新冠肺炎疫情期间美国保持社交距离的动态模型

Q2 Mathematics
S. Chitanvis
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引用次数: 2

摘要

摘要背景社交距离导致美国许多州的“曲线变平”。这是一项新颖、大规模的全球社会实验的一部分,该实验在没有疫苗或有效抗病毒药物的情况下,有助于缓解新冠肺炎大流行。因此,能够合理准确地预测住院人数是很重要的。方法我们在现象学的基础上提出了一个随机行走/广义扩散方程,该方程结合了社交距离的影响,以描述给定住院次数概率的时间演变。概率密度函数是住院人数的对数正态函数,这在描述住院人数非常高的流行病时很有用。研究结果我们利用这些见解和数据,使用蒙特卡罗方法对各州进行了预测。反向测试验证了我们的方法,在未来一周左右会产生良好的结果。在提交本文时,各州开始重新开放,我们的预测表明住院人数可能会增加。然而,到目前为止,我们预测的住院人数和感染人数的趋势显示出温和的增长。此外,我们还研究了纽约(意大利菌株)和加利福尼亚(武汉菌株)的再现性Ro。我们发现,即使这两种毒株的传播存在差异,保持社交距离也能够控制2019冠状病毒病的进展。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Dynamical model for social distancing in the U.S. during the COVID-19 epidemic
Abstract Background Social distancing has led to a “flattening of the curve” in many states across the U.S. This is part of a novel, massive, global social experiment which has served to mitigate the COVID-19 pandemic in the absence of a vaccine or effective anti-viral drugs. Hence it is important to be able to forecast hospitalizations reasonably accurately. Methods We propose on phenomenological grounds a random walk/generalized diffusion equation which incorporates the effect of social distancing to describe the temporal evolution of the probability of having a given number of hospitalizations. The probability density function is log-normal in the number of hospitalizations, which is useful in describing pandemics where the number of hospitalizations is very high. Findings We used this insight and data to make forecasts for states using Monte Carlo methods. Back testing validates our approach, which yields good results about a week into the future. States are beginning to reopen at the time of submission of this paper and our forecasts indicate possible precursors of increased hospitalizations. However, the trends we forecast for hospitalizations as well as infections thus far show moderate growth. Additionally we studied the reproducibility Ro in New York (Italian strain) and California (Wuhan strain). We find that even if there is a difference in the transmission of the two strains, social distancing has been able to control the progression of COVID 19.
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来源期刊
Computational and Mathematical Biophysics
Computational and Mathematical Biophysics Mathematics-Mathematical Physics
CiteScore
2.50
自引率
0.00%
发文量
8
审稿时长
30 weeks
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