To evaluate the application value of the ARIMA-GM(1,1) combined model in predicting the resistance rates of key drug-resistant bacteria in China, providing a scientific basis for optimizing antimicrobial management strategies. Based on data from the China Antibacterial Resistance Surveillance Network from 2014 to 2023, we selected six types of key drug-resistant bacteria, including methicillin-resistant Staphylococcus aureus (MRSA) and cefotaxime/ceftriaxone-resistant Klebsiella pneumoniae (CTX/CRO-R-KP), to construct the ARIMA-GM(1,1) combined model. The model performance was evaluated using five metrics: Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) and R2, and the resistance rate trends for 2024–2028 were predicted. The model exhibited strong predictive performance, with MSE, RMSE, MAE, and MAPE below 10 for all six strains, and R2 values exceeding 0.8 for five strains. Projected resistance rates for 2024 are as follows: MRSA at 27.46% (95% CI: 26.90%-28.02%), CTX/CRO-R-KP at 26.47% (25.81%-27.12%), CRKP at 9.87% (8.99%-10.74%), and CRPA at 15.17% (14.10%-16.24%). By 2028, these rates are expected to decrease to 24.05% (22.81%-25.29%), 22.30% (20.82%-23.77%), 7.58% (6.53%-8.63%), and 11.01% (8.61%-13.40%) (all P < 0.05). For 2024, resistance rates for CREC and CRAB are projected to be 1.63% (1.58%-1.68%) and 53.33% (51.37%-55.28%), respectively, with continued decline expected by 2028. The ARIMA-GM(1,1) model has been statistically validated in predicting the resistance rates of MRSA, CTX/CRO-R-KP, CRKP, and CRPA, indicating a significant downward trend driven by the National Action Plan for Curbing Bacterial Drug Resistance (2016–2020). While effective in capturing temporal dynamics, future research should integrate antibiotic usage data and other influencing factors for more targeted interventions.

Infectious diseases are a major global public health challenge. Socioeconomic and environmental changes have complicated the situation, underscoring the need for effective predictive models and key factor identification for prevention and control. The study aims to explore the establishment of an optimal machine learning model adaptation system for infectious diseases to provide standardized data support and reusable model references for related follow-up research. This study included infectious disease incidence data from the Global Burden of Disease 2021 database, as well as 19 factors including socioeconomic and air pollution indicators from the Our World in Data platform. Eight machine learning models were included, such as Gradient Boosting Decision Tree (GBDT), Light Gradient Boosting Machine (LightGBM), and Random Forest (RF). Model performance was evaluated using five metrics, including the Coefficient of Determination (R²). The SHapley Additive exPlanations (SHAP) method was applied to quantify the contributions of factors at both global and regional levels. The GBDT performed best for sexually transmitted (R² = 0.506) and respiratory infectious diseases (R² = 0.854); RF was optimal for all infectious diseases (R² = 0.662); LightGBM suited enteric infectious diseases (R² = 0.725); GBDT also achieved the optimal performance for neglected tropical diseases (R² = 0.434). SHAP identified core factors: child dependency ratio (SHAP = 0.47 for diarrheal diseases), unsafe drinking water (SHAP = 0.23 for all infectious diseases), particulate matter 2.5 exposure (SHAP = 0.19 for respiratory infectious diseases), and socio-demographic index (SHAP = 0.17 for other types). Adapted machine learning models efficiently identify key factors. Core strategies to reduce the global infectious disease burden include improving drinking water safety, promoting clean energy, optimizing diets, and strengthening child health services.

Hepatitis B virus (HBV) infection remains a significant global public health challenge. Hubei Province, a historically high-prevalence area in central China, continues to face persistently high rates of adult HBsAg carriers despite effective reduction of childhood transmission through universal vaccination, hindering progress toward HBV elimination goals. This study aims to identify key drivers behind the rising hepatitis B incidence in the region, forecast future trends, and inform targeted control strategies. This population-based spatiotemporal epidemiological study analyzed hepatitis B surveillance data from Hubei Province between January 2015 and December 2024, covering 631,361 reported cases across 17 prefecture-level divisions. Spatial clustering was assessed using Global Moran's I statistics. Urbanization-related spatial drivers were identified via geographically weighted random forest modeling with SHAP value interpretation, and future incidence trends were predicted using Holt-Winters time series forecasting. The average annual incidence was 106.19 per 100,000 population, showing a U-shaped temporal trend: declining from 111.75 per 100,000 in 2015 to 83.58 in 2020, then rising to 125.86 in 2024. Adults aged 30-50 years consistently accounted for 40-50% of total cases. Significant spatial clustering was detected (Global Moran's I: 0.124-0.307, P < 0.05), with persistent high-risk zones concentrated in the Wuhan metropolitan area. The geographically weighted random forest model identified urbanization rate as the primary spatial driver (SHAP value ≈2.0), followed by higher education enrollment rate (≈1.0) and passenger traffic volume (≈0.8). Time series forecasting projected monthly incidence rates of 8.98-10.41 per 100,000 over the subsequent 18 months (R² = 0.892, MAPE = 1.74%). In Hubei Province, the hepatitis B burden has concentrated in adults born before the vaccine era, underscoring horizontal transmission risks in this susceptible cohort. Urbanization significantly impacts the spatial distribution of hepatitis B, with high-risk areas mainly concentrated in the Wuhan metropolitan region. It is crucial to address urbanization-related factors affecting HBV transmission and to implement targeted screening and revaccination for adults aged 30-50, particularly among high-risk individuals, mobile populations, and rural residents, to reduce incidence and alleviate the regional disease burden.

The rapid transmission and severe public health impacts of influenza A highlight the critical importance of accurate epidemic prediction for effective prev