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- W4281263014 abstract "• Structurally describes the problem scenario of public health emergencies. • Adopts a Markov model to analyze the scenario evolution. • Builds a dynamic case-based reasoning model. • Estimates the similarity between the target and historical scenarios. • Provides an optimized dynamic emergency decision guide. Infectious diseases are a global public health problem, which requires timely and effective responses. This study proposes a novel model that contributes to the development of such responses. First, the problem scenario features of infectious disease emergency scenarios are extracted, and the problem scenario is structurally described. A Markov model is adopted to analyze the scenario evolution of the infectious disease outbreaks. Then, a dynamic case-based reasoning model is built. Different matching algorithms are designed for crisp symbols, crisp numbers, interval numbers, and fuzzy linguistic variables. The similarity between the target scenario and various historical scenarios is calculated. Finally, an optimized dynamic emergency decision guide is provided. An experiment is conducted to test the validity and feasibility of the proposed method. The results suggest that the model can realistically simulate the process of infectious disease outbreaks and quickly match the recorded scenarios to generate effective and real-time responses." @default.
- W4281263014 created "2022-05-23" @default.
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- W4281263014 date "2022-10-01" @default.
- W4281263014 modified "2023-09-27" @default.
- W4281263014 title "A dynamic case-based reasoning system for responding to infectious disease outbreaks" @default.
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- W4281263014 doi "https://doi.org/10.1016/j.eswa.2022.117628" @default.
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