TEST SEIRCRT |ˈsəːkrɪt |: For the Health of Our Communities

Abstract

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.

Keywords

SEIR model critical race theory SEIRCRT model predictive modeling health disparities

Introduction

On January 25, 2020, the first cases of the 2019 coronavirus disease (COVID-19) emerged in Canada that are directly linked to pneumonia cases in Wuhan City, Hubei Province, China.¹ The World Health Organization (WHO) confirmed that the causative agent of this pneumonia is a severe acute respiratory syndrome coronavirus 2 (SARS-COV-2) infection and its resulting condition coronavirus disease 2019 (COVID-19) is a public health emergency of international concern. A local market visited by patient zero and many individuals indicated that a common-source zoonotic exposure may have been the main mode of transmission (Li et al., 2020). However, after the local market was closed, and the Public Health Agency “activated the Emergency Operation Centre to support Canada’s response to COVID-19, and screening requirements related to COVID-19 for travelers returning from China to major airports in Montréal, Toronto and Vancouver,²” the number of cases continued to increase across Canada (see Figure 2). As of May 26, 2020, a total of 1, 502, 990 individuals tested, 86,647 cases, including 6, 639 deaths were confirmed in Canada.³ It is now speculated that constant human-to human transmission and community transmission aided in the spread of COVID-19 in this country (Government of Canada, 2020). Similar cases of COVID-19 infections were also reported in Europe, North America, the Caribbean and around the world.

Early assessment of COVID-19 in Canada and the ethnicity/race of individuals can help to qualitatively and quantitatively understand and contain the its spread. One important epidemic measure of Susceptibility, Exposure, Infection and Recovery (SEIR) provides a basic disease model to explain the transmission of a virus through a population as explicated by the Institute for Disease Modeling (IDM). One critical perspective, Critical Race Theory (CRT) attempts to move beyond merely documenting health inequities toward understanding and challenging the power hierarchies that ungird them. Integrated as SEIRCRT |ˈsəːkrɪt |, the model helps public health researchers to carry out health equity research with adherence to CRT principles.

Using the The Institute for Disease Modeling (IDM, 2019) mathematical modeling of SEIR and CRT, this article develops a SEIRCRT |ˈsəːkrɪt | model to simulate COVID-19’s spread of the disease through a population, predict and forecast COVID-19 cases and co-morbidities affecting African Canadians through predictive modeling. The African Canadian population is what Mensah (2014, p. 10) defines as a: “highly heterogeneous in their ethnicity, immigration status, generation, place of origin and other factors with immigrants coming from the continental Africa, others from the Caribbean, United States, Europe and elsewhere, and still others tracing their nativity to Canada as indigenous Black Canadians.” So, we need data and trends on numbers from this group to learn more about how they are affected by COVID-19.⁴ The diagram tells us that to reduce the impact of the disease, we need to lower the contact ratio as much as possible, which is exactly what the current quarantine, isolation, social/physical distancing, hygiene, cleaning, wearing masks or face coverings measures are designed to accomplish.⁵ To this end, the purpose of this paper is to visualize the SEIRCRT model, process and summarize its key characteristics.

African Canadians and the Herd Immunity Myth

African Canadian history is rooted in anti-Black racism and there are numerous efforts made to resist this history of oppression. The African Canadian population is disproportionately affected by poverty, limited health care access, discrimination, gun violence, unemployment among the poor, racial profiling, youth incarceration, school disengagement, social health and chronic illnesses (Chiu et al., 2010; Etowa et al., 2007; Kisely et al., 2008; Kobayashi et al., 2008; Liu et al. 2010). Many African Canadians are well versed in critiquing this marginalized status and possess first-hand accounts of the devastating impact of inequity. These social conditions and experiences provide a false premise to accept the myth of herd immunity to COVID-19; the myth of the potential for immunity against a global crisis.

During the COVID-19 pandemic in China, a young Cameroonian student who resided in China was infected and became the first person of African descent to contract the virus. He received treatment for the disease in China, and within a few weeks, he recovered from the condition (Vincent, 2020). Subsequently, a number of unsubstantiated reports emerged declaring that the genome of Blacks or even the presence of melanin rendered Black African descended populations immune to the virus. The misinformation spread via the media and in social settings even as public Black public figures reported they contracted the disease (Glanton, 2020). More importantly, the data shows that there is an unmistakable lack of reported and accessible data on the racial and ethnic composition of those infected with COVID-19. The scarcity of this information generates a more substantial concern in which insufficiently identifying the affected, disaggregating data, may ultimately result in historically marginalizing racial/ethnic groups carrying the greatest burden of the disease and disproportionately bearing the social impact.

The concern is that African Canadians tend to live in close communities and an infectious agent has the ability to spread to this group due to the proximity. The culmination of African Canadians maintaining greater disease burden, higher poverty rates and limited health care access, higher rates of jobs in service industries where they are less able to work from home with a subsequent increased exposure risk and the under folding spread of the virus in cities with larger African Canadian populations is a forewarning that if disregarded may constitute imprudent action.

The COVID-19 Canada Map

Figure 1 shows a map of COVID-19 cases reported in Canada by province and territories, (n = 86, 939), as of May 28, 2020. The total n excludes 13 repatriated travelers. The map includes information on “demographics, symptoms and outcomes currently available for the reported cases” (Canada.ca). Currently, the data shows a decrease in the number of new infections. It is interesting to note that provinces of Ontario and Quebec reported the majority of cases (86%) and deaths (94%). For more than 2 weeks, “Newfoundland, Prince Edward Island, Yukon and Northwest Territories have not reported new cases. Nunvaut has not reported any cases” (Canada.ca).

Figure 1.

Map of COVID-19 cases reported in Canada by province and territories.

Figure 2.

Historical timeline of COVID-19 in Canada. The timeline shows Canada’s response to manage COVID-19 and its spread into a pandemic between January 2020 and May 2020.

Black Canadians and COVID-19

Canada has limited studies on Black Canadians and COVID-19 that emphasis the socioeconomic inequalities affecting them. This population has a long history in this country dating back to the seventeenth century and consists of numerous ethnic, religious, and linguistic communities which comprise 1,198, 540 people or 3% of the country’s population. Almost 52.4% of the Black Canadian population lives in Ontario and 27% in Quebec. A paucity of literature exists on Black Canadians and health disparities. The literature indicates that the burden of disease may be greater for Black Canadians compared to their White counterparts and that “Black Canadians face a number of barriers to achieving good health including: poverty, difficulty accessing health care, discrimination and poor health behaviours” (Chiu et al., 2010; Etowa et al., 2007; Kisely et al., 2008; Kobayashi et al., 2008; Liu et al. 2010).

During COVID-19 pandemic, the SEIRCRT model includes known cardiovascular risk factors, which puts underrepresented racialized communities who live in at-risk communities at greater risk for the disease, not just cardiovascular diseases but now for COVID-19 mortality. How are we to understanding the disease risks of importation and exportation in Canada to include race factors? How can we use a mathematical model informed by Critical Race Theory (CRT) to visualize the spread of the disease in our communities and its effect on Black Canadians? This is the focus of the next section.

SEIRCRT |ˈsəːkrɪt |: BASIC COVID-19 Modeling Structure and Process

SEIRCRT |ˈsəːkrɪt | is a basic disease model for the simulation of infectious disease spread of COVID-19 through a population based on predictive modeling as visualized in Figure 3. SEIRCRT displays the design that incorporates the IDM’s (2019) mathematical modeling and CRT to to visualize what is happening when a racialized population becomes infected.

Figure 3.

SEIRCRT |ˈsəːkrɪt |: BASIC COVID-19 modeling structure and process.

The key shows that the diagram incorporates epidemic and mathematical schematics in which shapes and arrows indicate four hypothesized causal associations, SEIRCRT’s shapes and arrows indicate the order in which to proceed during the virus transmission, the main areas of focus and the CRT principles to draw on, letters, numbers (^1,2,3,4), asterisk (*) and plus symbol (+). Although SEIRCRT generally moves sequentially from birth to death as indicated by the arrows and shapes, some movement in the opposite direction or skipping of sequences also occurs as indicated by the curved arrows. To work with a SEIRCRT focus means to devote one’s energies to addressing the conditions as illustrated below:

*ethnicity/race, age, immune status, hypertension, obesity, diabetes, cardiovascular disease, other comorbidities, other conditions, class, immigration status, local, provincial, national, territorial and global level, community, geographical region, time period^6–10

+saliva and/or blood test, level of social/ physical distancing, reinfections, patient zero, herd immunity, data revisions, case and contact management

S – susceptibility

E – exposed, infected, asymptomatic

A – infected, asymptomatic

I – infected, mild/severe symptoms

H – hospitalized

R – recovery

¹Race as a social construct

²Racialization

³Racism

⁴Race consciousness

V – vaccinations

When developing the SEIRCRT model, the ethnicity/race compartment is a new section added to the model to tell us what is happening to Black Canadians. Researchers can collect data on the Black Canadian population, in terms of ethnicity/race, age, immune status, because we know that this disease affects individuals differently and affects their ability to deal effectively with the disease, depending on other chronic diseases (hypertension, obesity, diabetes, cardiovascular disease, other comorbidities and other conditions.)

In addition, the SEIRCRT model assumes that given all of the ethnicities, gender, class, age groups and all of the conditions, some individuals are going to practice social distancing measures. SEIRCRT divides all of these sub-compartments into many more sub-compartments to look at how effective social distancing is to show the impact of the virus on our communities across the country at the municipal, provincial, territorial, national level and around the world.

The model takes into consideration the basic reproduction number referred to as R0, R affective or RT. According to Heffernan (2020), “the reproduction number is defined as the number of secondary infections produced by an initial infective when introduced into a total susceptible population.” The R0 is essentially what researchers, health agencies, government, and others are tracking. How many new infected people, is one infected person making right now at this current point in time during the pandemic? We want to get the reproduction number less than one so that the disease will die out. It is important to note that there are many models out there that can be used to determine what the reproduction number is. But the health agencies, government and others take all of this information into consideration to say when it is okay to relax the measures and conditions put in place.

Introducing New Compartments: Critical Race Theory in SEIRCRT

In Figure 3, SEIRCRT draws on CRT (Bonilla-Silva, 2006; Crenshaw et al., 1995). What CRT offers SEIRCRT is a model structure for conducting research that remains attentive to issues of both racial equity and methodologic rigor. As praxis and methodology, it combines theory, experiential knowledge, science and action to actively counter inequities. SEIRCRT may be used either alone as a broad model structure or in conjunction with other theories or methods to inform research on the causes of health disparities.

The SEIRCRT model implements CRT as a critical health framework so that the researcher establishes a personally race conscious orientation to a proposed endeavor, clarifying how and why a race conscious orientation is taken. To understand the causes of racial health inequities as applied to COVID-19 requires a solid understanding of the salience of racialization in Canadian society, one’s life and therefore race consciousness frames the entire process of Figure 3. Four key concepts are important to the model.

Race as a social construct “is a process of endowing a group or concept with a delineation, name or reality” (Delgado & Stefancic, 2001, p. 155). The statement, “race is a social construct” has both oppositional and affirmative connotations. Its oppositional connotation, the one typically invoked by health equity researchers, challenges the notion that health disparities result from phenotype differences that ignore behavioral changes, institutional changes or social arrangements. The affirmative connotation explains that race is a trait that orders us in hierarchical terms as members of inferior and superior races. To identify specific pathways linking racism and health requires explicit focus on racism.

Racialization describes social stratification in which socially constructed racial categories are the basis for ordering society. The primacy of racialization has two connotations within SEIRCRT. It connotes racialization’s foundational contribution to inequities. It also names critical race scholarship’s central focus: understanding how racialization influences: (1) observed outcomes, (2) knowledge production, and (3) the field’s impact on the broader society. Racialization is not the only cause of disparities. For critical race theorists, however, it is the point of scholarly departure in defining Canadian social problems. This principle is tied to contemporary patterns of racial relations.

The ordinariness of racism challenges the erroneous but widely held assumption that exposures to racism are perceptible because they are infrequent, intentional occurrences that stand out against as otherwise racism-free, societal context (Delgado & Stefancic, 2001). By definition, racialization is systemic because it permeates all facets of society. The SEIRCRT model argues that one way to improve understanding of the ordinariness of racism is through the concept of voice.

Race Consciousness is a lens through which all aspects of SEIRCRT research occurs. Race consciousness connotes explicit attention to racial dynamics in the social world and in one’s personal world (clarifying one’s relationship to a racial project). It is the foundation of the SEIRCRT because it is difficult to investigate racism’s contribution to health inequities without first acknowledging and understanding racialization. As Derek Bell, the father of CRT put it, “The racial philosophy we must seek is a hard-eyed view of racism as it is and out subordinate role in it” (Bell, 1991, pp. 89–90).

SEIR in SEIRCRT

The SEIRCRT model is based on SIR, but adds the Exposed compartment as a variable. SEIR refers to Susceptible-Exposed-Infectious-Removed or Recovered, respectively (Heffernan, 2020). Individuals within the Canadian population are each assigned to one of the following disease states: Susceptible (S) refers to individuals who can catch the infection and may become hosts if exposed. Exposed (E) are individuals who are already infected but are asymptomatic. Infectious (I) are individuals who are showing signs of infection and transmit the virus. Removed or recovered (R) are individuals who are previously infected, but are not longer infectious and already immune to the virus (Hazma et al., 2020, p. 7). The Susceptible (S), Exposed (E), Infectious (I), Removed or recovered (R) compartments help to segment not yet infected and disease free, individuals that are experiencing incubation duration, the confirmed (isolated) cases, recovered individuals, respectively. The SEIRCRT diagram in Figure 3 shows how individuals move through each compartment in the model.

Compartments as State Transitions

The SEIRCRT diagram takes many CRT^*+1,2,3,4 principles factors into consideration when researchers are modeling the coronavirus. In this case, SEIRCRT shows a Black Canadian population that we can use to visualize what is happening with an infectious disease outbreak.

The diagrammatic representation of the virus progress in individuals show that the population is Susceptible (S) to the virus. When individuals become Exposed (E) to a pathogen the individual is not infectious right away. So, individuals may have some time in the Exposed compartment. With COVID-19, there are some individuals who are asymptomatic. So, the model differentiates between those people who do not show symptoms and those who do. The diagram differentiates between those individuals who need to stay home and those who need to go to the hospital, those who have mild versus severe infections. At the same time, people who are mildly infected, asymptomatic, but their immune system may not be as strong as someone who has recovered from a severe infection. So, their immunity may decay quickly over time, and that means some people will become susceptible to the disease again. So, SEIRCRT consider new factors as the virus progresses in individuals.

The Exposed (E) individuals may become asymptomatically infected, rather than showing symptoms. These individuals are moved into the A^*+1,2,3,4 compartment. The individuals in our E^*+1,2,3,4 compartment and I^*+1,2,3,4 compartment, are starting to show symptoms that are mild; they may go to the hospital. But individuals may wait a while and they may start to show severe symptoms and they may proceed to the hospital. Some individuals are going to recover. The SEIRCRT model assumes that all people from the A^*+1,2,3,4 compartment are recovering in the R^*+1,2,3,4 compartment. Some individuals may recover from the hospitalized (H^*+1,2,3,4) compartment and go to recovered (R^*+1,2,3,4).

SEIRCRT anticipates that individuals who are mildly affected or asymptomatically affected may have an immunity that is not long lasting. The model can add these other compartments as W^*+1,2,3,4for waning immunity. Individuals can move from the recovered compartment (R^*+1,2,3,4) of where they are assumed to be recovered from the disease and considered to be waning in the waning compartment. Then, if individuals are exposed in W^*+1,2,3,4, the individuals can move to E^*+1,2,3,4 where you have been re-infected. The individuals have a higher probability of moving to the A^*+1,2,3,4 compartment again because they have some immunity which will decrease their symptoms.

Vaccines are in clinical trials right now. The assumption is that if vaccines are successful in being developed and brought out to the population, if infection induced immunity can wane over time, then vaccine induced immunity can wane over time as well. SEIRCRT includes this vaccinated compartment V^*+1,2,3,4 and epidemiologists have to figure out how often individuals need to be vaccinated to give them maximum level of immunity to lessen the risk of maximum or severe infection in the future. The assumption is that even though the pathogen is not evolving very quickly because the vaccine might not be very good in giving a sustained level of immunity; individuals may have to get vaccinated on a yearly basis, very similar to the flu vaccine. But coronavirus has not been evolving very quickly, so SEIRCRT assumes that the initial vaccine for the first few years will be the same unless scientists see some changes happening in COVID-19’s evolution.

For every scenario, SEIRCRT examines the number of daily presentations to the health care sector, whether they are severely infected, and they present to the health care sector early or late and we look to see if they are mildly affected. SEIRCRT examines out-patient presentations and the demand on all of our doctor offices and our emergency departments, and some of the provincial and territorial assessment clinics. What the SEIRCRT model is doing is trying to determine the cap or the maximum number of consultations that can happen at the provincial, territorial and national levels for all of the health care sectors. SEIRCRT can figure out how the health care workers are working in terms of shift length and whether they are working in an emergency department (hospital^*+1,2,3,4) or an assessment clinic and how we can move workers around from one place to another. After the patient sees a doctor in one of these sites, the doctor determines whether the patient needs a hospital bed. In the SEIRCRT model, individuals populate a bed in the ICU^*+1,2,3,4; or if beds are unavailable in ICU, then they are moved into the ward (W^*+1,2,3,4) based on the level of symptoms they have. The individuals will populate the beds for a certain period of time because the SEIRCRT can determine on average how long someone stays in one of these beds based on their level of symptoms that they have.

Parameters within this model are:

$″ β,$ controls the rate of spread, which represents the probability of transmitting disease between a susceptible and an infectious individual” (Hamzah et al., 2020, p. 11).

“Incubation rate $σ,$ is the rate of latent individuals becoming infectious. Given the known average duration of incubation Y, $σ = \frac{1}{Y}$ ” (Hamzah et al., 2020, p. 11).

“Recovery rate $γ = \frac{1}{D},$ is determined by the average duration of recovery D, of infection. After this period, they enter the removed phase. The average duration of infection is calculated as the average serial interval minus the average incubation duration” (Hamzah et al., 2020, p. 11).

IDM (2019) describes the virus transmission by using the following nonlinear ordinary differential equation as shown in the equation (1) to (4) below:

\frac{d S}{d t} = - \frac{β S I}{N}

(1)

\frac{d E}{d t} = \frac{β S I}{N} - σ E

(2)

\frac{d I}{d t} = σ E - γ I

(3)

\frac{d R}{d t} = γ I

(4)

This is a basic SEIR mathematical disease model.

Conclusion

This article presented a SEIRCRT model of disease transmission to visualize COVID-19. Yet, SEIRCRT is not a crystal ball trying to predict the future, rather the range of possibilities given the facts, the appropriate observations and updates to data as they emerge. COVID-19 is still an unclear infectious disease with some unclear or unknown properties, which means accurate SEIRCRT prediction can only be obtained once the outbreak has been successful contained and the data on racialized community’s is collected. The SEIRCRT model is a step in that direction to help in planning in advance to reduce risk. Further studies are needed to be done to help explain the effects of COVID-19 on Black Canadians communities to help contain the outbreak as soon as possible.

Footnotes

Acknowledgements

I want to thank my anonymous reviewers for their comments and suggestions offered on this article. I want to thank my sister Jacqueline Mills and my nephew Elliott Mills for supporting my research.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Jennifer Mills

Notes

Author Biography

Jennifer Mills completed a PhD in Sociology at York University, Toronto, Canada. She is currently a Sociologist and Researcher at the Institute for Social Research, York University. Her research focuses on qualitative research, critical research methodologies, Black feminism and Black Canadians.

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