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Related Experiment Video

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A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

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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.

|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.

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Published on: June 5, 2021

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.