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SUMMARY:An estimation framework to study epidemic fade-out using multiple 
 outbreak data - Punya Alahakoon\, University of Melbourne
DTSTART:20221013T100000Z
DTEND:20221013T110000Z
UID:TALK183986@talks.cam.ac.uk
CONTACT:Dr Ciara Dangerfield
DESCRIPTION:Deterministic epidemic models that allow for replenishment of 
 susceptibles display damped oscillatory behaviour. However\, dynamics of e
 pidemics are influenced by stochastic effects\, particularly when the dise
 ase prevalence is low. At the beginning of an epidemic\, due to low preval
 ence levels\, stochastic die out is possible and is well studied in the li
 terature. Once an epidemic takes off\, extinction is highly unlikely\, but
  the probability of extinction increases again with the decline of the wav
 e. Extinction taking place during this period\, that is\, during the troug
 h between the first and the potential second wave is known as epidemic fad
 e-out. We consider a set of epidemics that evolve independently. Some of t
 he epidemics may display fade-out while others do not. While fade-out is n
 ecessarily a stochastic phenomenon\, the probability of this event taking 
 place depends on the parameters associated with the epidemic. Therefore\, 
 we investigate whether time-series data of multiple outbreaks can be used 
 to identify key drivers of epidemic fade-out across sub-populations in whi
 ch the epidemics take place.\n\nIn this talk\, using synthetic data\, I wi
 ll illustrate how a Bayesian hierarchical modelling approach can 1) identi
 fy when the sub-population specific model parameters supporting each epide
 mic have significant variability and 2) estimate the probability of epidem
 ic fade-out for each sub-population. I will also demonstrate that a hierar
 chical analysis improves the estimates compared to when the epidemics are\
 nconsidered independently. Our estimation framework is applicable to other
  similar biological data.
LOCATION:Zoom
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