论文标题
在与纽约和纽约州拟合时符合时间相关的SIR模型上
On a coupled time-dependent SIR models fitting with New York and New-Jersey states COVID-19 data
论文作者
论文摘要
本文介绍了2020年3月在纽约州（纽约）州的COVID-19数据中的简单易感感染的回收（SIR）模型。该模型是经典的先生，但非自主。随着时间的流逝，调整了易感人的感染率，以适应可用的数据。死亡率也进行了第二调整。我们的拟合是在假设由于限制测试数量的假设下进行的，因此，很大一部分受感染的人群尚未得到阳性测试。在最后一部分中，我们扩展了模型，以考虑新泽西州（NJ）和纽约州之间的每日通量，并适合两种状态的数据。我们的简单模型符合可用数据，并说明了该疾病的典型动态：指数增加，顶点和减少。该模型突出显示了整个时期的传输速率下降，这给出了有关锁定策略如何减少大流行病的定量说明。与纽约州和新泽西州状态的耦合模型在纽约波浪之后显示了NJ中的波浪，这说明了从一个有吸引力的热点到邻居的传播机制。 }
This article describes a simple Susceptible Infected Recovered (SIR) model fitting with COVID-19 data for the month of march 2020 in New York (NY) state. The model is a classical SIR, but is non-autonomous; the rate of susceptible people becoming infected is adjusted over time in order to fit the available data. The death rate is also secondarily adjusted. Our fitting is made under the assumption that due to limiting number of tests, a large part of the infected population has not been tested positive. In the last part, we extend the model to take into account the daily fluxes between New Jersey (NJ) and NY states and fit the data for both states. Our simple model fits the available data, and illustrates typical dynamics of the disease: exponential increase, apex and decrease. The model highlights a decrease in the transmission rate over the period which gives a quantitative illustration about how lockdown policies reduce the spread of the pandemic. The coupled model with NY and NJ states shows a wave in NJ following the NY wave, illustrating the mechanism of spread from one attractive hot spot to its neighbor. }
