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BEGIN:VEVENT
DTSTAMP:20260427T064524Z
DTSTART;VALUE=DATE-TIME:20250224T130000
DTEND;VALUE=DATE-TIME:20250224T140000
SUMMARY:WCPM: Alice Cobella\, ÌÇÐÄTV
TZID:Europe/London
UID:20250224-8ac672c493cda1240193cff0a10017bf@warwick.ac.uk
CREATED:20250213T220633Z
DESCRIPTION:Location: A2.05B (there will be an informal sandwich lunch ou
 tside D2.02 at 12.30) Time: 13.00-14.00 To join this meeting online clic
 k here. Link opens in a new windoLink opens in a new window Modelling se
 verity of emerging epidemics via state-space models and the lifebelt par
 ticle filter. The inference of the severity (measured as probability of 
 death) of an emerging epidemic is a non-trivial statistical challenge. I
 nformation on the most severe events is often collected in terms of time
  series of the counts of severely symptomatic cases and deaths per day/w
 eek\; no data are usually available on the number of patients who recove
 r from the disease or on timing between consecutive events. Cumulative-b
 ased counts estimators are well known to be biased but they are still co
 nsidered the gold standard\, as the lack of suitable data seems to preve
 nt the use alternative methods. In this talk we approach this problem fr
 om a state-space model (SSM) perspective\, properly accounting for all t
 he unknown underlying processes. Particle-filtering methods can be used 
 to drive Bayesian inference of the severity parameters of interest\, but
  they are prone to fail in this context given the low counts and discret
 e system. We propose the Lifebelt Particle Filter (LBPF)\, a new particl
 e method that is robust to particle collapse thanks to the use of determ
 inistic mixtures in the importance distribution and a conservative resam
 pling scheme
LOCATION:A2.05B
CATEGORIES:WCPM,Women in Science
LAST-MODIFIED:20250213T220633Z
ORGANIZER;CN=Sarah Jarratt:
END:VEVENT
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