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UID:www.tcs.tifr.res.in/event/1201
DTSTAMP:20230914T125954Z
SUMMARY:Shift\, scale and restart smaller models to estimate larger ones: A
 gent based simulators in epidemiology
DESCRIPTION:Speaker: Daksh Mittal (TIFR Mumbai)\n\nAbstract: \nAgent-based 
 simulators are a popular epidemiological modelling tool to study the impac
 t of various non-pharmaceutical interventions in managing an evolving pand
 emic. They provide the flexibility to accurately model a heterogeneous pop
 ulation with time and location varying\, person specific interactions. To 
 accurately model detailed behaviour\, typically each person is separately 
 modelled. This however\, may make computational time prohibitive when the 
 region population is large and when time horizons involved are large.  In
  this talk we review the agent based city simulator developed by IISC-TIFR
  to model Covid epidemic and dig deeper into the underlying probabilistic 
 structure of the simulator (ABS) to arrive at modifications that allow sma
 ller models to give accurate statistics for larger models. We exploit the 
 observations that in the initial disease spread phase\, the starting infec
 tions create a family tree of infected individuals more-or-less independen
 t of the other trees and are modelled well as a multi-type super-critical 
 branching process. Soon after\, once enough people have been infected\, th
 e future evolution of the pandemic is closely approximated by its mean fie
 ld limit with a random starting state. We build upon these insights to dev
 elop a shift\, scale and restart algorithm for the simulator that accurate
 ly evaluates the ABS's performance using a much smaller model. We provide 
 theoretical support for the proposed approach through an asymptotic analys
 is where the population size increases to infinity.\n
URL:https://www.tcs.tifr.res.in/web/events/1201
DTSTART;TZID=Asia/Kolkata:20220506T163000
DTEND;TZID=Asia/Kolkata:20220506T173000
LOCATION:A-201 (STCS Seminar Room)
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