Coronavirus: What the numbers really mean for Toronto

Inadequate screening and incomplete reporting mean we will be forced to continue to build contingency plans on quicksand

It has been a disorienting month as Canadians have been grimly mesmerized by daily COVID-19 case reports and physician testimonials from around the world on the spread of the virus. 

The narratives and data emerging from each country and even each city differ dramatically. Each place is unique in terms of governance, population structure, public health and clinical infrastructure and of course culture, history and geography. Good local data can drive rational effective local responses, both at the population level and among health care providers.

So, what do we know about our hometown, Toronto?

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Toronto data is released daily by the Medical Officer of Health. This data is based primarily on cases identified at screening units throughout the city. Our statistical understanding of the situation on the ground is limited by testing criteria and has changed to become more restrictive over the course of the month.

At the beginning of March, the City was mainly testing returning travellers with symptoms of COVID-19. By mid-month, testing was further restricted to people with symptoms and a high risk of transmitting the virus to others (for example, people living in shelters or health care workers). At no point has Toronto opted to test widely to estimate just how many of us are infected without any symptoms.

Critically, the numbers reported lag behind the number of actual positive cases because of delays in reporting of results. These delays can be as long as five days.

The closure of schools and the implementation of modest business closures and social distancing began mid-month. The “flattening” effects of this will be felt now and in the next few weeks, but this will likely be offset by the effects of returning travellers after March break who may take up to 12 days to become symptomatic.

Finally, a fraction of the increase in cases over time simply reflects an increased amount of screening, with a greater likelihood of detecting cases. 

Can we say anything about transmission dynamics in Toronto specifically?

For the majority of Toronto cases, key descriptors remain unreported. Based on the limited data available, the infection rate among younger cohorts in Toronto may be higher in comparison to the rest of the country, also reflecting the demographics of our city. The ‘not reported’ fraction is much smaller elsewhere in the country, suggesting Toronto public health is underresourced for the task at hand.

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How does the pandemic in Toronto compare to other cities?

All cities compared here had a very low number of cases reported at the beginning of March. While Toronto reported its first case in a returning traveller in late January, Madrid had reported 5 cases on February 28 and NYC reported 1 case by March 1. 

Thirty days later, the scale of infection differs by an order of magnitude between Toronto and NYC and Madrid. In part, this is a reflection of population size, structure, density and behaviour. In Toronto, public health measures may already be mitigating transmission in comparison to other cities.

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The curve, for example, is steeper for Montreal and more closely mirrors the NYC curve. 

Testing rates are similar for Montreal and Toronto. Hence testing rates do not explain the difference in the number of cases and the rate of increase in cases between the two cities.

The COVID 19 pandemic has already developed differently in these four cities over the course of one month, emphasizing the importance of precise local data for precise local modelling and planning.

While we need to bear in mind the formidable challenges faced by health care providers in Spain, Italy and NYC, we need to create local responses firmly grounded in local data.

Two months after Toronto’s first case, population data on rates of asymptomatic and symptomatic infection, and recovery rates (and hence immunity) cannot be determined from available data.

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Inadequate screening and incomplete reporting mean we cannot create accurate local prediction models. We are left instead with provincial models with their very wide margin of uncertainty with respect to ICU demand, deaths, and the duration of the pandemic.  

Months before this pandemic began the province resolved to slash already precarious public health funding. It is not surprising that they are not funding better, faster epidemiological data collection. 

At this moment, inpatient wards in Toronto are being cleared, and clinics and surgeries cancelled to accommodate an anticipated ICU surge based on models with inadequate data. The cost of uncertainty is profound – in terms of lives, liberties and the prosperity of Torontonians. Perhaps the City of Toronto should fill the gap, or else we will be forced to continue to build contingency plans on quicksand.

With files by Aline Philbert, epidemiologist, statistician and researcher at Panzi Hospital & Foundation.

Suvendrini Lena is an assistant professor of neurology and psychiatry at the University of Toronto. 


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