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SUMMARY:Informing policy via dynamic models: Cholera in Haiti - Jesse Whee
 ler (University of Michigan)
DTSTART:20240807T113000Z
DTEND:20240807T114500Z
UID:TALK218239@talks.cam.ac.uk
DESCRIPTION:\n\nPublic health decisions must be made about when and how to
  implement interventions to control an infectious disease epidemic. These 
 decisions should be informed by data on the epidemic as well as current un
 derstanding about the transmission dynamics. Such decisions can be posed a
 s statistical questions about scientifically motivated dynamic models. Thu
 s\, we encounter the methodological task of building credible\, data-infor
 med decisions based on stochastic\, partially observed\, nonlinear dynamic
  models. This necessitates addressing the tradeoff between biological fide
 lity and model simplicity\, and the reality of misspecification for models
  at all levels of complexity. We assess current methodological approaches 
 to these issues via a case study of the 2010-2019 cholera epidemic in Hait
 i. We consider three dynamic models developed by expert teams to advise on
  vaccination policies. We evaluate previous methods used for fitting these
  models\, and we demonstrate modified data analysis strategies leading to 
 improved statistical fit. Specifically\, we present approaches for diagnos
 ing model misspecification and the consequent development of improved mode
 ls. Additionally\, we demonstrate the utility of recent advances in likeli
 hood maximization for high-dimensional nonlinear dynamic models\, enabling
  likelihood-based inference for spatiotemporal incidence data using this c
 lass of models. Our workflow is reproducible and extendable\, facilitating
  future investigations of this disease system.Co-Authors:&nbsp\;AnnaElaine
  Rosengart\, Zhuoxun Jiang\, Kevin Tan\, Noah Treutle\, Edward L. Ionides\
 n\n
LOCATION:Seminar Room 1\, Newton Institute
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