Scenario deduction and analysis for spillover risks of international sports events under influence of COVID-19

In order to improve risk prevention and control capabilities for international sports events under the background of COVID-19, case data of 23 international sports since the pandemic outbreak were collected, and an evolutionary network model with COVID-19 as risk source was established. Then, risk analysis on the model was carried out based on in-and-out degree, number of sub-net nodes, the shortest path and average path of complex network theory, key risk event nodes were identified, and preventive measures were put forward. Finally, critical chains were obtained by analyzing causal mechanism and types of risk chains, and countermeasures and suggestions for chain disconnection and disaster mitigation were put forward. The results show that severe epidemic situation and rising risk of virus transmission in host cities are the key nodes in evolutionary network, and cycle chain of political relations and public opinion is the most destructive one. Therefore, it is necessary to promote the development of a public opinion monitoring system and strengthen positive publicity of sports events. © 2020 China Safety Science Journal. All rights reserved.

Scenario deduction and analysis for spillover risks of international sports events under influence of COVID-19

In order to improve risk prevention and control capabilities for international sports events under the background of COVID-19, case data of 23 international sports since the pandemic outbreak were collected, and an evolutionary network model with COVID-19 as risk source was established. Then, risk analysis on the model was carried out based on in-and-out degree, number of sub-net nodes, the shortest path and average path of complex network theory, key risk event nodes were identified, and preventive measures were put forward. Finally, critical chains were obtained by analyzing causal mechanism and types of risk chains, and countermeasures and suggestions for chain disconnection and disaster mitigation were put forward. The results show that severe epidemic situation and rising risk of virus transmission in host cities are the key nodes in evolutionary network, and cycle chain of political relations and public opinion is the most destructive one. Therefore, it is necessary to promote the development of a public opinion monitoring system and strengthen positive publicity of sports events. © 2020 China Safety Science Journal. All rights reserved.

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.

Evaluating the Effectiveness of the COVID-19 Emergency Outbreak Prevention and Control Based on CIA-ISM.

The COVID-19 pandemic, characterized by high uncertainty and difficulty in prevention and control, has caused significant disasters in human society. In this situation, emergency management of pandemic prevention and control is essential to reduce the pandemic's devastation and rapidly restore economic and social stability. Few studies have focused on a scenario analysis of the entire emergency response process. To fill this research gap, this paper applies a cross impact analysis (CIA) and interpretive structural modeling (ISM) approach to analyze emergency scenarios and evaluate the effectiveness of emergency management during the COVID-19 crisis for outbreak prevention and control. First, the model extracts the critical events for COVID-19 epidemic prevention and control, including source, process, and resultant events. Subsequently, we generated different emergency management scenarios according to different impact levels and conducted scenario deduction and analysis. A CIA-ISM based scenario modeling approach is applied to COVID-19 emergency management in Nanjing city, China, and the results of the scenario projection are compared with actual situations to prove the validity of the approach. The results show that CIA-ISM based scenario modeling can realize critical event identification, scenario generation, and evolutionary scenario deduction in epidemic prevention and control. This method effectively handles the complexity and uncertainty of epidemic prevention and control and provides insights that can be utilized by emergency managers to achieve effective epidemic prevention and control.

Scenario prediction of public health emergencies using infectious disease dynamics model and dynamic Bayes.

This study was aimed to discuss the predictive value of infectious disease dynamics model (IDD model) and dynamic Bayesian network (DBN) for scenario deduction of public health emergencies (PHEs). Based on the evolution law of PHEs and the meta-scenario representation of basic knowledge, this study established a DBN scenario deduction model for scenario deduction and evolution path analysis of PHEs. At the same time, based on the average field dynamics model of the SIR network, the dimensionality reduction process was performed to calculate the epidemic scale and epidemic time based on the IDD model, so as to determine the calculation methods of threshold value and epidemic time under emergency measures (quarantine). The Corona Virus Disease (COVID) epidemic was undertaken as an example to analyze the results of DBN scenario deduction, and the infectious disease dynamics model was used to analyze the number of reproductive numbers, peak arrival time, epidemic time, and latency time of the COVID epidemic. It was found that after the M1 measure was used to process the S1 state, the state probability and the probability of being true (T) were the highest, which were 91.05 and 90.21, respectively. In the sixth stage of the development of the epidemic, the epidemic had developed to level 5, the number of infected people was about 26, and the estimated loss was about 220 million yuan. The comprehensive cumulative foreground (CF) values of O1  ∼  O3 schemes were -1.34, -1.21, and -0.77, respectively, and the final CF values were -1.35, 0.01, and -0.08, respectively. The final CF value of O2 was significantly higher than the other two options. The household infection probability was the highest, which was 0.37 and 0.35 in Wuhan and China, respectively. Under the measures of home quarantine, the numbers of confirmed cases of COVID in China and Wuhan were 1.503 (95% confidential interval (CI) = 1.328  ∼  1.518) and 1.729 (95% CI = 1.107  ∼  1.264), respectively, showing good fits with the real data. On the 21st day after the quarantine measures were taken, the number of COVID across the country had an obvious peak, with the confirmed cases of 24495, and the model prediction value was 24085 (95% CI = 23988  ∼  25056). The incubation period 1/q was shortened from 8 days to 3 days, and the number of confirmed cases showed an upward trend. The peak period of confirmed cases was advanced, shortening the overall epidemic time. It showed that the prediction results of scenario deduction based on DBN were basically consistent with the actual development scenario and development status of the epidemic. It could provide corresponding decisions for the prevention and control of COVID based on the relevant parameters of the infectious disease dynamic model, which verified the rationality and feasibility of the scenario deduction method proposed in this study.