A Model of Artificial Prevention and Control Measures for COVID-19 Isolation and Reception and Cure Based on Discrete Grids

With the outbreak of coronavirus disease 2019 (COVID-19) in the world, researches on the related epidemic situation are also constantly increasing. However, the current researches focus more on the prediction analysis and the researches on epidemic situation prevention and control measures, remain at the statistical level and the model parameters lack spatiotemporal evolution description. This paper introduces the granularity and virtual real line of the boundary of the discrete grid to describe the tightness of physical isolation measures and the connectivity and isolation of adjacent spaces separately and designs the medical reception and cure model under the discrete grid based on the spatial autocorrelation between the medical bed admission capacity and the grid. Furthermore, the LSEIR (logistic-susceptible-exposed-infected-removed) epidemic model is used to construct the artificial prevention and control measures model of physical isolation and medical reception and cure under the discrete grid, which provides an effective method to analyze and assess the impacts of the artificial prevention and control measures model of physical isolation and medical reception and cure on the spread and prevention and control of the epidemic situation. The original spatiotemporal evolution of COVID-19 epidemic situation in Wuhan, China was simulated with the early data of epidemic of the United States, Germany, Spain, and the United Kingdom, the experimental analysis result of epidemic situation data in Wuhan, China shows that physical isolation measures have a very obvious effect on reducing the peak value of infected population, advancing the peak of the inflection point and shortening the duration of the epidemic situation; medical reception and cure measures can effectively reduce the peak value of the infected population in the early stage of the epidemic, but has no significant impact on the advance of the peak inflection point and the shortening of the epidemic duration; the model can analyze and assess the impacts of physical isolation and medical reception and cure measures on the epidemic situation from both quantitative and qualitative perspectives, which has high rationality and correctness.