70.19, P=0.031), respectively. The epidemic of dengue fever showed no obvious seasonality in 2021, strict seasonality in 2022, and strong seasonality in the rest of the years. The epidemic peak days were mostly concentrated in the middle and late September to early October of each year, and the epidemic peak periods were mainly concentrated in August-early November. From 2015 to 2018, the incidence rate had no spatial aggregation nationwide, and the incidence rate in 2019 had spatial aggregation nationwide. In 2015-2019 and 2023, there were no high-low gathering areas in the country, and high-high gathering areas appeared in 2018 (Fujian, Hainan) and 2019 (Guangxi). Conclusions The situation of dengue fever prevention and control in China is grim. High-risk epidemic areas of dengue fever will no longer be limited to Class Ⅰ provinces, and the risk of expansion to Class Ⅱ provinces is increasing.

Objective:To understand the epidemiological and clinical characteristics of human infection with avian influenza A(H3N8) virus and the molecular biological characteristics of the pathogen, and provide scientific evidence for the prevention and control of human infection with avian influenza A(H3N8) virus.Methods:An epidemiological investigation was conducted for a case of human infection with avian influenza A(H3N8) virus in Changsha in May 2022 to collect the information about exposure history, route of infection, onset and treatment, potential contacts and other possible exposures for a descriptive analysis. Nucleic acid detection and gene sequencing were used to detect the pathogen and analyze the genetic characteristics.Results:The case had a history of exposure to live poultry trading market 6 days before the onset of illness. Subtypes H3 and N8 of avian influenza virus were detected in live poultry markets. Deep gene sequencing showed that the virus had adaptive mutations in mammals, reduced sensitivity to alkamine agent, and no resistance mutations related to neuraminidase inhibitors and polymerase inhibitors were detected.Conclusion:The case was infected due to exposure to the live poultry market environment contaminated by avian influenza A(H3N8) virus, and no human to human transmission was found.