Multimodal medical data provides a wide and real basis for disease diagnosis. Computer-aided diagnosis (CAD) powered by artificial intelligence (AI) is becoming increasingly prominent in disease diagnosis. CAD for multimodal medical data requires addressing the issues of data fusion and prediction. Traditionally, the prediction performance of CAD models has not been good enough due to the complicated dimensionality reduction. Therefore, this paper proposes a fusion and prediction model—EPGC—for multimodal medical data based on graph neural networks. Firstly, we select features from unstructured multimodal medical data and quantify them. Then, we transform the multimodal medical data into a graph data structure by establishing each patient as a node, and establishing edges based on the similarity of features between the patients. Normalization of data is also essential in this process. Finally, we build a node prediction model based on graph neural networks and predict the node classification, which predicts the patients’ diseases. The model is validated on two publicly available datasets of heart diseases. Compared to the existing models that typically involve dimensionality reduction, classification, or the establishment of complex deep learning networks, the proposed model achieves outstanding results with the experimental dataset. This demonstrates that the fusion and diagnosis of multimodal data can be effectively achieved without dimension reduction or intricate deep learning networks. We take pride in exploring unstructured multimodal medical data using deep learning and hope to make breakthroughs in various fields.

The first case of Corona Virus Disease 2019 (COVID-19) was reported in Wuhan, China in December 2019. Since then, COVID-19 has quickly spread out to all provinces in China and over 150 countries or territories in the world. With the first level response to public health emergencies (FLRPHE) launched over the country, the outbreak of COVID-19 in China is achieving under control in China. We develop a mathematical model based on the epidemiology of COVID-19, incorporating the isolation of healthy people, confirmed cases and contact tracing measures. We calculate the basic reproduction numbers 2.5 in China (excluding Hubei province) and 2.9 in Hubei province with the initial time on January 30 which shows the severe infectivity of COVID-19, and verify that the current isolation method effectively contains the transmission of COVID-19. Under the isolation of healthy people, confirmed cases and contact tracing measures, we find a noteworthy phenomenon that is the second epidemic of COVID-19 and estimate the peak time and value and the cumulative number of cases. Simulations show that the contact tracing measures can efficiently contain the transmission of the second epidemic of COVID-19. With the isolation of all susceptible people or all infectious people or both, there is no second epidemic of COVID-19. Furthermore, resumption of work and study can increase the transmission risk of the second epidemic of COVID-19.

Since late 2020, clade 2.3.4.4b H5N1 highly pathogenic avian influenza viruses (HPAIVs) have expanded globally, infecting wild birds, poultry, and an increasing range of mammals. Sustained transmission has occurred in minks and dairy cattle, accompanied by key polymerase basic 2 (PB2) mutations (e.g., E627K, D701N), signaling mammalian adaptation and heightened zoonotic risk. From 2021 to early 2025, 82 human cases, including fatalities in China and the US, underscored rising public health concerns, although no sustained human-to-human transmission has been detected. This review summarizes the epidemiology, transmission dynamics, and molecular evolution of clade 2.3.4.4b H5N1 in mammals. Large-scale outbreaks in US dairy cattle and associated human cases highlight significant cross-species risks, while China’s high poultry vaccination coverage, rapid culling, and nationwide surveillance demonstrate the value of proactive prevention. These contrasting experiences emphasize the need for targeted surveillance, reinforced biosecurity, accelerated countermeasure development, and global coordination under the One Health framework.

Introduction The increasing trend of globalization has led to a heightened risk of imported epidemics; however, existing surveillance systems remain fragmented and reliant on laboratory confirmation. We developed an open-source data-driven hybrid modeling system to provide earlier and more reliable alerts, designed to complement China’s multipoint trigger early-warning framework. Methods This system integrates heterogeneous signals, including official epidemiology, digital traces, mobility, meteorology, and pathogen genomics, using semantic harmonization and a hybrid analytic stack. Seasonality-adjusted baselines with anomaly detection, mobility- and climate-aware SEIR models, and short-horizon learners generated calibrated early-warning scores. Thresholds were constrained by positive predictive value. Pilot studies were conducted for coronavirus disease 2019 (COVID-19) in Yantai and severe fever with thrombocytopenia syndrome virus (SFTSV) in Shandong and Henan, with tuberculosis indicators embedded for programmatic use. Results Across deployments, the system achieved 83.3% sensitivity and 76.9% positive predictive value, providing a median lead time of 9.3 days before official confirmation. Forecasting accuracy reached 92.1% for COVID-19 in Yantai, 90.3% for SFTSV in Shandong, and 89.8% for SFTSV in Henan. Early warnings were aligned with subsequent confirmations and supported targeted screening and resource allocation. Conclusion An open-source data-driven hybrid modeling system can deliver calibrated and timely alerts across diverse pathogens. By broadening inputs, enabling cross-agency linkage, and offering operator-oriented dashboards, it serves as a practical complement to China’s national early-warning system and has the potential for scaling out with One Health inputs.

China CDC Weekly, first published in 2019 by China CDC, is an authoritative, trusted resource for public and global health research.