Forgive me, for I am about to Bayes. Lesson: Don't trust intuition, for even simple prior+likelihood scenarios defy it. Four examples below, each producing radically different posteriors. Can you guess what each does? Revealed in next tweet >>
Our new study modelling the urgent need for #naloxone in the US. Using a #Bayesian model we developed for BC we estimated the probability of its use during a witnessed overdose. https://t.co/KLlynTCUPU provides estimates for each state @DanCoombsUBC@TraciCGreen1
NEW—Naloxone: substantial increase in life-saving #opioid overdose intervention urgently needed in almost every U.S. state, finds study in @TheLancetPH.
https://t.co/98a7nNWFd0
Excellent discussion with @tedfieldglobal from @GlobalBC on our @ubcmedicine study – visits during the COVID19 peak pandemic @BCCHresearch
https://t.co/NpsR2B3Mqs
Long time frames to detect the impact of changing COVID-19 control measures. Tag line: it takes longer than 2 weeks to see the impact of a change in physical distancing (or other NPIs)
with @MostlyStatic@CarolineColijn
https://t.co/U1uGqD8Fp8
Pleased our work on the use of mixture density networks for emulation in epidemiology has recently been published in @PLOSCompBiol. Check it out: https://t.co/auwh3LSU27 @MikeAIrvine@DeirdreHoll @QuentinCAUDRON
Yesterday's talk for those who missed it, or don't have Netflix: Real-time modelling of the 2020 coronavirus epidemic. https://t.co/3EChtSad1n via @YouTube. Shoutout to @UBC cosmologist Douglas Scott for the invitation to speak in the physics colloquium.