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DESCRIPTION:Harlan Campbell\, PhD\n\nPostdoctoral Research Fellow | Departm
 ent of Statistics | University of British Columbia\n\nWhere: Virtual | Zoo
 m\n\nAbstract\n\nEstimating the COVID-19 infection fatality rate (IFR) has
  proven to be particularly challenging –and rather controversial– due in l
 arge part to the fact that both the data on deaths and the data on the num
 ber of individuals infected are subject to many different biases. In this 
 presentation\, I consider a Bayesian evidence synthesis approach which\, w
 hile simple enough for researchers to understand and use\, accounts for ma
 ny important sources of bias and uncertainty inherent in both the seroprev
 alence and mortality data. With the understanding that the results of one'
 s evidence synthesis may be largely driven by which studies are included a
 nd which are excluded\, two separate parallel analyses are conducted based
  on two different lists of eligible studies. The various challenges encoun
 tered in estimating the COVID-19 IFR provide valuable lessons for epidemio
 logists conducting evidence synthesis with challenging data.\n\nLearning O
 bjectives\n\n\n	Understand the various challenges of working with COVID-19 
 seroprevalence and mortality data and how these challenges can\, to a cert
 ain degree\, be addressed with Bayesian methods\n	Discuss how the results o
 f one's evidence synthesis analysis can be greatly impacted by which studi
 es are included and which are excluded. It is therefore important to deter
 mine the how the uncertainty inherent in one’s risk of bias assessment can
  impact parameter estimates\n	Describe how the lethality of COVID-19 likely
  varies with population age\, wealth\, and other factors which remain poor
 ly understood\, even today\n\n\nSpeaker Bio\n\nHarlan Campbell is a statis
 tician and is currently working as a postdoctoral research fellow in the D
 epartment of Statistics at the University of British Columbia. His work fo
 cuses on developing statistical methods with a wide range of applications 
 including in clinical trials\, epidemiology\, ecology\, and psychology. He
  is also interested in better understanding the parallels between frequent
 ist and Bayesian paradigms\, and in addressing the so-called reproducibili
 ty crisis. He earned his PhD in statistics at the University of British Co
 lumbia\, after completing his masters at Simon Fraser University\, and his
  undergraduate studies at McGill University.\n\nPresented as part of the E
 pidemiology Seminar Series\n\nThe Department of Epidemiology\, Biostatisti
 cs and Occupational Health Seminar Series is a self-approved Group Learnin
 g Activity (Section 1) as defined by the maintenance of certification prog
 ram of the Royal College of Physicians and Surgeons of Canada\n
DTSTART:20230220T210000Z
DTEND:20230220T220000Z
SUMMARY:Determining the lethality of COVID-19: Lessons for addressing bias 
 and uncertainty in evidence synthesis
URL:https://www.mcgill.ca/epi-biostat-occh/channels/event/determining-letha
 lity-covid-19-lessons-addressing-bias-and-uncertainty-evidence-synthesis-3
 45577
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