A national risk analysis model (NRAM) for the assessment of COVID-19 epidemic.

COVID-19 has caused a critical health concern and severe economic crisis worldwide. With multiple variants, the epidemic has triggered waves of mass transmission for nearly 3 years. In order to coordinate epidemic control and economic development, it is important to support decision-making on precautions or prevention measures based on the risk analysis for different countries. This study proposes a national risk analysis model (NRAM) combining Bayesian network (BN) with other methods. The model is built and applied through three steps. (1) The key factors affecting the epidemic spreading are identified to form the nodes of BN. Then, each node can be assigned state values after data collection and analysis. (2) The model (NRAM) will be built through the determination of the structure and parameters of the network based on some integrated methods. (3) The model will be applied to scenario deduction and sensitivity analysis to support decision-making in the context of COVID-19. Through the comparison with other models, NRAM shows better performance in the assessment of spreading risk at different countries. Moreover, the model reveals that the higher education level and stricter government measures can achieve better epidemic prevention and control effects. This study provides a new insight into the prevention and control of COVID-19 at the national level.

Early detection of emergency events from social media: a new text clustering approach

Emergency events require early detection, quick response, and accurate recovery. In the era of big data, social media users can be seen as social sensors to monitor real-time emergency events. This paper proposed an integrated approach to detect all four kinds of emergency events early, including natural disasters, man-made accidents, public health events, and social security events. First, the BERT-Att-BiLSTM model is used to detect emergency-related posts from massive and irrelevant data. Then, the 3 W attribute information (what, where, and when) of the emergency event is extracted. With the 3 W attribute information, we create an unsupervised dynamical event clustering algorithm based on text similarity and combine it with the supervised logistical regression model to cluster posts into different events. Experiments on Sina Weibo data demonstrate the superiority of the proposed framework. Case studies on some real emergency events show that the proposed framework has good performance and high timeliness. Practical applications of the framework are also discussed, followed by future directions for improvement.

Analysis and comparison of comprehensive risk index of covid-19 in China and Hubei Province (Special Section: COVID-19) [Chinese]

The comprehensive risk assessment of COVID-19 is a key approach to the comprehensive epidemic risk management while the assessment method is the key to the epidemic risk assessment. This paper uses the public security "triangular model" and considers the three factors, i.e., the incident(the hazard of the virus itself), the vulnerability of hazard-affected carriers(the vulnerability of vulnerable groups), and the effectiveness of emergency management to establish a multidimensional risk assessment method, which can help to provide effective method for the next epidemic response and plans of resource allocation. According to the assessment results, the top five provinces with highest comprehensive risk are respectively Hubei, Shanghai, Hong Kong, Beijing and Guangdong. Within Hubei province, the top four cities are Wuhan, E'zhou, Xiaogan and Huanggang.