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SUMMARY:Using viral loads to improve COVID-19 surveillance - James Hay\, H
 arvard University
DTSTART:20210129T150000Z
DTEND:20210129T160000Z
UID:TALK154924@talks.cam.ac.uk
CONTACT:Theodora Anderson
DESCRIPTION:Virologic testing has been central to tracking the COVID-19 pa
 ndemic. Most routine tests provide a quantitative result in the form of a 
 cycle threshold (Ct) value -- a metric proportional to the log viral load.
  These data are usually reported as a binary result\, thereby removing muc
 h of the information inherent in the full quantitative value. We propose t
 hat\, despite their caveats and variability\, the Ct value is a useful mea
 sure that can be harnessed to improve public health surveillance. I will p
 resent three projects that combine mathematical models and an understandin
 g of viral kinetics to generate insights into surveillance testing\, effic
 ient sample pooling and reconstruction of epidemic dynamics. \n\nBio: I co
 mpleted my PhD with Steven Riley in 2019 at the Department of Infectious D
 isease Epidemiology\, Imperial College London\, where I developed methods 
 to infer antibody kinetics\, infection histories and epidemic dynamics usi
 ng serological data. I have since been a postdoc in CCCD at Harvard SPH wo
 rking with Michael Mina on COVID-19 testing (though I was meant to be usin
 g epitope-level serological data to study population immunology).
LOCATION:Venue to be confirmed
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