论文标题
COVID-19：开发强大的数学模型和仿真软件包，并考虑到人口老龄化以及控制动作和重新敏感的时间延迟
COVID-19: Development of a Robust Mathematical Model and Simulation Package with Consideration for Ageing Population and Time Delay for Control Action and Resusceptibility
论文作者
论文摘要
当前的全球卫生紧急情况由大流行Covid-19引起的是这一代人类面临的最大挑战之一。计算模拟在预测当前大流行的发展方面发挥了重要作用。这种模拟可以早期对大流行的未来预测提出迹象，并有助于估计与SARS-COV-2病毒战斗中控制作用的效率。 SEIR模型是一种在感染性病毒疾病的计算模拟中使用的一种知名方法，它已被广泛用于模拟其他流行病，例如埃博拉病毒，SARS，MERS和流感A。本文在死亡率和复苏的形式中，在该模型中延长了预期的预测，该模型具有额外的退出条件，该模型具有额外的退出条件，该模型将延长预期，以延长预期，以扩展预期的预测，该模型延长了当前的预测。死亡，恢复和恢复，有可能重新敏感。该模型还考虑了特定信息，例如人口的老化因素，由于控制行动措施而导致大流行的时间延迟以及对时间免疫反应的重新敏感性。由于Covid-19表现出的临床症状发生了巨大变化，该提出的模型旨在更好地反思当前情况和病例数据，因此可以更好地理解疾病的传播以及采取的控制动作的效率。分别基于韩国和北爱尔兰的现实数据进行验证和预测研究验证该模型。
The current global health emergency triggered by the pandemic COVID-19 is one of the greatest challenges mankind face in this generation. Computational simulations have played an important role to predict the development of the current pandemic. Such simulations enable early indications on the future projections of the pandemic and is useful to estimate the efficiency of control action in the battle against the SARS-CoV-2 virus. The SEIR model is a well-known method used in computational simulations of infectious viral diseases and it has been widely used to model other epidemics such as Ebola, SARS, MERS, and influenza A. This paper presents a modified SEIRS model with additional exit conditions in the form of death rates and resusceptibility, where we can tune the exit conditions in the model to extend prediction on the current projections of the pandemic into three possible outcomes; death, recovery, and recovery with a possibility of resusceptibility. The model also considers specific information such as ageing factor of the population, time delay on the development of the pandemic due to control action measures, as well as resusceptibility with temporal immune response. Owing to huge variations in clinical symptoms exhibited by COVID-19, the proposed model aims to reflect better on the current scenario and case data reported, such that the spread of the disease and the efficiency of the control action taken can be better understood. The model is verified using two case studies for verification and prediction studies, based on the real-world data in South Korea and Northern Ireland, respectively.
