Wasiur KhudaBukhsh
Postdoctoral Fellow, MBI
Solutions to ordinary differential equations (ODEs) describing the large-population limits of Markovian stochastic epidemic models can be interpreted as survival or cumulative hazard functions when analysing data on individuals sampled from the population. We refer to the individual-level survival and hazard functions derived from population-level equations as a Survival Dynamical System (SDS).
I will report some progress on developing an SDS methodology for non-Markovian dynamics. Measure-valued processes play a key role in this endeavour. For the non-Markovian set-up, the SDS-likelihood is shown to depend on solutions to partial differential equations instead of ODEs as before. Preliminary numerical results for SDS likelihood-based parameter inference will be shown. Finally, I will discuss an extension to non-Markovian epidemics on configuration model random graphs.
This event has been postponed.
THIS EVENT HAS BEEN POSTPONED Mathematical Biosciences Institute mbi-webmaster@osu.edu America/New_York publicWasiur KhudaBukhsh
Postdoctoral Fellow, MBI
Solutions to ordinary differential equations (ODEs) describing the large-population limits of Markovian stochastic epidemic models can be interpreted as survival or cumulative hazard functions when analysing data on individuals sampled from the population. We refer to the individual-level survival and hazard functions derived from population-level equations as a Survival Dynamical System (SDS).
I will report some progress on developing an SDS methodology for non-Markovian dynamics. Measure-valued processes play a key role in this endeavour. For the non-Markovian set-up, the SDS-likelihood is shown to depend on solutions to partial differential equations instead of ODEs as before. Preliminary numerical results for SDS likelihood-based parameter inference will be shown. Finally, I will discuss an extension to non-Markovian epidemics on configuration model random graphs.
This event has been postponed.
