Laboratory-confirmed COVID-19 in children and youth in Canada, January 15-April 27, 2020.

Understanding the epidemiology of COVID-19 among children and youth in Canada will help to inform public health measures in settings where children gather. As of April 27, 2020, provinces and territories provided the Public Health Agency of Canada with detailed information on 24,079 cases, of which 3.9% (n=938) were younger than 20 years of age. The detection rate per 100,000 population was lower in this age group (11.9 per 100,000), compared with those aged 20-59 years (72.4 per 100,000) and 60 and older (113.6 per 100,000). The median age among those younger than 20 years of age was 13 years, and cases were distributed equally across male and female genders. Among provinces and territories with more than 100 cases, 1.6% to 9.8% of cases were younger than 20 years of age. Cases in this age group were more likely to be asymptomatic: 10.7% compared with 2.4% in those aged 20-59 years and 4.1% in those aged 60 and older. Children and youth experienced severe outcomes less often, but 2.2% (n=15/672) of cases within this age group were severe enough to require hospitalization. Based on available exposure information, 11.3% (n=59/520) of cases aged younger than 20 years had no known contact with a case. Canadian findings align with those of other countries.

National Influenza Mid-Season Report, 2019-2020.

Canada's national influenza season started in week 47 between November 17 to 23, 2019. Of the 3,762 laboratory-confirmed influenza detections reported from August 25 to December 14, 2019, 61% were influenza A, and of those subtyped, 68% were A(H3N2). Influenza B detections are above average for this time of year. Indicators of influenza activity are within the expected range for this time of year. The majority of hospitalizations reported by provinces and territories have been associated with influenza A(H3N2) (76%), and the greatest proportion have been among adults 65 years and older (40%). Among sentinel pediatric influenza hospitalizations, 55% were associated with influenza B and the majority have been under five years of age (63%). Antigenic and genetic characterization results to date suggest that the majority of circulating A(H3N2) and B viruses are not similar to the virus components recommended for use in the 2019-2020 seasonal influenza vaccines and that the majority of circulating A(H1N1) viruses are similar to the vaccine reference strains.

Seasonal Influenza Forecasting in Real Time Using the Incidence Decay With Exponential Adjustment Model.

BACKGROUND: Seasonal influenza epidemics occur frequently. Rapid characterization of seasonal dynamics and forecasting of epidemic peaks and final sizes could help support real-time decision-making related to vaccination and other control measures. Real-time forecasting remains challenging. METHODS: We used the previously described "incidence decay with exponential adjustment" (IDEA) model, a 2-parameter phenomenological model, to evaluate the characteristics of the 2015-2016 influenza season in 4 Canadian jurisdictions: the Provinces of Alberta, Nova Scotia and Ontario, and the City of Ottawa. Model fits were updated weekly with receipt of incident virologically confirmed case counts. Best-fit models were used to project seasonal influenza peaks and epidemic final sizes. RESULTS: The 2015-2016 influenza season was mild and late-peaking. Parameter estimates generated through fitting were consistent in the 2 largest jurisdictions (Ontario and Alberta) and with pooled data including Nova Scotia counts (R0 approximately 1.4 for all fits). Lower R0 estimates were generated in Nova Scotia and Ottawa. Final size projections that made use of complete time series were accurate to within 6% of true final sizes, but final size was using pre-peak data. Projections of epidemic peaks stabilized before the true epidemic peak, but these were persistently early (~2 weeks) relative to the true peak. CONCLUSIONS: A simple, 2-parameter influenza model provided reasonably accurate real-time projections of influenza seasonal dynamics in an atypically late, mild influenza season. Challenges are similar to those seen with more complex forecasting methodologies. Future work includes identification of seasonal characteristics associated with variability in model performance.