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SUMMARY:Epidemic models with ‘time since infection’ - Joseph Peterson\
 , DAMTP
DTSTART:20210316T130000Z
DTEND:20210316T140000Z
UID:TALK157717@talks.cam.ac.uk
CONTACT:Camille Scalliet
DESCRIPTION:Zoom link: https://maths-cam-ac-uk.zoom.us/j/94018037756\n\nEp
 idemic models are useful tools in the fight against infectious diseases\, 
 but their usefulness is limited (in part) by their ability to accurately d
 escribe the underlying disease dynamics. The most accurate epidemic models
  are typically the most computationally expensive\, and “compartment mod
 els” offer the most popular compromise between speed and accuracy. In a 
 compartment model\, an infected person progresses through a series of arti
 fical ‘stages’ or ‘compartments’ (e.g. exposed\, infectious\, reco
 vered)\, and the resultant equations are easily solved by standard tools f
 or ordinary differential equations. A more realistic model would describe 
 disease dynamics as a continuous function of time since infection (TSI)\, 
 but the computational cost of a TSI model is typically assumed to be large
  by comparison.\nIn this talk\, we share our recent work on TSI models. Fi
 rst\, we improve upon existing TSI models by using a ‘filter’ to parti
 tion the infection population into discrete compartments\, as and when suc
 h measurements are necessary for informing policy decisions (e.g. predicti
 ng hospitalizations\, deaths\, etc.). Second\, we provide a more efficient
  numerical method for solving the equations of a TSI model with spectral a
 ccuracy. Given this numerical approach\, we find that TSI models are now c
 ost-competitive with the standard ‘compartment’ strategy for many appl
 ications.\n
LOCATION:via zoom\, meeting ID 940-1803-7756
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