Enrique Amaro José, Dudouet Jérémie, Nicolás Orce José
Departamento de Física Atómica, Molecular y Nuclear and Instituto Carlos I de Física Teórica y Computacional, Universidad de Granada, Granada E-18071, Spain.
Univ Lyon, Univ Claude Bernard Lyon 1, CNRS/IN2P3, IP2I Lyon, UMR 5822, Villeurbanne F-69622, France.
Appl Math Model. 2021 Feb;90:995-1008. doi: 10.1016/j.apm.2020.10.019. Epub 2020 Oct 22.
Several analytical models have been developed in this work to describe the evolution of fatalities arising from coronavirus COVID-19 worldwide. The Death or 'D' model is a simplified version of the well-known SIR (susceptible-infected-recovered) compartment model, which allows for the transmission-dynamics equations to be solved analytically by assuming no recovery during the pandemic. By fitting to available data, the D-model provides a precise way to characterize the exponential and normal phases of the pandemic evolution, and it can be extended to describe additional spatial-time effects such as the release of lockdown measures. More accurate calculations using the extended SIR or ESIR model, which includes recovery, and more sophisticated Monte Carlo grid simulations - also developed in this work - predict similar trends and suggest a common pandemic evolution with universal parameters. The evolution of the COVID-19 pandemic in several countries shows the typical behavior in concord with our model trends, characterized by a rapid increase of death cases followed by a slow decline, typically asymmetric with respect to the pandemic peak. The fact that the D and ESIR models predict similar results - without and with recovery, respectively - indicates that COVID-19 is a highly contagious virus, but that most people become asymptomatic (D model) and eventually recover (ESIR model).
在这项工作中,已经开发了几种分析模型来描述全球范围内由冠状病毒COVID-19导致的死亡人数的演变。死亡或“D”模型是著名的SIR(易感-感染-康复) compartment模型的简化版本,它通过假设在疫情期间没有康复,使得传播动力学方程能够通过解析求解。通过拟合现有数据,D模型提供了一种精确的方法来表征疫情演变的指数阶段和正常阶段,并且可以扩展以描述诸如解除封锁措施等额外的时空效应。使用扩展的SIR或ESIR模型(包括康复)进行的更精确计算,以及同样在这项工作中开发出的更复杂的蒙特卡洛网格模拟,预测了相似的趋势,并表明存在具有通用参数的共同疫情演变。几个国家的COVID-19疫情演变显示出与我们模型趋势一致的典型行为,其特征是死亡病例迅速增加,随后缓慢下降,通常相对于疫情高峰不对称。D模型和ESIR模型分别在不考虑康复和考虑康复的情况下预测出相似结果,这一事实表明COVID-19是一种高传染性病毒,但大多数人会无症状(D模型)并最终康复(ESIR模型)。