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UID:20250715T214240EDT-8897BFjzO1@132.216.98.100
DTSTAMP:20250716T014240Z
DESCRIPTION:Epidemic Forecasting using Delayed Time Embedding.\n\n\n	Abstrac
 t:\n\n\nForecasting the future trajectory of an outbreak plays a crucial r
 ole in the mission of managing emerging infectious disease epidemics. Comp
 artmental models\, such as the Susceptible-Exposed-Infectious-Recovered (S
 EIR)\, are the most popular tools for this task. They have been used exten
 sively to combat many infectious disease outbreaks including the current C
 OVID-19 pandemic. One downside of these models is that they assume that th
 e dynamics of an epidemic follow a pre-defined dynamical system which may 
 not capture the true trajectories of an outbreak. Consequently\, the users
  need to make several modifications throughout an epidemic to ensure their
  models fit well with the data. However\, there is no guarantee that these
  modifications can also help increase the precision of forecasting. In thi
 s talk\, I will introduce a new method for predicting epidemics that does 
 not make any assumption on the underlying dynamical system. Our method com
 bines sparse random feature expansion and delay embedding to learn the tra
 jectory of an epidemic.\n\n\n	Speaker\n\n\nDr. Lam Ho is an Associate Profe
 ssor\, Canada Research Chair in Stochastic Modelling in the Department of 
 Mathematics and Statistics at Dalhousie University. His research focuses o
 n stochastic modelling\, mathematical biology\, machine Learning\, evoluti
 onary Biology\, infectious disease epidemiology statistics\, reliability a
 nd statistical computing.\n\nhttps://mcgill.zoom.us/j/83436686293?pwd=b0Rm
 WmlXRXE3OWR6NlNIcWF5d0dJQT09\n\nMeeting ID: 834 3668 6293\n\nPasscode: 123
 45\n
DTSTART:20230217T213000Z
DTEND:20230217T223000Z
SUMMARY:Lam Ho (Dalhousie University)
URL:/mathstat/channels/event/lam-ho-dalhousie-universi
 ty-346116
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