While surveillance can identify changes in COVID-19 transmission patterns over time and space,sections of the population at risk,and the efficacy of public health measures,reported cases of COVID-19 are generally unde...While surveillance can identify changes in COVID-19 transmission patterns over time and space,sections of the population at risk,and the efficacy of public health measures,reported cases of COVID-19 are generally understood to only capture a subset of the actual number of cases.Our primary objective was to estimate the percentage of cases reported in the general community,considered as those that occurred outside of long-term care facilities(LTCFs),in specific provinces and Canada as a whole.We applied a methodology using the delay-adjusted case fatality ratio(CFR)to all cases and deaths,as well as those representing the general community.Our second objective was to assess whether the assumed CFR(mean=1.38%)was appropriate for calculating underestimation of cases in Canada.Estimates were developed for the period from March 11th,2020 to September 16th,2020.Estimates of the percentage of cases reported(PrCR)and CFR varied spatially and temporally across Canada.For the majority of provinces,and for Canada as a whole,the PrCR increased through the early stages of the pandemic.The estimated PrCR in general community settings for all of Canada increased from 18.1%to 69.0%throughout the entire study period.Estimates were greater when considering only those data from outside of LTCFs.The estimated upper bound CFR in general community settings for all of Canada decreased from 9.07%on March 11th,2020 to 2.00%on September 16th,2020.Therefore,the true CFR in the general community in Canada was likely less than 2%on September 16th.According to our analysis,some provinces,such as Alberta,Manitoba,Newfoundland and Labrador,Nova Scotia,and Saskatchewan reported a greater percentage of cases as of September 16th,compared to British Columbia,Ontario,and Quebec.This could be due to differences in testing rates and criteria,demographics,socioeconomic factors,race,and access to healthcare among the provinces.Further investigation into these factors could reveal differences among provinces that could partially explain the variation in estimates of PrCR and CFR identified in our study.The estimates provide context to the summative state of the pandemic in Canada,and can be improved as knowledge of COVID-19 reporting rates and disease characteristics are advanced.展开更多
The SARS-CoV-2 virus causes the disease COVID-19,and has caused high morbidity and mortality worldwide.Empirical models are useful tools to predict future trends of disease progression such as COVID-19 over the near-t...The SARS-CoV-2 virus causes the disease COVID-19,and has caused high morbidity and mortality worldwide.Empirical models are useful tools to predict future trends of disease progression such as COVID-19 over the near-term.A modified Incidence Decay and Exponential Adjustment(m-IDEA)model was developed to predict the progression of infectious disease outbreaks.The modification allows for the production of precise daily estimates,which are critical during a pandemic of this scale for planning purposes.The m-IDEA model was employed using a range of serial intervals given the lack of knowledge on the true serial interval of COVID-19.Both deterministic and stochastic approaches were applied.Model fitting was accomplished through minimizing the sum-of-square differences between predicted and observed daily incidence case counts,and performance was retrospectively assessed.The performance of the m-IDEA for projection cases in the nearterm was improved using shorter serial intervals(1e4 days)at early stages of the pandemic,and longer serial intervals at mid-to late-stages(5e9 days)thus far.This,coupled with epidemiological reports,suggests that the serial interval of COVID-19 might increase as the pandemic progresses,which is rather intuitive:Increasing serial intervals can be attributed to gradual increases in public health interventions such as facility closures,public caution and social distancing,thus increasing the time between transmission events.In most cases,the stochastic approach captured the majority of future reported incidence data,because it accounts for the uncertainty around the serial interval of COVID-19.As such,it is the preferred approach for using the m-IDEA during dynamic situation such as in the midst of a major pandemic.展开更多
基金This work was funded by the Public Health Agency of Canada.
文摘While surveillance can identify changes in COVID-19 transmission patterns over time and space,sections of the population at risk,and the efficacy of public health measures,reported cases of COVID-19 are generally understood to only capture a subset of the actual number of cases.Our primary objective was to estimate the percentage of cases reported in the general community,considered as those that occurred outside of long-term care facilities(LTCFs),in specific provinces and Canada as a whole.We applied a methodology using the delay-adjusted case fatality ratio(CFR)to all cases and deaths,as well as those representing the general community.Our second objective was to assess whether the assumed CFR(mean=1.38%)was appropriate for calculating underestimation of cases in Canada.Estimates were developed for the period from March 11th,2020 to September 16th,2020.Estimates of the percentage of cases reported(PrCR)and CFR varied spatially and temporally across Canada.For the majority of provinces,and for Canada as a whole,the PrCR increased through the early stages of the pandemic.The estimated PrCR in general community settings for all of Canada increased from 18.1%to 69.0%throughout the entire study period.Estimates were greater when considering only those data from outside of LTCFs.The estimated upper bound CFR in general community settings for all of Canada decreased from 9.07%on March 11th,2020 to 2.00%on September 16th,2020.Therefore,the true CFR in the general community in Canada was likely less than 2%on September 16th.According to our analysis,some provinces,such as Alberta,Manitoba,Newfoundland and Labrador,Nova Scotia,and Saskatchewan reported a greater percentage of cases as of September 16th,compared to British Columbia,Ontario,and Quebec.This could be due to differences in testing rates and criteria,demographics,socioeconomic factors,race,and access to healthcare among the provinces.Further investigation into these factors could reveal differences among provinces that could partially explain the variation in estimates of PrCR and CFR identified in our study.The estimates provide context to the summative state of the pandemic in Canada,and can be improved as knowledge of COVID-19 reporting rates and disease characteristics are advanced.
基金I would like to thank the Knowledge Synthesis team members within the Public Health Risk Sciences Division of Public Health Agency of Canada.Their daily literature scans and summarization of Sars-CoV-2 publications contributed to the quick preparation of the work presented here.Thanks to Charly Phillips(Public Health Risk Sciences Division of Public Health Agency of Canada)for her assistance summarizing serial interval values from the literature.
文摘The SARS-CoV-2 virus causes the disease COVID-19,and has caused high morbidity and mortality worldwide.Empirical models are useful tools to predict future trends of disease progression such as COVID-19 over the near-term.A modified Incidence Decay and Exponential Adjustment(m-IDEA)model was developed to predict the progression of infectious disease outbreaks.The modification allows for the production of precise daily estimates,which are critical during a pandemic of this scale for planning purposes.The m-IDEA model was employed using a range of serial intervals given the lack of knowledge on the true serial interval of COVID-19.Both deterministic and stochastic approaches were applied.Model fitting was accomplished through minimizing the sum-of-square differences between predicted and observed daily incidence case counts,and performance was retrospectively assessed.The performance of the m-IDEA for projection cases in the nearterm was improved using shorter serial intervals(1e4 days)at early stages of the pandemic,and longer serial intervals at mid-to late-stages(5e9 days)thus far.This,coupled with epidemiological reports,suggests that the serial interval of COVID-19 might increase as the pandemic progresses,which is rather intuitive:Increasing serial intervals can be attributed to gradual increases in public health interventions such as facility closures,public caution and social distancing,thus increasing the time between transmission events.In most cases,the stochastic approach captured the majority of future reported incidence data,because it accounts for the uncertainty around the serial interval of COVID-19.As such,it is the preferred approach for using the m-IDEA during dynamic situation such as in the midst of a major pandemic.
