Cumulative and incident cases
Cumulative cases are a sum of incident cases over time and provides historic information as well as the number of cases reported each day (Fig. 1A). This format can obscure shorter-term trends, especially as the y-axis maximum increases with the cumulative number of cases reported (9). For example, the jump in cases for Québec on 23 March 2020 (associated with an increase in testing) is no longer apparent in the epidemic curve but would have been in earlier epidemic curves. Daily incident cases is a preferable format to depict daily changes in cases (Fig. 1B). The use of symptom onset date is a more accurate measure in terms of epidemic progression with respect to time as the date of notification results in a time lag that needs to be considered when interpreting any epidemic curve. An important limitation of incident or cumulative confirmed cases is that they are largely influenced by testing capacity and variation in the definition of confirmed cases and screening strategy by jurisdiction, which continues to evolve over time (Additional File 1). Furthermore, absolute values should not be compared across jurisdictions given population size differences.
Crude incidence rate
This relative frequency measure improves comparability between jurisdictions by dividing the absolute number of incident cases by the population size corresponding to the numerator, such as the general population or a more specific at-risk population (e.g., cruise ship population). The crude incidence rate reveals larger differences between Québec and Ontario from mid-March to the end of May, compared to what the cumulative and daily incident curves were suggesting (Fig. 1B). One limitation of this approach is that the intensity of screening may not be proportional to population size. If, for example, a jurisdiction of 10,000 inhabitants tests 10 times more than a jurisdiction of 100,000, the resulting number of cases will be the same at equal prevalence levels, but the crude incidence rate per 100,000 will be 10 times larger in the smaller jurisdiction.
Crude testing rate and test positivity rate
A potential solution to support the interpretation of confirmed cases is the number of COVID-19 tests administered. This is an important marker of the availability of testing in a given area as areas with a higher availability of testing are likely to find more confirmed cases (10). The daily crude testing rate per 100,000 population for Ontario has drastically increased since the beginning of testing and in June, Ontario surpassed Québec in its crude testing rate (Fig. 1C). The daily test positivity ratio (TPR) informs us as to what proportion of the population tested is positive. From 8 June 2020 to 8 July 2020, the TPR for Ontario and Québec averaged 0.8% and 1.3% respectively, down from 1.6% and 4.3% just a month earlier. The TPR provides insight into the magnitude of COVID-19 in a community but is entirely dependent on who is eligible for testing. Furthermore, nasopharyngeal specimens are not always sampled correctly, which can lead to false negative results that can vary between sites (11). There will be more false positive tests as the frequency of the infection decreases, which also complicates the interpretation of this measure.
Hospitalizations and mortality rates
Hospitalizations and intensive care unit (ICU) admissions, as well as mortality are believed to be more reliable indicators to assess COVID-19 burden as they rely less on testing strategy and capacity, and provide measures of epidemic severity (12). Hospitalizations are new inpatient admissions presenting with less severe disease than those admitted to the ICU, which are excluded from this measure. As with incident cases, reporting the relative frequency of daily incident hospitalizations per 100,000 is preferable. Differences in hospital admission protocols, hospital capacities, and approaches for classifying COVID-19 mortality will result in jurisdictional differences in hospitalizations and COVID-19 mortality. The crude daily hospitalization rate shows a relatively stable trend for Ontario with a downward trend beginning in June, whereas for Québec, twice the magnitude of hospitalizations was experienced from April to May, followed by a downward trend similar to Ontario (Fig. 2A). The crude daily ICU admission rate shows again the differences between Québec and Ontario, with Ontario marginally surpassing Québec at the beginning of June although it is more difficult to interpret given the daily fluctuations (Fig. 2B). A moving 7-day average can be useful for such fluctuations, highlighting daily trends while filtering out the noise of daily data. The moving 7-day average highlights the timing of the mortality peaks, with Ontario’s occurring slightly before Québec at end of April (Fig. 2C). The magnitude of difference in the crude mortality rate between Québec and Ontario from April to May is particularly striking (Fig. 2D). Having age group specific hospitalizations and mortality would be particularly insightful given the large number of outbreaks reported among nursing and retirement homes - information that is not publicly available for Ontario or Québec.