MULTISITE DISEASE ANALYTICS WITH APPLICATIONS TO ESTIMATING COVID-19 UNDETECTED CASES IN CANADA

• 成果类型: Article
• 署名作者: Parker, Matthew R. P.; Cao, Jiguo; Cowen, Laura l. E.; Elliott, Lloyd T.; Ma, Junling
• 署名单位: Simon Fraser University; University of Victoria
• 刊物名称: ANNALS OF APPLIED STATISTICS
• ISSN/ISSBN: 1932-6157
• DOI: 10.1214/24-AOAS1915
• 发表日期: 2024
• 页码: 2928-2949
• 关键词: n-mixture models

摘要

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 multivalued 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.