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Assessment on the anti-epidemic measures: Mathematical model establishment for COVID-19 epidemic and application of recurrent neural network

How to evaluate the effectiveness of epidemic prevention measures scientifically and reasonably has become the most urgent task for governments around the world. The rapid spread of the coronavirus has brought great challenges to the global community. In this paper, the effectiveness of the following three epidemic prevention measures, including makeshift hospitals, closed cities, and wearing masks, is evaluated by establishing relevant mathematical models and applying the circulating neural network. For the scenes of closed cities and makeshift hospitals, the improved model of SEIR eight cabins is established in this paper, and the parameters are updated in real-time by Long Short-Term Memory. The following results are obtained: the closed city measures greatly reduce the probability of the transfer of susceptible persons to latent ones, and the epidemic is effectively controlled. Regarding the issue of wearing masks, this paper established the MUEIR model and solved with a particle swarm optimization algorithm. It is concluded that the number of infected people decreased by 42% compared with the natural situation, indicating the effectiveness of wearing masks. In conclusion, the effectiveness of the above three epidemic prevention measures is scientifically evaluated, and artificial intelligence technology is combined to achieve intelligent dynamic prediction of the epidemic development trend.