论文标题
COVID-19的知识转移模型预测和非药物干预模拟
A knowledge transfer model for COVID-19 predicting and non-pharmaceutical intervention simulation
论文作者
论文摘要
自2019年12月以来，一种新型的冠状病毒（2019-NCOV）一直在中国爆发，这可能导致呼吸道疾病和严重的肺炎。依靠流行病状况量表预测疾病暴发的数学和经验模型受到了越来越多的关注。鉴于它在评估传染病量表中的成功应用，我们提出了一种易感未经诊断的被感染的（SUIR）模型，以提供有效的预测，预防和控制传染病。我们的模型是一种经过修改的易感感染的（SIR）模型，它注入未诊断的状态并提供训练前有效的繁殖数。我们的SUIR模型比传统的SIR模型更精确。此外，我们将对流行病的领域知识与估计有效的繁殖数相结合，这解决了传染病模型模型方法的初始易感人群。这些发现对Covid-19的流行趋势的预测具有影响，因为这些可能有助于估计流行病的增长。
Since December 2019, A novel coronavirus (2019-nCoV) has been breaking out in China, which can cause respiratory diseases and severe pneumonia. Mathematical and empirical models relying on the epidemic situation scale for forecasting disease outbreaks have received increasing attention. Given its successful application in the evaluation of infectious diseases scale, we propose a Susceptible-Undiagnosed-Infected-Removed (SUIR) model to offer the effective prediction, prevention, and control of infectious diseases. Our model is a modified susceptible-infected-recovered (SIR) model that injects undiagnosed state and offers pre-training effective reproduction number. Our SUIR model is more precise than the traditional SIR model. Moreover, we combine domain knowledge of the epidemic to estimate effective reproduction number, which addresses the initial susceptible population of the infectious disease model approach to the ground truth. These findings have implications for the forecasting of epidemic trends in COVID-19 as these could help the growth of estimating epidemic situation.
