BACKGROUND: The COVID-19 pandemic has had an unprecedented impact on citizens and health care systems globally . Valid near-term projections of cases are required to inform the escalation, maintenance and de-escalation of public health measures, and for short-term health care resource planning .
METHODS: Near-term case and epidemic growth rate projections for Canada were estimated using three phenomenological models: the logistic model, Generalized Richard's model (GRM) and a modified Incidence Decay and Exponential Adjustment (m-IDEA) model . Throughout the COVID-19 epidemic in Canada, these models have been validated against official national epidemiological data on an ongoing basis .
RESULTS: The best-fit models estimated that the number of COVID-19 cases predicted to be reported in Canada as of April 1 , 2020 and May 1 , 2020 would be 11,156 (90% prediction interval : 9,156-13,905) and 54,745 (90% prediction interval : 54,252-55,239). The three models varied in their projections and their performance over the first seven weeks of their implementation . Both the logistic model and GRM under-predicted cases reported a week following the projection date in nearly all instances . The logistic model performed best at the early stages, the m-IDEA model performed best at the later stages, and the GRM performed most consistently during the full period assessed .
CONCLUSIONS: All three models have yielded qualitatively comparable near-term forecasts of cases and epidemic growth for Canada . Under or over-estimation of projected cases and epidemic growth by these models could be associated with changes in testing policies and/or public health measures . Simple forecasting models can be invaluable in projecting the changes in trajectory of subsequent waves of cases to provide timely information to support the pandemic response.