论文标题

来自快速大流行的延迟动态模型的新近似值和政策含义

New approximations, and policy implications, from a delayed dynamic model of a fast pandemic

论文作者

Vyasarayani, C. P., Chatterjee, Anindya

论文摘要

我们研究了一种传染病的SEIQR(易感性接触性降低的被验证的)模型,其中有时间延迟的潜伏期和无症状阶段。对于没有人具有先前免疫力并且每个人在恢复后都具有免疫力的快速大流行,SEIQR模型将带有五个参数的两个非线性延迟微分方程(DDE)分解为两个非线性延迟微分方程(DDE)。通过缩放时间将一个参数设置为统一。首先检查完美的隔离和零自我恢复的子案例,带有两个免费参数。多个尺度的方法产生双曲线切线溶液。长波近似产生一阶普通微分方程(ODE)。使用不完美的隔离和非零自我恢复,长波近似是二阶ode。这三个近似值每个都捕获了从无限启动到最终饱和度的完整爆发。使用Galerkin投影获得的六个状态非删除的降低订单模型证明了DDES中的低维动力学。缩小订单模型的数值解决方案与DDE相匹配的一系列参数选择和初始条件。最后,稳定性分析和数字表明,在当前模型中,正确执行时变的社会距离如何将受影响的人的数量减少几乎一半。或者,更快的检测随后隔离隔离可能更有效。

We study an SEIQR (Susceptible-Exposed-Infectious-Quarantined-Recovered) model for an infectious disease, with time delays for latency and an asymptomatic phase. For fast pandemics where nobody has prior immunity and everyone has immunity after recovery, the SEIQR model decouples into two nonlinear delay differential equations (DDEs) with five parameters. One parameter is set to unity by scaling time. The subcase of perfect quarantining and zero self-recovery before quarantine, with two free parameters, is examined first. The method of multiple scales yields a hyperbolic tangent solution; and a long-wave approximation yields a first order ordinary differential equation (ODE). With imperfect quarantining and nonzero self-recovery, the long-wave approximation is a second order ODE. These three approximations each capture the full outbreak, from infinitesimal initiation to final saturation. Low-dimensional dynamics in the DDEs is demonstrated using a six state non-delayed reduced order model obtained by Galerkin projection. Numerical solutions from the reduced order model match the DDE over a range of parameter choices and initial conditions. Finally, stability analysis and numerics show how correctly executed time-varying social distancing, within the present model, can cut the number of affected people by almost half. Alternatively, faster detection followed by near-certain quarantining can potentially be even more effective.

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