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DTSTART:20241103T020000
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DESCRIPTION:Program: Applied Public Health Statistics Session: Small area 
 and small domain estimations in public health surveillance\, planning and
  evaluation Author: Yusuf Ransome See all authors and presenters → Abstra
 ct The Bayesian approach has been increasingly applied to analyze geospat
 ial health datasets at small-area levels (e.g.\, census tracts and zip co
 des). Both posterior sampling (e.g.\, Markov chain Monte Carlo\, MCMC) an
 d approximation (e.g.\, Integrated Nested Laplace Approximation\, INLA) a
 lgorithms have been developed to implement Bayesian models through softwa
 re and tools such as WinBUGS\, Stan\, NIMBLE\, and R-INLA. While these so
 ftware and tools make Bayesian statistical modeling much more accessible 
 than two decades ago\, its application in analyzing small-area level geos
 patial health data is still limited to researchers with expertise in prog
 ramming such as R\, Python\, and BUGS. An interactive platform that suppo
 rts Bayesian statistical modeling\, especially for spatial and spatiotemp
 oral datasets and in the Web setting\, is still lacking. Our study fills 
 this gap by developing a Web R-shiny tool for implementing Bayesian spati
 al and spatiotemporal models. Models fitted via both MCMC (i.e.\, NIMBLE)
  and INLA are supported in the tool\, with the former more flexible in ad
 dressing issues such as data censoring while the latter more computationa
 lly efficient. The tool leverages the cloud computing paradigm for Bayesi
 an model computation\, a feature especially benefitting MCMC-based models
  by making model convergence more time-efficient. The tool is used for an
 alyzing publicly accessible geospatial HIV datasets from AIDSVu.org and i
 s highly responsive to the Ending the HIV Epidemic in the U.S. (EHE) init
 iative. It will be distributed to and used by different stakeholders incl
 uding local health departments at jurisdictions prioritized by EHE for re
 ducing HIV infections and improving health equity.\n\nSpeaker:\nYusuf Ran
 some\n\nAdmission:\nRegistrationFees: APHA Event Registration is Required
 \n\nDetails URL:\nhttps://medicine.yale.edu/event/bayesian-spatial-statis
 tical-modeling-on-the-cloud/\n
DTEND;TZID=America/New_York:20251104T171500
DTSTAMP:20260514T231850Z
DTSTART;TZID=America/New_York:20251104T170000
GEO:38.903500;-77.022987
LOCATION:801 Allen Y Lew Pl NW\, Washington\, DC\, United States
SEQUENCE:0
STATUS:Confirmed
SUMMARY:4295.0 - Bayesian spatial statistical modeling on the cloud: a web
  tool for analyzing geospatial HIV datasets at small-area levels
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