Abstract
Even with daily case counts, the true scope of the COVID-19 pandemic in Canada is unknown due to undetected cases. We develop a novel multisite disease analytics model which estimates undetected cases using discrete-valued multivariate time series in the framework of Bayesian hidden Markov modelling techniques. We apply our multisite model to estimate the pandemic scope using publicly available disease count data including detected cases, recoveries among detected cases, and total deaths. These counts are used to estimate the case detection probability, the infection fatality rate through time, the probability of recovery, and several important population parameters including the rate of spread and importation of external cases. We estimate the total number of active COVID-19 cases per region of Canada for each reporting interval. We applied this multisite model Canada-wide to all provinces and territories, providing an estimate of the total COVID-19 burden for the 90 weeks from 23 April 2020 to 10 February 2022. We also applied this model to the five health authority regions of British Columbia, Canada, describing the pandemic in B.C. over the 31 weeks from 2 April 2020 to 30 October 2020.
Funding Statement
LC was supported by a Michael Smith Foundation for Health Research and Victoria Hospitals Foundation Grant # COV-2020-1061 and a Canadian Statistical Sciences Institute Rapid Response Program- COVID-19.
We would like to acknowledge the support of the Natural Sciences and Engineering Research Council of Canada (NSERC) for providing PGS-D support for MP [funding reference number 569754].
LTE was supported by a Michael Smith Health Research BC Scholar Award and NSERC grant numbers RGPIN/05484-2019 and DGECR/00118-2019.
JC was supported by an NSERC discovery grant (RGPIN-2023-04057) and the Canada Research Chairs program.
Acknowledgments
The authors are grateful to the Editor, the Associate Editor, and the anonymous reviewers for their time and effort. Their valuable feedback helped us to improve the quality of our manuscript. The authors gratefully acknowledge the Micheal Smith Foundation for Health Research and the Victoria Hospitals Foundation for support through a COVID-19 Research Response grant as well as a Canadian Statistical Sciences Institute Rapid Response Program—COVID-19 grant to LC that supported this research.
Citation
Matthew R. P. Parker. Jiguo Cao. Laura L. E. Cowen. Lloyd T. Elliott. Junling Ma. "Multisite disease analytics with applications to estimating COVID-19 undetected cases in Canada." Ann. Appl. Stat. 18 (4) 2928 - 2949, December 2024. https://doi.org/10.1214/24-AOAS1915
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