Facultad de Ciencias Básicas, Universidad Católica del Maule, Talca, Chile.
Facultad de Ingeniería y Ciencias, Universidad Adolfo Ibáñez, Santiago, Chile.
PLoS One. 2022 Jun 16;17(6):e0269843. doi: 10.1371/journal.pone.0269843. eCollection 2022.
The classical SEIR model, being an autonomous system of differential equations, has important limitations when representing a pandemic situation. Particularly, the geometric unimodal shape of the epidemic curve is not what is generally observed. This work introduces the βSEIR model, which adds to the classical SEIR model a differential law to model the variation in the transmission rate. It considers two opposite thrives generally found in a population: first, reaction to disease presence that may be linked to mitigation strategies, which tends to decrease transmission, and second, the urge to return to normal conditions that pulls to restore the initial value of the transmission rate. Our results open a wide spectrum of dynamic variabilities in the curve of new infected, which are justified by reaction and restoration thrives that affect disease transmission over time. Some of these dynamics have been observed in the existing COVID-19 disease data. In particular and to further exemplify the potential of the model proposed in this article, we show its capability of capturing the evolution of the number of new confirmed cases of Chile and Italy for several months after epidemic onset, while incorporating a reaction to disease presence with decreasing adherence to mitigation strategies, as well as a seasonal effect on the restoration of the initial transmissibility conditions.
经典的 SEIR 模型是一个自治的微分方程系统，在表示大流行情况时存在重要的局限性。特别是，流行曲线的几何单峰形状并不是通常观察到的。这项工作引入了βSEIR 模型，该模型在经典 SEIR 模型中增加了一个微分定律，以模拟传播率的变化。它考虑了一般在人群中发现的两种相反的趋势：首先，对疾病存在的反应，可能与缓解策略有关，这往往会降低传播率；其次，恢复正常状态的冲动，促使恢复初始传播率。我们的结果在新感染曲线中开辟了广泛的动态可变性，这些动态可变性是由随时间推移影响疾病传播的反应和恢复趋势所证明的。其中一些动态已经在现有的 COVID-19 疾病数据中观察到。特别是，为了进一步例证本文提出的模型的潜力，我们展示了它在纳入对疾病存在的反应，减少对缓解策略的遵守，以及对初始传染性条件恢复的季节性影响后，对智利和意大利几个月后新确诊病例数量演变的捕捉能力。
