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COVID-19 infected cases in Canada: Short-term forecasting models
Mo'tamad H Bata1, Rupp Carriveau1, David S-K Ting1
1Turbulence and Energy Lab, Ed Lumley Centre for Engineering Innovation, University of Windsor, Windsor, Ontario, Canada.
Plos One
|September 22, 2022
Summary
Forecasting COVID-19 cases in Canada is crucial for effective pandemic response. This study found that 7-10 weeks of data can accurately predict infections for two weeks, aiding decision-making.
Area of Science:
- Epidemiology
- Public Health
- Data Science
Background:
- Governments worldwide implemented diverse strategies to control COVID-19.
- Accurate projection of infected cases is vital for determining the intensity and frequency of control measures.
- Short-term forecasting models are essential for data-informed pandemic management.
Purpose of the Study:
- To propose and evaluate three short-term forecasting models for predicting COVID-19 infected cases in Canada.
- To assess model performance degradation with increased forecast horizons.
- To determine the impact of historical data volume on model accuracy.
Main Methods:
- Development of three distinct short-term forecasting models.
- Evaluation of model performance based on forecast horizon and historical data utilized.
- Analysis of model accuracy using metrics such as Normalized Root Mean Square Error (NRMSE).
Main Results:
- 7 to 10 weeks of historical data provide sufficient information for accurate short-term predictions.
- A two-week predictive model achieved a Normalized Root Mean Square Error (NRMSE) of 1% to 2%.
- Model performance is sensitive to the forecast horizon and the amount of historical data.
Conclusions:
- The study identified a preferred forecasting model for rapid deployment.
- The model supports evidence-based, short-term pandemic decision-making across all governance levels.
- Accurate short-term case projections are critical for effective public health interventions.
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