The 2019 coronavirus disease (COVID-19) cases that are being confirmed in Canada provide an opportunity to expand the epidemic model for the simulation of disease infection spread: Susceptible- Exposed-Infectious-Recovered (SEIR). This paper develops a SEIRCRT |ˈsəːkrɪt | model that integrates the Institute for Disease Modeling’s SEIR model and Critical Race Theory (CRT) to answer the question: What is in a SEIRCRT model? SEIRCRT provides a basic modeling structure from a CRT lens to simulate, predict and forecast COVID-19 cases, comorbidities affecting African Canadians, and deaths through predictive modeling. Knowledge of SEIRCRT is critical to characterize the severity of COVID-19 in this early stage. To this end, the purpose of this paper is to describe SEIRCRT’s model and summarize its key characteristics. SEIRCRT as a public health framework provides insight into the conclusions drawn about race and COVID-19, and expands our thinking about what health disparities mean for African Canadian communities.

加拿大已确认的2019年冠状病毒疾病（COVID-19）病例提供了一个扩展流行病模型以模拟疾病感染传播的机会：易感-暴露-传染-恢复（SEIR）。本文开发了一个SEIRCRT | ˈsəːkrɪt | 该模型将疾病建模研究所的SEIR模型和关键种族理论（CRT）集成在一起，以回答以下问题：SEIRCRT模型中有什么？SEIRCRT从CRT镜头提供了基本的建模结构，可以通过预测建模来模拟，预测和预测COVID-19病例，影响非裔加拿大人的合并症以及死亡。SEIRCRT的知识对于在此早期阶段表征COVID-19的严重性至关重要。为此，本文的目的是描述SEIRCRT模型并总结其关键特征。