Particle modeling of the spreading of coronavirus disease (COVID-19)
Particle modeling of the spreading of coronavirus disease (COVID-19)
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DOI：
10.1063/5.0020565
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发表时间：
2020-08-01
影响因子：
4.6
通讯作者：
Pederiva, Francesco
中科院分区：
文献类型：
--
作者：
De-Leon, Hilla;Pederiva, Francesco
截至 2020 年 7 月底，COVID-19 大流行已感染超过 17 x 10(6) 人，并已传播到全球几乎所有国家。对此，世界许多国家采取了不同的方法来降低感染率，例如病例隔离、关闭学校和大学、禁止公共活动以及强制保持社交距离，包括地方和国家封锁。在我们的工作中，我们使用基于蒙特卡罗的算法，利用最新的流行病数据来预测不同人口密度的病毒感染率。我们使用三种不同的锁定模型和八种不同的约束组合来测试冠状病毒的传播，这使我们能够检查每种模型和约束的效率。在本文中，我们测试了三种不同的无限制/锁定模式的时间循环模式。该模型的主要预测是，每个时间周期至少包含十天锁定的无限制/锁定的循环时间表可以帮助控制病毒感染。特别是，这种模式在保持社交距离和完全隔离有症状患者的情况下降低了感染率。
By the end of July 2020, the COVID-19 pandemic had infected more than 17 x 10(6) people and had spread to almost all countries worldwide. In response, many countries all over the world have used different methods to reduce the infection rate, such as case isolation, closure of schools and universities, banning public events, and forcing social distancing, including local and national lockdowns. In our work, we use a Monte Carlo based algorithm to predict the virus infection rate for different population densities using the most recent epidemic data. We test the spread of the coronavirus using three different lockdown models and eight various combinations of constraints, which allow us to examine the efficiency of each model and constraint. In this paper, we have tested three different time-cyclic patterns of no-restriction/lockdown patterns. This model's main prediction is that a cyclic schedule of no-restrictions/lockdowns that contains at least ten days of lockdown for each time cycle can help control the virus infection. In particular, this model reduces the infection rate when accompanied by social distancing and complete isolation of symptomatic patients.