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SUMMARY:BSU Seminar: &quot\;Personalized Decision-Making for Infectious Di
 sease Control: Causal Inference and Complex Dependence&quot\; - Ivana Male
 nica\, Harvard University
DTSTART:20241203T140000Z
DTEND:20241203T150000Z
UID:TALK220135@talks.cam.ac.uk
CONTACT:Alison Quenault
DESCRIPTION:Effective management of emerging and existing epidemics requir
 es strategic decisions on where\, when\, and to whom interventions should 
 be applied. However\, personalized decision-making in infectious disease a
 pplications introduces new and unique statistical challenges. For instance
 \, the individuals at risk of infection are unknown\, the true outcome of 
 interest (positive infection status) is often a latent variable\, and the 
 presence of complex dependence reduces data to a single observation. In th
 is work\, we investigate an adaptive sequential design under latent outcom
 e structures and unspecified dependence through space and time. The statis
 tical problem is addressed within a nonparametric model that respects the 
 unknown dependence structure. I will begin by formalizing a treatment allo
 cation strategy that utilizes up-to-date data to inform who is at risk of 
 infection in real-time\, with favorable theoretical properties. The optima
 l allocation strategy\, or optimal policy\, maximizes the mean latent outc
 ome under a resource constraint. The proposed estimator learns the optimal
  policy over time and exploits the double-robust structure of the efficien
 t influence function of the target parameters of interest. In the second p
 art of the talk\, I will present the study of data-adaptive inference on t
 he mean under the optimal policy\, where the target parameter adapts over 
 time in response to the observed data (state of the epidemic). Lastly\, I 
 present a novel paradigm in nonparametric efficient estimation particularl
 y suited for target parameters with complex dependence. 
LOCATION:MRC Biostatistics Unit\, East Forvie Building\, Forvie Site Robin
 son Way Cambridge CB2 0SR.
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