论文标题
在基于规则的流行病学模型框架中，用于仿真的广义吉莱斯皮算法
Generalised Gillespie Algorithms for Simulations in a Rule-Based Epidemiological Model Framework
论文作者
论文摘要
基于规则的模型已成功地代表了COVID-19大流行的不同方面，包括年龄，测试，住院，锁定，免疫，感染性，行为，行动，流动性和个人疫苗接种。这些基于规则的方法是由化学反应规则激励的，这些化学反应规则传统上是通过分子动力学中提出的标准吉莱斯皮算法在数值上求解的。当将反应系统的方法类型应用于流行病学时，由于问题的时间依赖性，需要吉莱斯皮算法的概括。在本文中，我们介绍了标准Gillespie算法的不同概括，该算法解决了离散的子类型（例如，结合了人口的年龄结构），时间 - 差异更新（例如，将锁定率的每日施加率更改）和确定性延迟（例如，给定等待时间更改，直到从隔离中释放出来）。这些算法在COVID-19大流行和数值结果的背景下与相关示例相辅相成。
Rule-based models have been successfully used to represent different aspects of the COVID-19 pandemic, including age, testing, hospitalisation, lockdowns, immunity, infectivity, behaviour, mobility and vaccination of individuals. These rule-based approaches are motivated by chemical reaction rules which are traditionally solved numerically with the standard Gillespie algorithm proposed in the context of molecular dynamics. When applying reaction system type of approaches to epidemiology, generalisations of the Gillespie algorithm are required due to the time-dependency of the problems. In this article, we present different generalisations of the standard Gillespie algorithm which address discrete subtypes (e.g., incorporating the age structure of the population), time-discrete updates (e.g., incorporating daily imposed change of rates for lockdowns) and deterministic delays (e.g., given waiting time until a specific change in types such as release from isolation occurs). These algorithms are complemented by relevant examples in the context of the COVID-19 pandemic and numerical results.
