Search

Current epidemic models mainly estimate the number of confirmed patients by fitting statistical data. Few studies consider the direct effect of fine-grained spatial crowd mobile interaction on the spatial-temporal diffusion features. A new method for estimating the spatial-temporal spread process of coronavirus disease 2019 (COVID-19) is proposed, incorporating spatial interaction features into epidemiological models. This paper also estimates the number of confirmed patients and spatial-temporal spread process of COVID-19 in Wuhan from December 2019 to March 2020. The results show that the method proposed in this paper can effectively estimate the daily traffic analysis zones (TAZs) where new confirmed patients appear, completely covering the TAZs with the epidemic announcements. And the TAZs with the epidemic announcements account for 72.7% of the estimated TAZs. The cumulative number of estimated confirmed patients agrees very well with the total number of officially announced confirmed patients after February 18, 2020, with a gap of approximately 5.6%, indirectly verifying the rationality of the previous estimation. The method proposed in this paper can effectively estimate the spread of infectious diseases under fine-grained spaces. It also has scientific significance in understanding the influence mechanism of the crowd interaction under finegrained spaces on the spatial-temporal spread of infectious diseases, and enhancing the macroscopically spatial interpretability of epidemiological models macroscopic. [ABSTRACT FROM AUTHOR]

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 predic‐ tion analysis and the researches on epidemic situation prevention and control measures, remain at the statis‐ tical 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 isola‐ tion measures and the connectivity and isolation of adjacent spaces separately and designs the medical recep‐ tion and cure model under the discrete grid based on the spatial autocorrelation between the medical bed ad‐ mission capacity and the grid. Furthermore, the LSEIR (logistic‐susceptible‐exposed‐infected‐removed) epi‐ demic 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 spatio‐ temporal 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 re‐ duce the peak value of the infected population in the early stage of the epidemic, but has no significant im‐ pact 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 epidem ‐ ic situation from both quantitative and qualitative perspectives, which has high rationality and correctness. [ABSTRACT FROM AUTHOR]