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.

Objective: Major epidemics have had a huge impact on the manufacturing industry. This study aimed to explore knowledge innovation in the field of emergency manufacturing during pandemics with a systematic quantitative analysis. Methods: Based on the Web of Science (WOS) Core Collection, the bibliometric method and the CiteSpace tool were used. Results: A total of 286 literature were obtained from the WOS database. During coronavirus disease (COVID-19), there was a surge in the number of publications. A new field of research on pandemic-triggered emergency manufacturing is gradually forming with accumulated research output. The analysis of the document co-citation showed how the research on pandemic situations and viruses brought emergency manufacturing into the research scope of scholars, and what attempts were made by the original scholars. Pandemic-triggered research hotspots and research trends in the post-pandemic era mainly boiled down to 3 aspects: technological innovation, material innovation, and management innovation in the field of emergency manufacturing. Conclusions: COVID-19 strengthened academic exchange and cooperation and promotes knowledge output in this field. This study provides an in-depth perspective for emergency manufacturing research and helps researchers realize the panorama of this field and establish future research directions.

The outbreak of unconventional emergencies leads to a surge in demand for emergency supplies. How to effectively arrange emergency production processes and improve production efficiency is significant. The emergency manufacturing systems are typically complex systems, which are difficult to be analyzed by using physical experiments. Based on the theory of Random Service System (RSS) and Parallel Emergency Management System (PeMS), a parallel simulation and optimization framework of production processes for surging demand of emergency supplies is constructed. Under this novel framework, an artificial system model paralleling with the real scenarios is established and optimized by the parallel implementation processes. Furthermore, a concrete example of mask shortage, which occurred at Huoshenshan Hospital in the COVID-19 pandemic, verifies the feasibility of this method.

Aim Nonpharmaceutical interventions (NPIs) are important strategies to utilize in reducing the negative systemic impact pandemic disasters have on human health. However, early on in the pandemic, the lack of prior knowledge and the rapidly changing nature of pandemics make it challenging to construct effective epidemiological models that can be used for anti-contagion decision-making. Subject and methods Based on the parallel control and management theory (PCM) and epidemiological models, we developed a Parallel Evolution and Control Framework for Epidemics (PECFE), which can optimize epidemiological models according to the dynamic information during the evolution of pandemics. Results The cross-application between PCM and epidemiological models enabled us to successfully construct an anti-contagion decision-making model for the early stages of COVID-19 in Wuhan, China. Using the model, we estimated the effects of gathering bans, intra-city traffic blockades, emergency hospitals, and disinfection, forecasted pandemic trends under different NPIs strategies, and analyzed specific strategies to prevent pandemic rebounds. Conclusion The successful simulation and forecasting of the pandemic showed that the PECFE could be effective in constructing decision models during pandemic outbreaks, which is crucial for emergency management where every second counts.