年龄相关随机传染病模型的倾向矩阵方法
Propensity matrix method for age dependent stochastic infectious disease models
摘要：
数学建模是有效控制新发现传染病（如COVID-19）的关键因素之一。我们的知识的参数和感染的过程是非常有限的，在流行病的开始，因此计算机实现的模型必须是快速和灵活的。本文讨论的倾向矩阵-更新图方法是一种有效实现年龄结构随机流行病模型的简便方法。我们为预测工作实现的代码库也包含在附加的GitHub存储库中。
英文摘要：
Mathematical modeling is one of the key factors of the effective control of newly found infectious diseases, such as COVID-19. Our knowledge about the parameters and the course of the infection is highly limited in the beginning of the epidemic, hence computer implementation of the models have to be quick and flexible. The propensity matrix - update graph method we discuss in this paper serves as a convenient approach to efficiently implement age structured stochastic epidemic models. The code base we implemented for our forecasting work is also included in the attached GitHub repository.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
