Exploring the percentage of COVID-19 cases reported in the community in Canada and associated case fatality ratios

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.

A simple approach to estimate the instantaneous case fatality ratio:Using the publicly available COVID-19 surveillance data in Canada as an example

The case fatality ratio(CFR)is one of the key measurements to evaluate the clinical severity of infectious diseases.The CFR may vary due to change in factors that affect the mortality risk.In this study,we developed a simple likelihood-based framework to estimate the instantaneous CFR of infectious diseases.We used the publicly available COVID-19 surveillance data in Canada for demonstration.We estimated the mean fatality ratio of reported COVID-19 cases(rCFR)in Canada was estimated at 6.9%(95%CI:4.5e10.6).We emphasize the extensive implementation of the constructed instantaneous CFR that is to identify the key determinants affecting the mortality risk.

An evaluation of mathematical models for the outbreak of COVID-19

Mathematical modelling performs a vital part in estimating and controlling the recent outbreak of coronavirus disease 2019(COVID-19).In this epidemic,most countries impose severe intervention measures to contain the spread of COVID-19.The policymakers are forced to make difficult decisions to leverage between health and economic development.How and when tomake clinical and public health decisions in an epidemic situation is a challenging question.The most appropriate solution is based on scientific evidence,which is mainly dependent on data and models.So one of the most critical problems during this crisis is whether we can develop reliable epidemiological models to forecast the evolution of the virus and estimate the effectiveness of various intervention measures and their impacts on the economy.There are numerous types of mathematical model for epidemiological diseases.In this paper,we present some critical reviews on mathematical models for the outbreak of COVID-19.Some elementary models are presented as an initial formulation for an epidemic.We give some basic concepts,notations,and foundation for epidemiological modelling.More related works are also introduced and evaluated by considering epidemiological features such as disease tendency,latent effects,susceptibility,basic reproduction numbers,asymptomatic infections,herd immunity,and impact of the interventions.