Ramos A M, Ferrández M R, Vela-Pérez M, Kubik A B, Ivorra B
MOMAT Research Group, Interdisciplinary Mathematics Institute, Complutense University of Madrid, Spain.
Department of Computer Science, University of Almería, Spain.
Physica D. 2021 Jul;421:132839. doi: 10.1016/j.physd.2020.132839. Epub 2021 Jan 1.
Since the start of the COVID-19 pandemic in China many models have appeared in the literature, trying to simulate its dynamics. Focusing on modeling the biological and sociological mechanisms which influence the disease spread, the basic reference example is the SIR model. However, it is too simple to be able to model those mechanisms (including the three main types of control measures: social distancing, contact tracing and health system measures) to fit real data and to simulate possible future scenarios. A question, then, arises: how much and how do we need to complexify a SIR model? We develop a -SEIHQRD model, which may be the simplest one satisfying the mentioned requirements for arbitrary territories and can be simplified in particular cases. We show its very good performance in the Italian case and study different future scenarios.
自中国爆发新冠疫情以来，文献中出现了许多模型，试图模拟其动态变化。聚焦于对影响疾病传播的生物学和社会学机制进行建模，基本的参考范例是SIR模型。然而，它过于简单，无法对这些机制（包括三种主要的控制措施：社交距离、接触者追踪和卫生系统措施）进行建模以拟合实际数据并模拟未来可能的情况。于是，一个问题出现了：我们需要在多大程度上以及如何使SIR模型复杂化？我们开发了一个-SEIHQRD模型，它可能是满足任意地区上述要求的最简单模型，并且在特定情况下可以简化。我们展示了它在意大利案例中的良好表现，并研究了不同的未来情况。
