Integration of analytical COVID-19 models for remote execution and advanced visualization for improved decision making

近年来，基于传统的易感-传染病-恢复期(SIR)方程的仿真模型被用于预测大流行的动力学。这些模型目前正被用来预测疾病在全球大多数国家的传播。对新冠肺炎的研究是基于传染病动力学的理论方法，而传染病动力学的理论方法是基于展示疾病传播方式的数学模型。最初的模型自1927年以来一直使用，它将参与疾病传播的个人分为易受病毒影响的人、传染性人和最终康复的人。SIR模型被多次扩展和修改，以研究其他疾病的进展情况和先进的新技术。加拿大国防研究和发展中心(DRDC，国防部下属机构)的作战研究和分析中心(CORA)已经开发了几个基于模型的模型；然而，这些模型不足以回答DND决策者提出的所有问题。卡尔顿大学的高级实时模拟实验室正在与CORA合作，研究如何调整现有的分析模型，使其能够与先进的地理信息系统(GIS)混合，并在获得新的研究成果时易于修改和调整，并可用于轻松远程执行。

In recent years, simulation models based on the traditional Susceptible-Infectious-Recovered (SIR) equations have been used to predict the pandemic dynamics. These models are being used at present to predict the spread of the disease in most countries worldwide. Studies of COVID-19 are based on theoretical methods for infectious disease dynamics, which are based on mathematical models that show how the disease spreads. The original model has been used since 1927, and it classifies the individuals involved in the transmission of the disease into those that are Susceptible to the virus, those who are Infectious and finally those who Recovered. The SIR model was extended and adapted numerous times to study the progression of other diseases and advanced new techniques. The Centre for Operational Research and Analysis (CORA) at Defence Research and Development Canada (DRDC, an Agency of the Department of National Defence, DND) has developed several models based; however, these models are not enough to answer all the questions posed by the DND decision-makers. The Advanced Real-Time Simulation laboratory at Carleton University is partnering with CORA to collaborate to study how to adapt the analytical models available so they could be mixed with advanced Geographical Information Systems (GIS), and to be easily modified and adapted when new research results become available, and to be available for easy remote execution.