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Model-based forecasting for Canadian COVID-19 data.

Li-Pang Chen1, Qihuang Zhang1, Grace Y Yi1,2

  • 1Department of Statistical and Actuarial Sciences, University of Western Ontario, London, Ontario, Canada.

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Summary
This summary is machine-generated.

Canadian COVID-19 trends were forecasted using Smooth Transition Autoregressive (STAR), Neural Network (NN), and Susceptible-Infected-Removed (SIR) models. The Neural Network model showed superior performance, predicting an upward trend in confirmed cases across four provinces.

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Area of Science:

  • Epidemiology
  • Data Science
  • Public Health

Background:

  • The COVID-19 pandemic, declared by the WHO in March 2020, has led to a rapid global increase in cases and deaths.
  • Understanding the pandemic's impact on public health necessitates robust data analysis and forecasting.
  • This study specifically examines Canadian COVID-19 data to forecast short-term trends.

Purpose of the Study:

  • To forecast the dynamic short-term trend of COVID-19 in Canada.
  • To compare the predictive performance of three distinct modeling approaches: STAR, NN, and SIR models.
  • To provide model-based insights into the pandemic's evolution within Canada.

Main Methods:

  • Analysis of Canadian COVID-19 data from March 18, 2020, to August 16, 2020, focusing on Ontario, Alberta, British Columbia, and Quebec.
  • Application of Smooth Transition Autoregressive (STAR) models, Neural Network (NN) models, and Susceptible-Infected-Removed (SIR) models to time series data of confirmed cases.
  • Comparative analysis including daily infection data from Texas and New York state, USA.

Main Results:

  • STAR, NN, and SIR models produced varying results, with smaller prediction variability for short-term forecasts.
  • The Neural Network (NN) method demonstrated superior performance compared to STAR and SIR models.
  • All models forecasted an upward trend in confirmed COVID-19 cases for the four Canadian provinces from August 12 to August 23, 2020, with differing degrees of increase.

Conclusions:

  • Model-based insights are crucial for understanding pandemic dynamics.
  • The NN model offers a promising approach for short-term COVID-19 trend forecasting in Canada.
  • Continued monitoring and advanced modeling are essential for managing the ongoing pandemic.