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COVID-19 infected cases in Canada: Short-term forecasting models

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  • Mo’tamad H Bata
  • Rupp Carriveau
  • David S-K Ting
  • Matt Davison
  • Anneke R Smit

Abstract

Governments have implemented different interventions and response models to combat the spread of COVID-19. The necessary intensity and frequency of control measures require us to project the number of infected cases. Three short-term forecasting models were proposed to predict the total number of infected cases in Canada for a number of days ahead. The proposed models were evaluated on how their performance degrades with increased forecast horizon, and improves with increased historical data by which to estimate them. For the data analyzed, our results show that 7 to 10 weeks of historical data points are enough to produce good fits for a two-weeks predictive model of infected case numbers with a NRMSE of 1% to 2%. The preferred model is an important quick-deployment tool to support data-informed short-term pandemic related decision-making at all levels of governance.

Suggested Citation

  • Mo’tamad H Bata & Rupp Carriveau & David S-K Ting & Matt Davison & Anneke R Smit, 2022. "COVID-19 infected cases in Canada: Short-term forecasting models," PLOS ONE, Public Library of Science, vol. 17(9), pages 1-16, September.
    • Handle: RePEc:plo:pone00:0270182
    DOI: 10.1371/journal.pone.0270182
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    References listed on IDEAS

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    1. Samuel V. Scarpino & Giovanni Petri, 2019. "On the predictability of infectious disease outbreaks," Nature Communications, Nature, vol. 10(1), pages 1-8, December.
    2. Yan Hao & Ting Xu & Hongping Hu & Peng Wang & Yanping Bai, 2020. "Prediction and analysis of Corona Virus Disease 2019," PLOS ONE, Public Library of Science, vol. 15(10), pages 1-15, October.
    3. Fanelli, Duccio & Piazza, Francesco, 2020. "Analysis and forecast of COVID-19 spreading in China, Italy and France," Chaos, Solitons & Fractals, Elsevier, vol. 134(C).
    4. Yihang Zhu & Yinglei Zhao & Jingjin Zhang & Na Geng & Danfeng Huang, 2019. "Spring onion seed demand forecasting using a hybrid Holt-Winters and support vector machine model," PLOS ONE, Public Library of Science, vol. 14(7), pages 1-18, July.
    5. Loring J. Thomas & Peng Huang & Fan Yin & Xiaoshuang Iris Luo & Zack W. Almquist & John R. Hipp & Carter T. Butts, 2020. "Spatial heterogeneity can lead to substantial local variations in COVID-19 timing and severity," Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, vol. 117(39), pages 24180-24187, September.
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