Review Articles

Evaluation of the Canadian government policies on controlling the COVID-19 outbreaks

Mengyao Chen ,

Department of Statistics and Finance, University of Science and Technology of China, Anhui, People's Republic of China

Yuehua Wu ,

Department of Mathematics and Statistics, York University, Toronto, Canada

Baisuo Jin

Department of Statistics and Finance, University of Science and Technology of China, Anhui, People's Republic of China

Pages | Received 13 Jul. 2021, Accepted 03 Apr. 2023, Published online: 28 Apr. 2023,
  • Abstract
  • Full Article
  • References
  • Citations

In this paper, we investigate the COVID-19 pandemic in Canada and evaluate the Canadian government policies on controlling COVID-19 outbreaks. The first case of COVID-19 was reported in Ontario on 25 January 2020. Since then, there have been over million cases by now. During this time period, the federal, provincial and local governments have implemented regulations and policies in order to control the pandemic. To evaluate these government policies, which may be done by analysing the infection rate, infection period and reproductive number of COVID-19, we approach the problem by introducing an extended susceptible-exposed-infectious-removed (SEIR) model and conducting the model inference by using the iterated filter ensemble adjustment Kalman filter (IF-EAKF) algorithm. We first divide the time period into phases according to the policy intensities in each province by segmenting the time period from 4 March 2020 to 31 October 2020 into three time phases: the exploding phase, the strict policy implementation phase, and the provincial reopening phase. We then use IF-EAKF algorithm to obtain the estimates of the model parameters. We show that the infection rate in the second phase is lower than that in both first and third phases. We also discuss the number of new COVID-19 cases under different policy intensities and different policy durations in the third wave of the pandemic.


  • Anderson, J. L. (2001). An ensemble adjustment Kalman filter for data assimilation. Monthly Weather Review129(12), 2884–2903.<2884:AEAKFF>2.0.CO;2 
  • Berry, I., O'Neill, M., Sturrock, S. L., Wright, J. E., Acharya, K., Brankston, G., Harish, V., Kornas, K., Maani, N., Naganathan, T., Obress, L., Rossi, T. M., Simmons, A. E., Van Camp, M., Xie, X. T., Tuite, A. R., Greer, A. L., Fisman, D. N., & Soucy, J. R. (2021). A sub-national real-time epidemiological and vaccination database for the COVID-19 pandemic in Canada. Scientific Data8(1), 1–10.
  • Chimmula, V. K. R., & Zhang, L. (2020). Time series forecasting of COVID-19 transmission in Canada using LSTM networks. Chaos, Solitons & Fractals135, 109864. 
  • Ciuriak, D. (2020). The policy response to the coronavirus pandemic: Recommendations for Canada. Opinion, Centre for International Governance Innovation. 
  • Deo, V., & Grover, G. (2021). A new extension of state-space SIR model to account for underreporting – an application to the COVID-19 transmission in California and Florida. Results in Physics24, 104182. 
  • Gu, J., Yan, H., Huang, Y., Zhu, Y., Sun, H., Zhang, X., Wang, Y., Qiu, Y., & Chen, S. (2020). Better strategies for containing COVID-19 epidemics – a study of 25 countries via an extended varying coefficient SEIR model. medRxiv. 
  • Jüni, P., Rothenbühler, M., Bobos, P., Thorpe, K. E., da Costa, B. R., D. N. Fisman, Slutsky, A. S., & Gesink, D. (2020). Impact of climate and public health interventions on the COVID-19 pandemic: A prospective cohort study. CMAJ192(21), E566–E573. 
  • Karaivanov, A., Lu, S. E., Shigeoka, H., Chen, C., & Pamplona, S. (2020). Face masks, public policies and slowing the spread of COVID-19: Evidence from Canada. Technical report, National Bureau of Economic Research. 
  • Kucharski, A. J., Russell, T. W., Diamond, C., Liu, Y., Edmunds, J., Funk, S., Eggo, R. M., Sun, F., Jit, M., Munday, J. D., & Davies, N. (2020). Early dynamics of transmission and control of COVID-19: A mathematical modelling study. The Lancet Infectious Diseases20(5), 553–558.
  • Lai, S., Ruktanonchai, N. W., Zhou, L., Prosper, O., Luo, W., Floyd, J. R., Wesolowski, A., Santillana, M., Zhang, C., Du, X., & Yu, H. (2020). Effect of non-pharmaceutical interventions to contain COVID-19 in China. Nature585(7825), 410–413. 
  • Li, Q., Guan, X., Wu, P., Wang, X., Zhou, L., Tong, Y., Ren, R., Leung, K. S., Lau, E. H., Wong, J. Y., & Xing, X. (2020). Early transmission dynamics in Wuhan, China, of novel coronavirus-infected pneumonia. New England Journal of Medicine, 382(13), 1199-1207.
  • Li, R., Pei, S., Chen, B., Song, Y., Zhang, T., Yang, W., & Shaman, J. (2020). Substantial undocumented infection facilitates the rapid dissemination of novel coronavirus (SARS-CoV-2). Science (New York, N.Y.)368(6490), 489–493. 
  • Mossa-Basha, M., Medverd, J., Linnau, K. F., Lynch, J. B., Wener, M. H., Kicska, G., Staiger, T., & Sahani, D. V. (2020). Policies and guidelines for COVID-19 preparedness: Experiences from the university of Washington. Radiology296(2), E26–E31. 
  • Peng, L., Yang, W., Zhang, D., Zhuge, C., & Hong, L. (2020). Epidemic analysis of COVID-19 in China by dynamical modeling. Preprint arXiv:2002.06563
  • Prem, K., Liu, Y., Russell, T. W., Kucharski, A. J., Eggo, R. M., Davies, N., Flasche, S., Clifford, S., Pearson, C. A., Munday, J. D., & Abbott, S. (2020). The effect of control strategies to reduce social mixing on outcomes of the COVID-19 epidemic in Wuhan, China: A modelling study. The Lancet Public Health5(5), e261–e270. 
  • Rodríguez-Morales, A. J., MacGregor, K., Kanagarajah, S., Patel, D., & Schlagenhauf, P. (2020). Going global – travel and the 2019 novel coronavirus. Travel Medicine and Infectious Disease33, 101578. 
  • Statistic Canada (2020). Non-resident travellers entering Canada. 
  • Tian, H., Liu, Y., Li, Y., Wu, C.-H., Chen, B., Kraemer, M. U., Li, B., Cai, J., Xu, B., Yang, Q., Wang, B., Yang, P., Cui, Y., Song, Y., Zheng, P., Wang, Q., O. N. Bjornstad, Yang, R., Grenfell, B. T., & Dye, C. (2020). An investigation of transmission control measures during the first 50 days of the COVID-19 epidemic in China. Science (New York, N.Y.)368(6491), 638–642. 
  • To, T., Zhang, K., Maguire, B., Terebessy, E., Fong, I., Parikh, S., & Zhu, J. (2020). Correlation of ambient temperature and COVID-19 incidence in Canada. Science of the Total Environment750, 141484. 
  • Xie, J., & Zhu, Y. (2020). Association between ambient temperature and COVID-19 infection in 122 cities from China. Science of the Total Environment724, 138201.
  • Xu, J., & Tang, Y. (2021). An integrated epidemic modelling framework for the real-time forecast of COVID-19 outbreaks in current epicentres. Statistical Theory and Related Fields5(3), 1–21. 
  • Zambrano-Monserrate, M. A., Ruano, M. A., & Sanchez-Alcalde, L. (2020). Indirect effects of COVID-19 on the environment. Science of the Total Environment728, 138813. 

To cite this article: Mengyao Chen, Yuehua Wu & Baisuo Jin (2023) Evaluation of the Canadian government policies on controlling the COVID-19 outbreaks, Statistical Theory and Related Fields, 7:3, 223-234, DOI: 10.1080/24754269.2023.2201108

To link to this article: