Given the non-repeatability, complexity, and unpredictability of unconventional public health emergencies, building accurate models and making effective response decisions based only on traditional prediction–response decision-making methods are difficult. To solve this problem, under the scenario–response paradigm and theories on parallel emergency management and discrete event system (DES), the parallel simulation decision-making framework (PSDF), which includes the methods of abstract modeling, simulation operation, decision-making optimization, and parallel control, is proposed for unconventional public health emergency response processes. Furthermore, with the example of the severe acute respiratory syndrome (SARS) response process, the evolutionary scenarios that include infected patients and diagnostic processes are transformed into simulation processes. Then, the validity and operability of the DES–PSDF method proposed in this paper are verified by the results of a simulation experiment. The results demonstrated that, in the case of insufficient prior knowledge, effective parallel simulation models can be constructed and improved dynamically by multi-stage parallel controlling. Public health system bottlenecks and relevant effective response solutions can also be obtained by iterative simulation and optimizing decisions. To meet the urgent requirements of emergency response, the DES–PSDF method introduces a new response decision-making concept for unconventional public health emergencies.

Negative online public sentiment generated by government mishandling of pandemics and other disasters can easily trigger widespread panic and distrust, causing great harm. It is important to understand the law of public sentiment dissemination and use it in a timely and appropriate way. Using the big data of online public sentiment during the COVID-19 period, this paper analyzes and establishes a cross-validation based public sentiment system dynamics model which can simulate the evolution processes of public sentiment under the effects of individual behaviors and governmental guidance measures. A concrete case of a violation of relevant regulations during COVID-19 epidemic that sparked public sentiment in China is introduced as a study sample to test the effectiveness of the proposed method. By running the model, the results show that an increase in government responsiveness contributes to the spread of positive social sentiment but also promotes negative sentiment. Positive individual behavior suppresses negative emotions while promoting the spread of positive emotions. Changes in the disaster context (epidemic) have an impact on the spread of sentiment, but the effect is mediocre.

Oil tankers carry large quantities of liquefied chemical cargoes that are flammable, explosive and/or toxic. Hence, a collision with a tanker that causes an oil spill poses a severe threat to the marine environment and human life. In order to quantify and analyze the risk factors of ship collision oil spill, this paper adopts a combination of knowledge element (KE) and dynamic Bayesian networks (DBN) to conduct an emergency scenario study based on the “scenario-response” model. Firstly, the key elements of “accident scenario state, human factors, emergency measures, and emergency goals” are selected to represent the accident. Then, the mechanism of accident evolution is analyzed according to the case, and DBN is used to build a scenario model of oil spills from tanker collisions. Finally, to verify the importance of human factors and the scientificity of emergency measures, the oil spill accident due to the collision between the two vessels known as MT “SANCHI” and MV “CF CRYSTAL” is used as an example for analysis. The accident model deduction results are in line with reality, and the research results help relevant decision makers to understand the deduction process of oil spills from tanker collisions, which is of great significance to enhance the safety of oil tanker shipping and marine environmental protection.