Rapid urbanization has increased the risks and complexity of multi-hazard urban disasters due to various risk factors such as natural disasters, accidents, public security incidents and public health incidents. Prevention, control and emergency responses to multi-hazard urban disasters need fusion analyses using more comprehensive multi-dimensional and cross-disciplinary big data. This study develops a fusion analysis system for cyber-physical-social-cognitive (CPS-C) domains integrated with three layer "planning-tactics- operations" responses. The fusion analysis then works in conjunction with an emergency management framework with the "objective-scenario-mission-resource-response-assessment-decision" procedure. The management of an urban waterlogging emergency is used as a case study with the planning goal based on a typical case analysis and the operations analysis based on a disaster cause analysis. The tactics analysis analyzes the emergency process and key elements to either prevent or control the urban waterlogging for the scenario of emergency management of waterlogging disasters. This method is applied to a virtual interactive scenario simulation of a typhoon induced waterlogging disaster. This method provides theoretical and technical support for emergency management of urban complex disasters.

We used the Bass model to investigate the transmission dynamics of COVID-19 taking the United States and China as examples. The Bass model was originated from business literature and initially modeled the process of new products getting adopted by the population with an external and internal influence term. First, we fit the cumulative number of confirmed COVID-19 cases in 8 major cities in the United States with the Bass model. The external and internal parameters of Bass were calculated and correlation analyses were performed between the parameters and the volume of traveling across different cities and within a city. The results show that the Bass model fits the epidemics data better than the logistic distribution which only has an internal influence term and the SIR model which is a classical infectious disease model. Besides, there is a significant positive correlation between the external parameter of Bass and the number of passengers at the airport as well as between the internal parameter of Bass and the number of short-distance trips in a city. Therefore, it is closer to true circumstances considering both external and internal transmission rather than assuming a region to be isolated. The external infection rate rises as the number of enplanements rises and the internal infection rate rises as the number of short-distance trips in a city rises. Second, we put forward an adapted multi-center Bass model for the multi-chain COVID-19 transmission in China and compared it with the original Bass model. The results indicated that the accuracy of the multi-center Bass model was higher than that of the original Bass model. In conclusion, the Bass model distinguishes the external and internal effects and is suitable for simulating the spread of COVID-19 and analyzing the infection rate caused by social interactions among different regions and inside a region. The adapted multi-center Bass model commendably described disease transmission when there is more than one transmission center. Our research proves the Bass model to be a useful tool for fine-level analyses on the transmission mechanism of COVID-19.

This paper describes the development of an inverse model to identify the fire location and intensity level based on sensor data and physical-model output for use with a decentralized control system. The model was proven to be accurate and effective. The inverse procedure can be divided into two steps. In the first step, the fire estimation is conducted in pair-wise adjacent zones based on Bayesian inference. A local relative probability ratio is calculated. In the second step, the global inference is derived from the cooperation of all zones. For demonstration purposes, the model was applied to a decentralized control system with limited information of the building structure and sensor readings. Based on the building layout, the fire location and intensity level can be determined effectively. The results from the decentralized control are compared with those obtained for conventional centralized control with full information of the building structure and sensor readings. The decentralized fire estimation model proved to be accurate for practical applications. The results were also found to be insensitive to the traversal path selection in the topology of zones.