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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.
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COVID-19 , Infecções por Coronavirus , Coronavirus , Humanos , Pandemias , Bibliometria , COVID-19/epidemiologiaRESUMO
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. Supplementary Information: The online version contains supplementary material available at 10.1007/s10389-023-01843-2.
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Governments face difficulties in policy making in many areas such as health, food safety, and large-scale projects where public perceptions can be misplaced. For example, the adoption of the MMR vaccine has been opposed due to the publicity indicating an erroneous link between the vaccine and autism. This research proposes the "Parallel Evolution and Response Decision Framework for Public Sentiments" as a real-time decision-making method to simulate and control the public sentiment evolution mechanisms. This framework is based on the theories of Parallel Control and Management (PCM) and System Dynamics (SD) and includes four iterative steps: namely, SD modelling, simulating, optimizing, and controlling. A concrete case of an anti-nuclear mass incident that sparked public sentiment in China is introduced as a study sample to test the effectiveness of the proposed method. In addition, the results indicate the effects by adjusting the key control variables of response strategies. These variables include response time, response capacity, and transparency of the government regarding public sentiment. Furthermore, the advantages and disadvantages of the proposed method will be analyzed to determine how it can be used by policy makers in predicting public opinion and offering effective response strategies.
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Numerous studies examined the association between excision repair complementation group 1 (ERCC1) expression and the prognosis of gastric cancer patients receiving platinum-based chemotherapy but yielded controversial results. We thus conducted a meta-analysis to quantitatively evaluate the prognostic value of ERCC1 expression in gastric cancer patients receiving platinum-based chemotherapy. A systematic literature search was performed to identify relevant studies in PubMed, Web of Science, Embase, Cochrane Library, China National Knowledge Infrastructure, Chinese Biomedical Literature Database, and WanFang Database up to December 17, 2013. Pooled hazard ratios (HRs) or odds ratios (ORs) with 95 % confidence intervals (CIs) were estimated. Moreover, meta-regression analysis and subgroup analysis were conducted according to ethnicity, HR extraction, detection methods, survival analysis, and quality score. A total of 1,409 patients from 21 studies were subjected to final analysis. Positive/high ERCC1 expression was significantly associated with poorer overall survival (HR, 1.58; 95 % CI, 1.09-2.28), especially in Asians (HR, 1.81; 95 % CI, 1.20-2.73), and lower response rate (OR, 0.26; 95 % CI, 0.18-0.36), but not with clinicopathological features, such as gender (OR, 1.01; 95 % CI, 0.68-1.51), grade (OR, 0.66; 95 % CI, 0.43-1.01), and stage (OR, 1.05; 95 % CI, 0.58-1.90). This meta-analysis suggested that ERCC1 expression might be a useful biomarker to predict response and survival for gastric cancer patients receiving platinum-based chemotherapy, particularly in Asians.
