COVID-19时间序列数据模型融合的替代参数优化
Surrogate Parameters Optimization for Data and Model Fusion of COVID-19 Time-series Data
摘要：
我们的研究重点是开发一个计算框架，以模拟COVID-19大流行的传播动态。我们研究了一个名为ADRIANA的系统的开发，以南非为例进行模拟研究。ADRIANA系统的设计将三个子模型相互连接，以建立一种计算技术，为有关封锁措施的政策提供建议，以减少COVID-19在南非的传播模式。此外，ADRIANA的输出可以被医疗管理部门用来预测治疗感染个体所需资源的高峰需求时间。ABM是适合我们的研究实验，但为了防止使用ABM为基础的框架，这项研究的计算限制，我们开发了一个SEIR房室模型，离散事件模拟器，和一个优化的代理模型，形成一个系统名为ADRIANA。我们还确保代理人的调查结果足够准确，以提供最佳解决方案。我们使用遗传算法（GA）的优化，估计最佳的超参数配置的代理。我们通过讨论ADRIANA系统提出的解决方案来结束这项研究，该系统与我们研究的主要目标相一致，即为政府的封锁政策和医院管理人员的资源管理提供最佳指导。
英文摘要：
Our research focuses on developing a computational framework to simulate the transmission dynamics of COVID-19 pandemic. We examine the development of a system named ADRIANA for the simulation using South Africa as a case study. The design of the ADRIANA system interconnects three sub-models to establish a computational technique to advise policy regarding lockdown measures to reduce the transmission pattern of COVID-19 in South Africa. Additionally, the output of the ADRIANA can be used by healthcare administration to predict peak demand time for resources needed to treat infected individuals. ABM is suited for our research experiment, but to prevent the computational constraints of using ABM-based framework for this research, we develop an SEIR compartmental model, a discrete event simulator, and an optimized surrogate model to form a system named ADRIANA. We also ensure that the surrogate's findings are accurate enough to provide optimal solutions. We use the Genetic Algorithm (GA) for the optimization by estimating the optimal hyperparameter configuration for the surrogate. We concluded this study by discussing the solutions presented by the ADRIANA system, which aligns with the primary goal of our study to present an optimal guide to lockdown policy by the government and resource management by the hospital administrators.
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。
